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Python code to create folders and Word documents for research papers in biomedical sciences — all in one go with only two inputsContinue reading on Towards Data Science »

How I used AI and Streamlit to create a festive and fun gift recommendation appContinue reading on Towards Data Science »

A discussion of the latest research suggesting that LLMs do work like the human brain—with some substantial differencesContinue reading on Towards Data Science »

30 Days, 30 Maps: My November Adventure in Digital CartographyContinue reading on Towards Data Science »

My top tips to smash your next data science behavioural interviewContinue reading on Towards Data Science »

Let’s see how many stars we’ll collect.Continue reading on Towards Data Science »

How to predict DAU using Duolingo’s growth model and control the prediction1. IntroductionDoubtlessly, DAU, WAU, and MAU — daily, weekly, and monthly active users — are critical business metrics. An article “How Duolingo reignited user growth” by Jorge Mazal, former CPO of Duolingo, is #1 in the Growth section of Lenny’s Newsletter blog. In this article, Jorge paid special attention to the methodology Duolingo used to model the DAU metric (see another article “Meaningful metrics: how data sharpened the focus of product teams” by Erin Gustafson). This methodology has multiple strengths, but I’d like to focus on how one can use this approach for DAU forecasting.The new year is coming soon, so…

Implementing Speculative and Contrastive DecodingLarge Language models are comprised of billions of parameters (weights). For each word it generates, the model has to perform computationally expensive calculations across all of these parameters.Large Language models accept a sentence, or sequence of tokens, and generate a probability distribution of the next most likely token.Thus, typically decoding n tokens (or generating n words from the model) requires running the model n number of times. At each iteration, the new token is appended to the input sentence and passed to the model again. This can be costly.Additionally, decoding strategy can influence the quality of the generated words. Ge…

DATA SCIENCE CONSULTINGInsider consulting guide to conducting a successful 2-day executive workshopImage by author using Canva“Our industry does not respect tradition — it only respects innovation.” — Satya Nadella, CEO Microsoft, Letter to employees in 2014While not all industries are as competitive and cutthroat as the software and cloud industries, innovating and applying the latest technological developments is a fundamental theme for executives. Seasoned business leaders understand that staying up-to-date with the relevant technologies is necessary for success.As a data science consultant, some of the questions clients often ask me are: “How do we effectively integrate the right AI and…

Understanding AI applications in bio for machine learning engineersPhoto by Ousa Chea on UnsplashAnyone who has tried teaching a dog new tricks knows the basics of reinforcement learning. We can modify the dog’s behavior by repeatedly offering rewards for obedience and punishments for misbehavior. In reinforcement learning (RL), the dog would be an agent, exploring its environment and receiving rewards or penalties based on the available actions. This very simple concept has been formalized mathematically and extended to advance the fields of self-driving and self-driving/autonomous labs.As a New Yorker, who finds herself riddled with anxiety driving, the benefits of having a stoic robot ch…

And learn about LLM architecture techniques, parsed output, test design and performance measurement of your systemContinue reading on Towards Data Science »

How to kickstart your EDA using simple one linersContinue reading on Towards Data Science »

REGRESSION ALGORITHMRoping in key features with coordinate descentLeast Squares Regression, Explained: A Visual Guide with Code Examples for BeginnersLinear regression comes in different types: Least Squares methods form the foundation, from the classic Ordinary Least Squares (OLS) to Ridge regression with its regularization to prevent overfitting. Then there’s Lasso regression, which takes a unique approach by automatically selecting important factors and ignoring others. Elastic Net combines the best of both worlds, mixing Lasso’s feature selection with Ridge’s ability to handle related features.It’s frustrating to see many articles treat these methods as if they’re basically the same thi…

The Advent, Evolution, and Current state of “Data Translators”IntroductionWith Data being constantly glorified as the most valuable asset organizations can own, leaders and decision-makers are always looking for effective ways to put their data insights to use. Every time customers interact with digital products, millions of data points are generated and the opportunity loss of not harnessing these data points to make better products, optimize revenue generation, and improve customer footprint is simply too high to ignore. The role of “Data Translators” began to emerge in analytics and data science job boards in the 2010s to help bridge the knowledge gap between business and Data teams and …

A beginner-friendly guide with example (Python) codeThis is the third article in a larger series on multimodal AI. In the previous posts, we discussed multimodal LLMs and embedding models, respectively. In this article, we will combine these ideas to enable the development of multimodal RAG systems. I’ll start by reviewing key concepts and then share example code for implementing such a system.Image from Canva.Language models like GPT, LLaMA, and Claude learn a tremendous amount of world knowledge via their pre-training. This makes them powerful tools for solving custom problems and answering complex questions.However, there is knowledge that even the most advanced language models are ignor…

Mathematics and statistics, once the primary guides of machine learning research, now struggle to provide immediate insight into the latest breakthroughs.

This shift has prompted speculation about mathematics’ diminished role in machine learning research moving forward.

It is also the way that symmetries are usually leveraged when performing computations (for example, in machine learning).

One can reasonably argue that diagrammatic descriptions of well-known constructions, like products, are not useful for the machine learning researcher.

However, as we’ve demonstrated, while mathematics may not maintain the same role in machine learning research that it has held in the past, the success of…

Let's jump back to the 1970s, when Roger Schank introduced his "restaurant script" as a kind of dialogue system [1].

The minimum requirement we could have for a dialogue system is that it can stay on the task we gave them.

Concluding marksI have reviewed the making of current LLM dialogue systems, how and why it is insufficient.

The following are two research questions that I’m mostly excited about:(1) Better monitoring and control of dialogue systems with steering techniques.

CitationFor attribution of this in academic contexts or books, please cite this work as:Kenneth Li, "From prediction to purpose: a tutorial on LLM dialogue system", The Gradient, 2024.

This leads to our second conclusion: We need plausible positive visions of a society with capable AI, grounded in wellbeing.

The rest of this post describes in more detail (1) what we mean by AI that benefits our wellbeing, (2) the need for positive visions for AI grounded in wellbeing, and (3) concrete leverage points to aid in the development and deployment of AI in service of such positive visions.

In diving into the philosophy of flourishing, wellbeing economics, or psychological theories of human wellbeing, one encounters many interesting, compelling, but seemingly incompatible ideas.

The case so far is that we need positive visions for society with capable AI, grounded in individual a…

Looked at from another angle, there is much more noise than signal in financial data.

Another financial market application of LLMs might be synthetic data creation [4,8].

Then precious real market data could be employed to fine-tune the predictions and determine precisely the optimal speed to trade.

Financial market practitioners are often interested in extreme events, the times when trading strategies are more likely to experience significant gains or losses.

CitationFor attribution in academic contexts or books, please cite this work asRichard Dewey and Ciamac Moallemi, "Financial Market Applications of LLMs," The Gradient, 2024

All of these terms (“AI”, “gender”, and “bias”) can be somewhat overused and ambiguous.

A Short History of Studying Gender Bias in AIHere, I cover a very small sample of papers I’ve found influential studying gender bias in AI.

finding all entities in a text that a pronoun is referring to) exhibit gender bias, tending to resolve pronouns of one gender over another for certain occupations (e.g.

This article mainly focused on gender bias — and particularly, on binary gender.

AcknowledgementsThis post was originally posted on Art Fish IntelligenceCitationFor attribution in academic contexts or books, please cite this work asYennie Jun, "Gender Bias in AI," The Gradient, 2024@article{Jun2024bia…

As a general sequence model backbone, Mamba achieves state-of-the-art performance across several modalities such as language, audio, and genomics.

Here we’ll discuss:The advantages (and disadvantages) of Mamba (🐍) vs Transformers (🤖),Analogies and intuitions for thinking about Mamba, andWhat Mamba means for Interpretability, AI Safety and Applications.

The Mamba BlockLike a Transformer made up of stacked transformer blocks, Mamba is made up of stacked Mamba blocks as above.

The Mamba authors write, “the efficiency vs. effectiveness tradeoff of sequence models is characterised by how well they compress their state”.

Thanks to Gonçalo for reading an early draft, Jaden for the nnsight library …

We've just seen 3 prominent families of LLM usage in self-driving cars: Perception, Planning, and Generation.

The first wave of papers mentioning LLMs in Self-Driving Cars is from mid-2023, so let's give it some time.

Next StepsIf you want to get started on LLMs for self-driving cars, there are several things you can do:⚠️ Before this, the most important : If you want to keep learning about self-driving cars.

Author BioJérémy Cohen is a self-driving car engineer and founder of Think Autonomous, a platform to help engineers learn about cutting-edge technologies such as self-driving cars and advanced Computer Vision.

CitationFor attribution in academic contexts or books, please cite this work…

Beyond the requirements of semantic similarity, there are no constraints on what embedding must be assigned for a given text input.

What if someone hacks into the database and gains access to all your text embedding vectors – would this be bad?

From text to embeddings...back to textThe problem of recovering text from embeddings is exactly the scenario we tackle in our paper Text Embeddings Reveal As Much as Text (EMNLP 2023).

Scaling and future workThe fact that text embeddings can be perfectly inverted raises many follow-up questions.

CitationFor attribution in academic contexts or books, please cite this work asJack Morris, "Do text embeddings perfectly encode text?

If your model latches on to these during training, it will appear to work well, but may not work on new data.

This happens when the model training pipeline has access to information it shouldn’t have access to, particularly information that confers an advantage to the model.

This means that knowledge of the test data is implicitly entering the model training pipeline, even if it is not explicitly used to train the model.

Well, this is a common thing to do, but if you’re developing a model iteratively and using the same test set to evaluate the model after each iteration, then you’re basically using that test set to guide the development of the model.

CitationFor attribution in academic cont…

Created Using MidjourneyAnother week another agentic framework.

The market for AI agents seems as hot as it is fragmented but the level of innovation is remarkable.

One of the areas that seems to be gaining a tremendous level of attention is the multi-agent systems.

Microsoft is one of the companies really active in the agents space with frameworks such as AutoGen and TaskWeaver.

Recently, Microsoft open sourced a new framework focus on multi-agent systems.

Created Using DALL-EThe release of GPT-01 marked many important milestones in the generative AI space.

The model has sparked a tremendous new phase of innovation in reasoning models which has materialized in the release of models such as DeepSeek’s R1 or Alibaba’s QwQ.

The magical reasoning capabilities of these models is powered by an increasing transition from pretraining to post-training computation time.

In this essay, we will explore the fundamentals behind that transition highlighting the limitations associated with scaling pretraining and the emerging techniques in post-training.

Understanding Pretraining in Foundation Models

Created Using MidjourneyIn this issue:Introducing cross model distillation.

UC Berkeley’s paper about cross modal distillation for supervision transfer.

However, the question of having distillation models in which the teacher transfers knowledge from one modality to a student primary built for another modality is an interesting one.

Cross-modal distillation enables the transfer of knowledge between models operating on different data modalities.

This approach extends the concept of knowledge distillation, originally proposed for model compression, to scenarios where the teacher and student models process fundamentally different types of input data.

Since its initial release, GPT-o1 has been regarded as the most sophisticated model for long-term reasoning tasks.

This transparency offers valuable insights into the model's reasoning mechanisms and underscores Alibaba's commitment to promoting a deeper understanding of how LRMs function.

a preview of its reasoning model —> Read more.

Model Context ProtocolAnthropic open sourced the Model Context Protocol, a new standard for integrating AI assistants with data —> Read more.

SmolVLMHuggingFace open sourced SmolVLM, a 2B parameter vision language model —> Read more.

Join AI leaders from Meta, Mistral, Salesforce, DoorDash, Harvey AI, Nubank, Hugging Face, and more at SmallCon on Dec 11th for deep-dive tech talks, panel discussions, and live demos on the latest tech and trends in GenAI.

You'll learn what it takes to build the GenAI stack of the future and put your SLMs into production!

Sneak peek at the agenda:The Future is Small: Why Apple is Betting Big on Small ModelsUnlocking Enterprise Transformation with GenAITrends in GenAI: What's New in SLM Training and ServingAI Agents that Work: Lessons Learned from SalesforceApplied AI and Real World Use CasesData 2.0: Synthetic Data Generation Best PracticesSave your spot for SmallCon to learn how to build …

The audio generation in NotebookLM touches on aspects such as humor, regular questions, interruptions etc which are incredibly hard to master.

Well, NotebookLM’s audio generation capabilities were the result of combining several techniques developed by Google DeepMind over the last few years.

Specifically NotebookLM audio magic was powered by innovations in two key models: SoundStorm and AudioLM, which underpin Google DeepMind’s approach to audio generation.

Audio generation represents a burgeoning area of research within the domain of Artificial Intelligence (AI).

Google DeepMind has made notable strides in this domain, pioneering novel techniques that are significantly impacting audio gen…

Created Using MidjourneyLarge foundation models are like black boxes!

We regularly hear this statement associated with the limited interpretability in the current generation of large generative AI models across different modalities.

The advent of large foundation models has revolutionized the field of artificial intelligence, bringing unprecedented capabilities in natural language processing, image generation, and multi-modal tasks.

However, these models present significant challenges in terms of interpretability, far surpassing those encountered in traditional machine learning approaches.

When thinking about the interpretability challenges of AI models, an important point to understand is …

Created Using MidjourneyIn this issue:An introduction to multi-teacher distillation.

An analysis of the MT-BERT multi-teacher distillation method.

💡 ML Concept of the Day: Understanding Multi-Teacher DistillationDistillation is typically explained using a teacher-student architecture, where we often conceptualize it as involving a single teacher model.

However, there are many scenarios where this process can be enhanced by using multiple teachers.

An alternative to the traditional approach is to use multiple teachers in a method known as multi-teacher distillation.

Like many other scientific fields, researchers are wondering what impact AI could have on quantum computing.

One of the biggest challenges in quantum computing lies in the inherent noise that plagues quantum processors.

To unlock the full potential of quantum computing, effective error correction is paramount.

Enter AlphaQubit—a cutting-edge AI system developed through a collaboration between Google DeepMind and Google Quantum AI.

🔎 ML ResearchAlphaQubitResearchers from: Google DeepMind and Google Quantum AI published a paper detailing a new AI system that accurately identifies errors inside quantum computers.

Created Using MidjourneyControlling the behavior of foundation models has been at the forefront of research in the last few years in order to accelerate mainstream adoption.

From a philosophical standpoint, the meta question is whether we can ultimately control intelligent entities that are way smarter than ourselves.

Given that we are nowhere near that challenge, a more practical question is whether models show emerging behaviors that subvert human evaluations.

In a new paper, Anthropic proposes a framework for assessing the risk of AI models sabotaging human efforts to control and evaluate them.

This framework, called “Sabotage Evaluations”, aims to provide a way to measure and mitigate t…

Most large foundation models have been virtually trained on the entirety of the internet, using datasets like Fine-Web that encapsulate much of the publicly available data.

As a result, the question of AI hitting a 'data wall' has become increasingly relevant.

After all, the famous scaling laws are fundamentally dependent on the availability of vast amounts of data.

This essay explores the thesis of the "end of data" for AI models, examining both sides of the argument and delving into potential solutions such as extracting higher quality data and generating synthetic datasets.

Let’s start with some points that validate the data wall argument:The Data Scarcity Argument

Created Using MidjourneyIn this issue:Understanding adversarial distillation.

Alibaba’s paper about Introspective Adversarial Distillation (IAD).

The first stop is about a method known as adversarial distillation.

As it names indicates, adversarial distillation draws inspiration from generative adversarial networks(GANs) using a generator-discriminator architecture.

In that setting, the generator creates synthetic samples close to the true data distribution while the discriminator learns to differentiate between the synthetic and original data samples.

Much of AI's impressive performance in math benchmarks relies on scenarios where the problem is perfectly articulated within a prompt.

Unlike traditional math benchmarks such as GSM-8K and MATH, where AI models now score over 90%, Frontier Math presents a significantly more challenging test.

The benchmark comprises hundreds of intricate math problems spanning diverse fields of modern mathematics, from computational number theory to abstract algebraic geometry.

Frontier Math provides a critical benchmark for measuring progress in AI reasoning as these systems continue to evolve.

Frontier MathFrontierMath is, arguably, the toughest math benchmark ever created —> Read more.

Convirza, a leader in call analytics, recently faced this exact challenge and found an answer with Predibase’s multi-LoRA serving infrastructure.

Join us on November 21st at 10:00 am PT for an exclusive look into how Convirza transitioned from Longformer models to fine-tuned SLMs, improving speed and accuracy while cutting costs.

Optimized Cost Structure : Instead of dedicated GPUs for each of the 60 indicators—each costing $500-$1,500 per month—Convirza now runs multiple indicators on a single scalable GPU deployment.

Faster Iterations with SLMs: They swapped out Longformer models for fine-tuned SLMs, reducing the training cycles from 9-24+ hours to less than 3 hours on average per adapter…

The landscape of vision model pre-training has undergone significant evolution, especially with the rise of Large Language Models (LLMs)…Continue reading on SyncedReview »

A foundation model refers to a pre-trained model developed on extensive datasets, designed to be versatile and adaptable for a range of…Continue reading on SyncedReview »

Continue reading on SyncedReview »

While large language models (LLMs) dominate the AI landscape, Small-scale Large Language Models (SLMs) are gaining traction as…Continue reading on SyncedReview »

Consistency models (CMs) are a cutting-edge class of diffusion-based generative models designed for rapid and efficient sampling. However…Continue reading on SyncedReview »

The field of text-to-image synthesis has advanced rapidly, with state-of-the-art models now generating highly realistic and diverse images…Continue reading on SyncedReview »

In cognitive science, human thought processes are commonly divided into two systems: the fast, intuitive System 1 and the slower…Continue reading on SyncedReview »

Continue reading on SyncedReview »

Generative AI, including Language Models (LMs), holds the promise to reshape key sectors like education, healthcare, and law, which rely…Continue reading on SyncedReview »

Recent large language models (LLMs) have shown impressive performance across a diverse array of tasks.Continue reading on SyncedReview »

For artificial intelligence to thrive in a complex, constantly evolving world, it must overcome significant challenges: limited data…Continue reading on SyncedReview »

The rise of large language models (LLMs) has sparked questions about their computational abilities compared to traditional models. While…Continue reading on SyncedReview »

Multimodal Large Language Models (MLLMs) have rapidly become a focal point in AI research. Closed-source models like GPT-4o, GPT-4V…Continue reading on SyncedReview »

Large language models (LLMs) like GPTs, developed from extensive datasets, have shown remarkable abilities in understanding language…Continue reading on SyncedReview »

Cellular automata (CA) have become essential for exploring complex phenomena like emergence and self-organization across fields such as…Continue reading on SyncedReview »

В этой статье мы разберемся, чему научилась новая GPT o1, и как это повлияет на дальнейшую эволюцию ИИ.

Компания утверждает, что для них сброс счётчика линейки моделей к единичке знаменует собой переход к новой парадигме, и что эта нейросеть и вовсе демонстрирует новый уровень возможностей ИИ.

На форумах и в Твиттере была куча обсуждений, предвосхищений и хайпа, на фоне которых планка ожиданий некоторых людей взлетела до небес.

Издание Bloomberg рассказало, что в ходе внутренней демонстрации OpenAI показали концепцию из пяти уровней, помогающую отслеживать прогресс в создании ИИ.

Однако на уровне GPT-5 прирост в навыках может быть совсем другим (как в лучшую, так и в худшую сторону).

И в том, и в другом случаях объяснение действия не связано с реальным мотивом его сделать, и там, и там рождается поддельное (но правдоподобно звучащее) объяснение причин.

Просто сейчас это не воспринимается всерьёз, ведь LLM не распространены и не становятся ядром бизнес-процессов, включающих принятие решений.

Один и тот же текст запроса+ответа подаётся в модель, и производится оценка вероятности получить именно такой ответ при фиксированном запросе.

Это и желание продолжать существовать/жить, и нежелание умирать, и рассуждения об эмоциях и контроле.

Потому что абстракции, потому что обобщение, потому что это ровно то, за что мы ценим модели.

В ней авторы выделили 4 этапа зрелости, грубо можно разделить компании по частоте запусков экспериментов:на этапе Crawl компания проводит эксперимент раз в месяц (примерно 10 экспериментов в год);на этапе Walk – раз в неделю (примерно 50 экспериментов в год);на этапе Run – ежедневно (примерно 250 экспериментов в год);на этапе Fly – более 1000 экспериментов в год.

Более четкое разделение основывается на некоторых важных свойствах компаний, таких как: наличие команды платформы экспериментов, возможности автоматического расчета метрик, распространенности экспериментов в компании, самодостаточность продуктовых команд в проведении экспериментов, влияние экспериментов на компанию и другое.

Точка …

В ней авторы выделили 4 этапа зрелости, грубо можно разделить компании по частоте запусков экспериментов:на этапе Crawl компания проводит эксперимент раз в месяц (примерно 10 экспериментов в год);на этапе Walk – раз в неделю (примерно 50 экспериментов в год);на этапе Run – ежедневно (примерно 250 экспериментов в год);на этапе Fly – более 1000 экспериментов в год.

Более четкое разделение основывается на некоторых важных свойствах компаний, таких как: наличие команды платформы экспериментов, возможности автоматического расчета метрик, распространенности экспериментов в компании, самодостаточность продуктовых команд в проведении экспериментов, влияние экспериментов на компанию и другое.

Точка …

Mlflow Experiments and Mlflow RunsMLflow Experiments и MLflow Runs - это основные абстракции для структурирования проекта.

mlflow run mlproject --entry-point hyperparameters-tuning --env-manager conda --experiment-name Cancer_Classification --run-name Hyperparameters_Search -P n-trials=10Посмотрим на результаты в MLflow UI.

artifact_path: model flavors: python_function: data: model.xgb env: conda: conda.yaml virtualenv: python_env.yaml loader_module: mlflow.xgboost python_version: 3.11.4 xgboost: code: null data: model.xgb model_class: xgboost.sklearn.XGBClassifier model_format: xgb xgb_version: 2.0.3 mlflow_version: 2.14.2 model_size_bytes: 35040 model_uuid: 516954aae7c94e91adeed9df76cb405…

Как это определить и как предсказывать?

- Да... упс.. правда, они же уже определяют целевой признакВовремя не выкрутился, пришел фидбек, что я не понимаю разницу между обычными признаками (а.к.а.

Не, не делал, только DDP?

NVIDIA ищет единорогов, крутых и в рисече, и в инженерии.

Вакансия в Лондоне была, и в итоге они взяли моего бывшего коллегу: он уже в Лондоне и с чуть большим опытом в рекомендашках, явно лучше подходит.

ChatGPT 4 был значительно улучшен по сравнению с ChatGPT 3.5, что делает его более мощным инструментом.

Вам тоже надо учиться — учиться выстраивать взаимоотношение с ChatGPT, учиться общаться с ним.

Помните, вы всегда можете уточнить любую строчку в ответе ChatGPT и, в большинстве случаев, получить исчерпывающий ответ, который, вероятнее всего, обогатит вас новыми знаниями.

Вот несколько идей:Откройте с ChatGPT новый чат и в нём отправьте запрос в другой форме, желательно с новыми деталями.

И поэтому, когда с ChatGPT не удаётся что-то сделать с первого раза за 2–5 минут, возникает возмущение: “Ну, как так?!”.

In machine learning projects, achieving optimal model performance requires paying attention to various steps in the training process. But before focusing on the technical aspects of model training, it is important to define the problem, understand the context, and analyze the dataset in detail. Once you have a solid grasp of the problem and data, […]

The post Tips for Effectively Training Your Machine Learning Models appeared first on MachineLearningMastery.com.

Reinforcement Learning (RL) is a type of machine learning. It trains an agent to make decisions by interacting with an environment. This article covers the basic concepts of RL. These include states, actions, rewards, policies, and the Markov Decision Process (MDP). By the end, you will understand how RL works. You will also learn how […]

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Deep learning is a subset of machine learning that has become a cornerstone in many technological breakthroughs. At the core of deep learning, it’s a model inspired by the human brain, which we call a neural network. Contrary to the traditional machine learning model, deep learning can automatically find feature representations from data. That’s why […]

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Feature engineering is an important step in the machine learning pipeline. It is the process of transforming data in its native format into meaningful features to help the machine learning model learn better from the data. If done right, feature engineering can significantly enhance the performance of machine learning algorithms. Beyond the basics of understanding […]

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Using machine learning to solve real-world problems is exciting. But most eager beginners jump straight to model building—overlooking the fundamentals—resulting in models that aren’t very helpful. From understanding the data to choosing the best machine learning model for the problem, there are some common mistakes that beginners often tend to make. But before we go […]

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Photography has been around for more than a century. There are many old photos around, and probably your family has some, too. Limited by the camera and film of the time, you may have photos of low resolution, blurry, or with folds or scratches. Restoring these old photos and making them like new ones taken […]

The post Stable Diffusion Project: Reviving Old Photos appeared first on MachineLearningMastery.com.

Today’s digital landscape has never been so diverse. Every individual and company selects their preferred tools and operating systems, creating a diverse technological system. However, this diversity often leads to compatibility issues, making it hard to ensure application performance across different environments. This is where Docker plays a key role as an indispensable tool for […]

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Stable Diffusion has taken the AI art world by storm, empowering users to generate stunning and imaginative visuals with just a few text prompts. This opens exciting possibilities for creatives, including crafting impactful commercial posters. In this post, we’ll delve into using Stable Diffusion to design a compelling poster for a product. After finishing this […]

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Introduction Here’s a something that new machine learning practitioners figure out almost immediately: not all datasets are created equal. It may now seem obvious to you, but had you considered this before undertaking machine learning projects on a real world dataset? As an example of a single class vastly outnumbering the rest, take for instance […]

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Introduction Choosing the right machine learning model for your data is of major importance in any data science project. The model you select will have a significant impact on the insights you derive from your data, and ultimately determine the usefulness of a project. In this article, we aim to provide practical tips to help […]

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Many people write in their jobs. Not everyone is a novel writer; some write technical documentation, business plans, news articles, and even blog posts. In those writings, illustrations are not essential but often good to have. They are decorations, interpretations, or visual explanations of the text. However, you probably do not want to spend too […]

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If you are a machine learning student, researcher, or practitioner, it is crucial for your career growth to have a deep understanding of how each algorithm works and the various techniques to enhance model performance. Nowadays, many individuals tend to focus solely on the code, data, and pre-trained models, often without fully comprehending the machine […]

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Stable Diffusion is a powerful tool that helps you generate pictures. It is fun to play with the generative AI tool. But it would be useful if the tool could help you in a real job. In this post, you will see how you can leverage the power of Stable Diffusion to work on something […]

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As a data professional, you should also know how to build predictive models with machine learning to solve business problems. And if you’re interested in machine learning, you’re probably also looking for the best resources to get going. Well, you can always choose a self-paced online course that best aligns with your learning preferences. But […]

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If you’re looking to make a career in data science, you probably know that machine learning is one of the most in-demand skills. Whether you are a beginner looking to break into the field or an experienced professional aiming to level up your expertise, selecting the right machine learning course is super important. So how […]

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I expected to fill that void with more blog writing, but that’s not what happened.

The puzzles are great though, and if that’s good enough for you, I had fun with that.

Undertale YellowUndertale Yellow is a fantastic fan game, that’s been in development for 7 years and comes out feeling like a canon entry made by Toby Fox.

markdown 15837 2024 - 01 - 11 - ai - timelines - 2024. markdown 1939 2024 - 01 - 21 - mh - 2024. markdown 5076 2024 - 03 - 23 - crew - battle .

markdown 826 2024 - 04 - 30 - puzzlehunting - 201. markdown 8641 2024 - 07 - 08 - tragedies - of - reality .

There’s often a conflation between the research field of AI safety and the community of AI safety.

Me thinking AI safety is important is not an endorsement for or against anything else in the broader meme space it came from.

Historically, AI safety work did not appeal to me because of how theoretical it was.

I’m aware of the arguments that most AI safety work so far has either been useless or not that different from broader AI work.

Those who care about safety a lot call this safetywashing, the stapling of “safety” to work that does not advance safety.

I would extend it to reality is complicated, relative to code, and in robotics you’re often pushing a messy reality into an abstraction nice enough for code to act on it.

Robotics research relies on building new bridges between reality and software, but that happens outside of robotics too.

Any software that interfaces with reality will have imperfect knowledge of that reality.

However, that means all the messiness of reality is coming for a field that historically does a bad job at considering reality.

I consider the world of bits to be as much a part of reality as the world of atoms.

Most people will have more fun if they solve puzzles than if they don’t, but you don’t have to solve puzzles quickly to have fun.

I’m still going to explain the solving strategies I’ve learned, but puzzle solving is really an activity where you learn by doing.

Puzzle solving often involves relating two parts of the puzzle together.

Search everythingHonestly, a lot of puzzle solving is about taking random parts of the puzzle and throwing them into a search engine.

Bringing This TogetherTo showcase these strategies together, here is a puzzle I remember speedrunning especially quickly: The Three Little Pigs from Hunt 20 2.1 Puzzle Hunt.

This means we can reduce all the crew battle outcomes down to a single \(n \times n\) matrix, which I’ll call the crew battle matrix.

So I Wrote Some Python Code to Compute \(f\) for Arbitrary Crew BattlesLet’s consider the RPS crew battle again.

Here’s the matchup matrix:and here’s what my code outputs for the crew battle matrix, assuming optimal play.

But also, this suggests that crew battle strategy really isn’t that important in the first place!

If your crew loses, you don’t get to blame bad crew battle strategy.

From these excerpts, sociotechnical AI seems to have something to do with being people-centred, having social-purpose, understanding and reducing risks, building trust, widening opportunities, centring rights and values, being accountable for claims, and taking a system-wide view on AI.

To do this we will need more than technical AI research; and that is the role of sociotechnical AI research.

This expresses a hope for significant public benefit, but again, the burden of proof lies with AI designers, and so requires new forms of sociotechnical AI research.

Further developing all these varied forms for social and community involvement is another focus area for sociotechnical AI.

Sociotechnic…

At Youtube, the relationships between system parameters and metrics often seem simple — straight-line models sometimes fit our data well.

To find optimal values of two parameters experimentally, the obvious strategy would be to experiment with and update them in separate, sequential stages.

We address uncertainty in estimates $\widehat \beta$ of model coefficients with subsampling, our usual strategy for variance estimation in online experiments[12].

If our experiments were smaller then we might need to model our metrics with Poisson, Binomial or other GLMs instead.

We sometimes use $L^1$ regularization when we fit models to our live experiment data to select the most important non-linear c…

Superficially this looks fine, but of course the problem is hidden in how do we arrive with these equivalences?

Let's take a closer look: Kurzweil claims we should be seeing insect brain capability in $1000 computer in 2001.

Alright what would we expect from an insect brain?

It's 2024 at the time of writing this, and the best military technology that could benefit from an insect brain like capabilities is still on remote control.

That is of course if we actually get an insect brain this year.

Both of these assumptions seem reasonable and general and we can quickly see where Gaussian distribution should start popping out.

Since Gaussian distribution is fully defined by just two numbers - mean and variance, we can, by collecting enough data rather precisely estimate these values.

Below I simulate 400x200=80000 independent pixels, each taking a random value between zero and one [code available here].

These distributions we often refer to as "fat tails" [for putting much more "weight" in the tail of the distribution, as in outside of the "central" part] are really weird and problematic.

Let's get back to why we see Gaussian distribution anywhere at all?

Researchers have reported the UDA techniques are not working well under large co-variate shift problems where e.g.

supervised source data consists of handwritten digits data in monotone color and unsupervised target data colored digits data from the street view.

We perform two stages domain invariant representation learning to bridge the gap between source and target with semantic intermediate data (unsupervised).

This induction for the gradient descent search greatly eases learning convergence in terms of classification performance for target data even when large co-variate shift.

Our experiment will be a basis for challenging UDA problems with large co-variate shift.

In this paper, we present PanoDreamer, a novel method for producing a coherent 360$^\circ$ 3D scene from a single input image.

Unlike existing methods that generate the scene sequentially, we frame the problem as single-image panorama and depth estimation.

Once the coherent panoramic image and its corresponding depth are obtained, the scene can be reconstructed by inpainting the small occluded regions and projecting them into 3D space.

Our key contribution is formulating single-image panorama and depth estimation as two optimization tasks and introducing alternating minimization strategies to effectively solve their objectives.

We demonstrate that our approach outperforms existing technique…

Dependable visual drone detection is crucial for the secure integration of drones into the airspace.

However, drone detection accuracy is significantly affected by domain shifts due to environmental changes, varied points of view, and background shifts.

To address these challenges, we present the DrIFT dataset, specifically developed for visual drone detection under domain shifts.

DrIFT includes fourteen distinct domains, each characterized by shifts in point of view, synthetic-to-real data, season, and adverse weather.

We use the MCDO-map in our uncertainty-aware unsupervised domain adaptation method, demonstrating superior performance to SOTA unsupervised domain adaptation techniques.

Accurate and automatic segmentation of brain tumors in multi-parametric magnetic resonance imaging (mpMRI) is essential for quantitative measurements, which play an increasingly important role in clinical diagnosis and prognosis.

The International Brain Tumor Segmentation (BraTS) Challenge 2024 offers a unique benchmarking opportunity, including various types of brain tumors in both adult and pediatric populations, such as pediatric brain tumors (PED), meningiomas (MEN-RT) and brain metastases (MET), among others.

We propose a deep learning-based ensemble approach that integrates state-of-the-art segmentation models.

Given the heterogeneous nature of the tumors present in the BraTS datasets…

This may result in some items receiving lower exposure than they "should"; to counter this, several algorithmic approaches have been developed to ensure item fairness.

These approaches necessarily degrade recommendations for some users to improve outcomes for items, leading to user fairness concerns.

In turn, a recent line of work has focused on developing algorithms for multi-sided fairness, to jointly optimize user fairness, item fairness, and overall recommendation quality.

Theoretically, we develop a model of recommendations with user and item fairness objectives and characterize the solutions of fairness-constrained optimization.

We identify two phenomena: (a) when user preferences are…

Virtual models of human gait, or digital twins, offer a promising solution for studying mobility without the need for labor-intensive data collection.

To address these, we developed and validated a framework to create a skeletal humanoid agent capable of adapting to varying walking speeds while maintaining biomechanically realistic motions.

The framework combines a synthetic data generator, which produces biomechanically plausible gait kinematics from open-source biomechanics data, and a training system that uses adversarial imitation learning to train the agent's walking policy.

We conducted comprehensive analyses comparing the agent's kinematics, synthetic data, and the original biomechan…

The digitization of vocal music scores presents unique challenges that go beyond traditional Optical Music Recognition (OMR) and Optical Character Recognition (OCR), as it necessitates preserving the critical alignment between music notation and lyrics.

This paper introduces and formalizes, for the first time, the Aligned Music Notation and Lyrics Transcription (AMNLT) challenge, which addresses the complete transcription of vocal scores by jointly considering music symbols, lyrics, and their synchronization.

We analyze different approaches to address this challenge, ranging from traditional divide-and-conquer methods that handle music and lyrics separately, to novel end-to-end solutions in…

Google Research Scientist David Warde and Google DeepMind Research Scientist Ian Goodfellow will present on Generative Adversarial Nets.

Teams across Google DeepMind will present more than 150 new papers on topics ranging from AI agents and generative media to innovative learning approaches.

Building adaptive, smart, and safe AI Agents LLM-based AI agents are showing promise in carrying out digital tasks via natural language commands.

Yet their success depends on precise interaction with complex user interfaces, which requires extensive training data.

AI agents trained using this dataset showed significant performance gains which we hope helps advance research into more general AI agents.

Because a perfect weather forecast is not possible, scientists and weather agencies use probabilistic ensemble forecasts, where the model predicts a range of likely weather scenarios.

The evolution of AI weather models GenCast marks a critical advance in AI-based weather prediction that builds on our previous weather model, which was deterministic, and provided a single, best estimate of future weather.

Better forecasts of extreme weather, such as heat waves or strong winds, enable timely and cost-effective preventative actions.

GenCast offers greater value than ENS when making decisions about preparations for extreme weather, across a wide range of decision-making scenarios.

Advanced forec…

Today we introduce Genie 2, a foundation world model capable of generating an endless variety of action-controllable, playable 3D environments for training and evaluating embodied agents.

Based on a single prompt image, it can be played by a human or AI agent using keyboard and mouse inputs.

Games play a key role in the world of artificial intelligence (AI) research.

However, training more general embodied agents has been traditionally bottlenecked by the availability of sufficiently rich and diverse training environments.

As we show, Genie 2 could enable future agents to be trained and evaluated in a limitless curriculum of novel worlds.

Quantum computers have the potential to revolutionize drug discovery, material design and fundamental physics — that is, if we can get them to work reliably.

Certain problems, which would take a conventional computer billions of years to solve, would take a quantum computer just hours.

If we want to make quantum computers more reliable, especially at scale, we need to accurately identify and correct these errors.

In a paper published today in Nature, we introduce AlphaQubit, an AI-based decoder that identifies quantum computing errors with state-of-the-art accuracy.

This collaborative work brought together Google DeepMind’s machine learning knowledge and Google Quantum AI’s error correction…

AI is revolutionizing the landscape of scientific research, enabling advancements at a pace that was once unimaginable — from accelerating drug discovery to designing new materials for clean energy technologies.

The AI for Science Forum — co-hosted by Google DeepMind and the Royal Society — brought together the scientific community, policymakers, and industry leaders to explore the transformative potential of AI to drive scientific breakthroughs, address the world's most pressing challenges, and lead to a new era of discovery.

Technologies Pushing the frontiers of audio generation ShareCopy link ×Our pioneering speech generation technologies are helping people around the world interact with more natural, conversational and intuitive digital assistants and AI tools.

Pioneering techniques for audio generation For years, we've been investing in audio generation research and exploring new ways for generating more natural dialogue in our products and experimental tools.

Download audio Audio clip of two speakers telling a funny story, with laughter at the punchline.

Download audio Audio clip of two speakers expressing excitement about a surprise birthday party.

Scaling our audio generation models Scaling our single-spe…

Technologies New generative AI tools open the doors of music creation ShareCopy link ×Our latest AI music technologies are now available in MusicFX DJ, Music AI Sandbox and YouTube Shorts For nearly a decade, our teams have been exploring how artificial intelligence (AI) can support the creative process, building tools that empower enthusiasts and professionals to discover new forms of creative expression.

Their input has been guiding our state-of-the-art generative music experiments, and helping us ensure that our new generative AI tools responsibly open the doors of music creation to everyone.

We’re also announcing updates to our music AI toolkit, called Music AI Sandbox, and highlighting…

This morning, Co-founder and CEO of Google DeepMind and Isomorphic Labs Sir Demis Hassabis, and Google DeepMind Senior Research Scientist Dr. John Jumper were co-awarded the 2024 Nobel Prize in Chemistry for their work developing AlphaFold, a groundbreaking AI system that predicts the 3D structure of proteins from their amino acid sequences.

AlphaFold’s predictions, made freely available through the AlphaFold Protein Structure Database, have given more than 2 million scientists and researchers from 190 countries a powerful tool for making new discoveries.

In a statement released after informed of the news, Demis Hassabis said:"Receiving the Nobel Prize is the honour of a lifetime.

After rec…

Research How AlphaChip transformed computer chip design ShareCopy link ×Our AI method has accelerated and optimized chip design, and its superhuman chip layouts are used in hardware around the world In 2020, we released a preprint introducing our novel reinforcement learning method for designing chip layouts, which we later published in Nature and open sourced.

Today, we’re publishing a Nature addendum that describes more about our method and its impact on the field of chip design.

Computer chips have fueled remarkable progress in artificial intelligence (AI), and AlphaChip returns the favor by using AI to accelerate and optimize chip design.

Using AI to design Google’s AI accelerator chips…

Developers can access our latest models for free via Google AI Studio and the Gemini API.

With the latest updates, 1.5 Pro and Flash are now better, faster, and more cost-efficient to build with in production.

For more details on migrating to the latest versions of Gemini 1.5 Pro and 1.5 Flash, check out the Gemini API models page.

Gemini 1.5 Pro We continue to be blown away with the creative and useful applications of Gemini 1.5 Pro’s 2 million token long context window and multimodal capabilities.

Increased rate limits To make it even easier for developers to build with Gemini, we are increasing the paid tier rate limits for 1.5 Flash to 2,000 RPM and increasing 1.5 Pro to 1,000 RPM, up f…

We’re changing that, and making these incredible technologies more easily accessible to millions of creators and billions of users around the world.

Over the next few months, we’re bringing our advanced generative AI models, Veo and Imagen 3 , to YouTube creators through Dream Screen .

Artificial intelligence (AI) technologies for generating creative content are improving rapidly, but seamless ways of using them still aren’t widely available.

New video generation technology in YouTube Shorts will help millions of people realize their creative visionCreators can soon generate video with Dream ScreenBy integrating Veo into Dream Screen, YouTube creators will soon be able to generate exciting …

Research Our latest advances in robot dexterity ShareCopy link ×Two new AI systems, ALOHA Unleashed and DemoStart, help robots learn to perform complex tasks that require dexterous movement People perform many tasks on a daily basis, like tying shoelaces or tightening a screw.

Today, we introduce two new papers featuring our latest artificial intelligence (AI) advances in robot dexterity research: ALOHA Unleashed which helps robots learn to perform complex and novel two-armed manipulation tasks; and DemoStart which uses simulations to improve real-world performance on a multi-fingered robotic hand.

This helps the robot learn from the data, so it can perform the same tasks on its own.

To ena…

Research AlphaProteo generates novel proteins for biology and health research ShareCopy link ×New AI system designs proteins that successfully bind to target molecules, with potential for advancing drug design, disease understanding and more.

Today, we introduce AlphaProteo, our first AI system for designing novel, high-strength protein binders to serve as building blocks for biological and health research.

AlphaProteo can generate new protein binders for diverse target proteins, including VEGF-A, which is associated with cancer and complications from diabetes.

Learning the intricate ways proteins bind to each other Protein binders that can bind tightly to a target protein are hard to desig…

Research FermiNet: Quantum physics and chemistry from first principles ShareCopy link ×Using deep learning to solve fundamental problems in computational quantum chemistry and explore how matter interacts with light Note: This blog was first published on 19 October 2020.

Our neural network architecture, FermiNet (Fermionic Neural Network), is well-suited to modeling the quantum state of large collections of electrons, the fundamental building blocks of chemical bonds.

A brief history of quantum mechanics Mention “quantum mechanics” and you’re more likely to inspire confusion than anything else.

But even the humble covalent bond — the basic building block of chemistry — is a consequence of t…

Company A new generation of African talent brings cutting-edge AI to scientific challenges ShareCopy link ×Food security, healthcare and exploring the cosmos are among the ways students of a new pan-African Master’s program aspire to apply AI At Google DeepMind, we’re committed to supporting the next generation of artificial intelligence (AI) leaders to help build a stronger, more diverse and inclusive global AI community.

Students have the opportunity to accelerate scientific discovery, with mentoring and support from Google DeepMind’s researchers and engineers.

As the next generation of AI leaders in Africa, Béria Chingnabé Kalpélbé, Olivier Mahumawon Adjagba and Diffo Mboudjiho Annette D…

HighLevel is an all-in-one sales and marketing platform built for agencies.

We empower businesses to streamline their operations with tools like CRM, marketing automation, appointment scheduling, funnel building, membership management, and more.

But what truly sets HighLevel apart is our commitment to AI-powered solutions, helping our customers automate their businesses and achieve remarkable results.

As a software as a service (SaaS) platform experiencing rapid growth, we faced a critical challenge: managing a database that could handle volatile write loads.

Our business often sees database writes surge from a few hundred requests per second (RPS) to several thousand within minutes.

Generative AI is giving people new ways to experience audio content, from podcasts to audio summaries.

For example, users are embracing NotebookLM’s recent Audio Overview feature, which turns documents into audio conversations.

While Notebook LM offers incredible benefits for making sense of complex information, some users want more control over generating unique audio experiences – for example, creating their own podcasts.

Podcasts are an increasingly popular medium for creators, business leaders, and users to listen to what interests them.

Today, we’ll share how Gemini 1.5 Pro and the Text-to-Speech API on Google Cloud can help you create conversations with diverse voices and generate pod…

Google Kubernetes Engine (GKE) was a primary reason for Moloco selecting Google Cloud over other cloud providers.

The Google Cloud team supported Moloco in implementing solutions that ensured a smooth transition and minimal disruption to operations.

Specifically, Moloco worked with the Google Cloud team to migrate its ML workloads to GKE using the platform's autoscaling and pod prioritization capabilities to optimize resource utilization and cost efficiency.

Additionally, Moloco integrated Cloud TPUs into its training pipeline, resulting in significantly reduced training times for complex ML models.

And Google Cloud continues to invest in GKE so it can handle even the most demanding AI trai…

Agentic RAG represents a significant leap forward, combining the power of information retrieval with advanced action planning capabilities.

Today, we're excited to announce a collaboration with LlamaIndex on open-source integrations for Google Cloud databases including AlloyDB for PostgreSQL and Cloud SQL for PostgreSQL.

The integrations include:In addition, developers can also access previously published LlamaIndex integrations for Firestore, including for Vector Store and Index Store.

Integration benefitsLlamaIndex supports a broad spectrum of different industry use cases, including agentic RAG, report generation, customer support, SQL agents, and productivity assistants.

Through these us…

The story of Fireworks AI started seven years ago at Meta AI, where a group of innovators worked on PyTorch — an ambitious project building leading AI infrastructure from scratch.

Fireworks AI delivers the fastest and most efficient gen AI inference engine to date.

We’re pushing the boundaries with compound AI systems, which replace more traditional single AI models with multiple interacting models.

Keeping AI open source and accessible is paramount, and that's one of the reasons we continue to work with Google Cloud.

With Google Cloud, we can enable more companies to drive value from innovative uses of gen AI.

Understanding multimodal AI & long context window:Before we step into the kitchen, let's break down what "multimodal" and “long context window” mean in the world of AI:Multimodal AI can process and understand multiple types of data.

Think of it as an AI system that can see, hear, read, and understand all at once.

With a projected market size of over $13 billion by 2032 and a staggering CAGR of around 30% from 2024 to 2032, multimodal plus long context window capabilities are the secret ingredients for success.

Let’s look at a real world exampleWhen it comes to running a restaurant, AI can step in as is your inventory manager and safety inspector all rolled into one.

In the following test, w…

Consistent with our AI Principles, Veo and Imagen 3 on Vertex AI were built with safety at the core.

Digital watermarking: Google DeepMind's SynthID embeds invisible watermarks into every image and frame that Imagen 3 and Veo produce, helping decrease misinformation and misattribution concerns.

Safety filters: Veo and Imagen 3 both have built-in safeguards to help protect against the creation of harmful content and adhere to Google’s Responsible AI Principles.

"Our collaboration with Google Cloud has been instrumental in harnessing the power of generative AI, notably through Imagen 3, to revolutionize content production.

They’re now testing Imagen and Veo on Vertex AI to create visuals, all…

At the Gemini for Work event in September, we showcased how generative AI is transforming the way enterprises work.

Gen AI has the potential to revolutionize how we work, but only if its output is reliable and relevant.

This is where concepts like grounding, retrieval augmented generation (RAG), and search come into play.

RAG is a specific technique for grounding that finds relevant information from a knowledge base and gives it to the LLM as context.

Search is the core retrieval technology behind RAG, as it's how the system finds the right information in the knowledge base.

Furthermore, gen AI can enable the creation of digital twins of the Vodafone network, coupled with ground classification capabilities.

These use cases highlight the transformative potential of gen AI in revolutionizing various aspects of Vodafone's network operations.

By embracing gen AI, Vodafone is not only driving innovation but also paving the way for a more efficient, agile, and customer-centric future.

Cost reduction: By automating routine tasks and optimizing network operations, gen AI can significantly reduce operational costs for Vodafone.

AI Booster, a sophisticated machine learning platform built on Google Cloud's Vertex AI, serves as the engine room for Vodafone's AI development.

The concept of using automated tooling within a CI/CD pipeline to proactively detect issues with code quality isn’t entirely new.

However, the advances in generative AI present new opportunities that go beyond the capabilities of traditional code analysis.

This doesn’t mean that the AI tools are in a position to replace the trusted tools and processes altogether.

For example Gemini Code Assist is a end user application that is built on top of the Gemini models and provides an assistant that helps in code generation, transformation and understanding as mentioned above.

Developers can also directly integrate Gemini models in their own application through Vertex AI, an end-to-end platform whic…

We’ll start by leveraging Vertex AI Agent Builder and Conversational Agents (Dialogflow CX) to create it.

This allows the chatbot to access and retrieve relevant information from the article in real time, providing comprehensive answers to user queries.

Conversational Agents (Dialogflow CX): We design the conversational flow using Conversational Agents (Dialogflow CX), enabling the chatbot to understand user intent and respond appropriately.

Generators: We create a generator in Conversational Agents (Dialogflow CX) named "Summarize_mail" that utilizes a zero-shot prompt (direct prompt with no examples) to summarize the conversation.

Here's the zero-shot prompt we use:

Your company has given you full creative freedom to build a minimal viable product using Google’s generative AI products, so you’ve chosen to use Gemini 1.5 Pro and loop in other external APIs.

The first step is to define potential queries that any user might enter into the Gemini chat.

Notebook setupTo use Gemini 1.5 Pro for development, you’ll need to either create or use an existing project in Google Cloud.

Working in a Jupyter notebook environment is one of the easiest way to get started developing with Gemini 1.5 Pro.

First, you’ll need to install the latest version of the Vertex AI SDK for Python, import the necessary modules, and initialize the Gemini model:1.

This involves identifying key information related to the specific financial advisory document that needs to be completed.

Summary generation (4.5):This step focuses on generating concise and accurate summaries for each field within the financial advisory document.

By streamlining these workflows, Commerzbank empowers its sales advisors to focus on higher-value tasks, ultimately improving client service and driving business growth.

By automating the manual tasks associated with financial advisory documentation, Commerzbank has achieved substantial productivity gains.

Looking into the FutureCommerzbank's collaboration with Google Cloud exemplifies the transformative power of AI in the financi…

In July, we announced the availability of Mistral AI’s models on Vertex AI: Codestral for code generation tasks, Mistral Large 2 for high-complexity tasks, and the lightweight Mistral Nemo for reasoning tasks like creative writing.

Today, we’re announcing the availability of Mistral AI’s newest models on Vertex AI Model Garden: Mistral-Large-Instruct-2411 is now generally available, and the new Codestral-2411 will be available in the coming weeks.

Large-Instruct-2411 : Mistral AI’s latest version of Mistral Large is an advanced dense large language model (LLM) of 123B parameters with strong reasoning, knowledge and coding capabilities extending its predecessor with better long context, func…

In July, we announced the availability of Mistral AI’s models on Vertex AI: Codestral for code generation tasks, Mistral Large 2 for high-complexity tasks, and the lightweight Mistral Nemo for reasoning tasks like creative writing.

Today, we’re announcing the availability of Mistral AI’s newest model on Vertex AI Model Garden: Mistral-Large-Instruct-2411 is now generally availableLarge-Instruct-2411 is an advanced dense large language model (LLM) of 123B parameters with strong reasoning, knowledge and coding capabilities extending its predecessor with better long context, function calling and system prompt.

The model is ideal for use cases that include complex agentic workflows with precise…

“It recognises the value of our award-winning journalism and will give us early insights into how content is surfaced through AI.

“Apart from the benefits to the FT, there are broader implications for the industry.

It’s right, of course, that AI platforms pay publishers for the use of their material.

“We value the opportunity to be inside the development loop as people discover content in new ways.

As with any transformative technology, there is potential for significant advancements and major challenges, but what’s never possible is turning back time.

OpenAI, alongside industry leaders including Amazon, Anthropic, Civitai, Google, Meta, Metaphysic, Microsoft, Mistral AI, and Stability AI, has committed to implementing robust child safety measures in the development, deployment, and maintenance of generative AI technologies as articulated in the Safety by Design principles.

By adopting comprehensive Safety by Design principles, OpenAI and our peers are ensuring that child safety is prioritized at every stage in the development of AI.

Responsibly source our training datasets, detect and remove child sexual abuse material (CSAM) and child sexual exploitation material (CSEM) from training data, and report any confirmed CSAM to the relevant a…

Customers with a sustained level of tokens per minute (TPM) usage on GPT-4 or GPT-4 Turbo can request access to provisioned throughput to get discounts ranging from 10–50% based on the size of the commitment.

Reduced costs on asynchronous workloads: Customers can use our new Batch API to run non-urgent workloads asynchronously.

Batch API requests are priced at 50% off shared prices, offer much higher rate limits, and return results within 24 hours.

We plan to keep adding new features focused on enterprise-grade security, administrative controls, and cost management.

For more information on these launches, visit our API documentation or get in touch with our team to discuss custom solution…

Our new local presence also gets us closer to leading businesses like Daikin, Rakuten, and TOYOTA Connected who are using ChatGPT Enterprise to automate complex business processes, assist in data analysis, and optimize internal reporting.

ChatGPT also helps accelerate the efforts of local governments, such as Yokosuka City, which is leveraging the technology to improve the efficiency of public services in Japan.

Over the past year, the city has gradually provided ChatGPT access to almost all city employees, and 80% have reported increases in productivity.

Now Yokosuka City has formed a network with 21 local governments—including the Tokyo Metropolitan Government and the City of Kobe—to …

Assisted Fine-TuningAt DevDay last November, we announced a Custom Model program designed to train and optimize models for a specific domain, in partnership with a dedicated group of OpenAI researchers.

Since then, we've met with dozens of customers to assess their custom model needs and evolved our program to further maximize performance.

Today, we are formally announcing our assisted fine-tuning offering as part of the Custom Model program.

Fully custom-trained models imbue new knowledge from a specific domain by modifying key steps of the model training process using novel mid-training and post-training techniques.

Our team modified every step of the model training process, from domain-s…

We’ve also introduced additional content safeguards for this experience, such as blocking prompts and generations in a wider range of categories.

There are many benefits to creating an account including the ability to save and review your chat history, share chats, and unlock additional features like voice conversations and custom instructions.

For anyone that has been curious about AI’s potential but didn’t want to go through the steps to set-up an account, start using ChatGPT today.

We recognize that generating speech that resembles people's voices has serious risks, which are especially top of mind in an election year.

We are engaging with U.S. and international partners from across government, media, entertainment, education, civil society and beyond to ensure we are incorporating their feedback as we build.ÂThe partners testing Voice Engine today have agreed to our usage policies, which prohibit the impersonation of another individual or organization without consent or legal right.

In addition, our terms with these partners require explicit and informed consent from the original speaker and we don’t allow developers to build ways for individual users to create the…

Starting his career at DreamWorks Animation, Don Allen III is a multidisciplinary creator, speaker and consultant who collaborates with major tech and entertainment companies on mixed reality, virtual reality and AI applications.

“For a long time I've been making augmented reality hybrid creatures that I think would be fun combinations in my head.

Now I have a much easier way of prototyping the ideas before I fully build out the 3-D characters to place in spatial computers.” Don cites Sora’s “weirdness” as its greatest strength: “It’s not bound by traditional laws of physics or conventions of thought.” He says that working with Sora shifted his focus from “technical hurdle…

Echoing this sentiment, Louis Dreyfus, CEO of Le Monde, stated, "At the moment we are celebrating the 80th anniversary of Le Monde, this partnership with OpenAI allows us to expand our reach and uphold our commitment to providing accurate, verified, balanced news stories at scale.

Collaborating with OpenAI ensures that our authoritative content can be accessed and appreciated by a broader, more diverse audience. ÂEvery shift in the media landscape has presented Le Monde with new opportunities.

From the transition to digital platforms to embracing the era of free media, Le Monde has consistently seized these moments to underscore its commitment to independence, expertise, and journalistic i…

Additionally, Sam Altman, CEO, will rejoin the OpenAI Board of Directors.ÂSue, Nicole and Fidji have experience in leading global organizations and navigating complex regulatory environments, including backgrounds in technology, nonprofit and board governance.

They will work closely with current board members Adam D’Angelo, Larry Summers and Bret Taylor as well as Sam and OpenAI’s senior management.ÂBret Taylor, Chair of the OpenAI board, stated, “I am excited to welcome Sue, Nicole, and Fidji to the OpenAI Board of Directors.

She also served as President of Sony Entertainment, Inc., and simultaneously served as President of Sony Corporation of America.

She also serves as a member of …

The Special Committee of the OpenAI Board today announced the completion of the review by WilmerHale.

The firm conducted dozens of interviews with members of OpenAI’s prior Board, OpenAI executives, advisors to the prior Board, and other pertinent witnesses; reviewed more than 30,000 documents; and evaluated various corporate actions.

“We have unanimously concluded that Sam and Greg are the right leaders for OpenAI,” stated Bret Taylor, Chair of the OpenAI Board.

The Special Committee acknowledged the important work done by WilmerHale in conducting this extensive review and thanked OpenAI current and former Board members, advisors and employees for their cooperation.

The Special Commi…

Date: January 31, 2018 at 11:54:30 PM PSTSubject: Re: Top AI institutions todayWorking at the cutting edge of AI is unfortunately expensive.

For example,In addition to DeepMind, Google also has Google Brain, Research, and Cloud.

If historical trends are any indication, progress in AI is primarily driven by systems - compute, data, infrastructure.

Not only that, but any algorithmic advances published in a paper somewhere can be almost immediately re-implemented and incorporated.

The “second stage” would be a full self driving solution based on large-scale neural network training, which OpenAI expertise could significantly help accelerate.

This technical report focuses on (1) our method for turning visual data of all types into a unified representation that enables large-scale training of generative models, and (2) qualitative evaluation of Sora’s capabilities and limitations.

Model and implementation details are not included in this report.

Much prior work has studied generative modeling of video data using a variety of methods, including recurrent networks,[^1][^2][^3] generative adversarial networks,[^4][^5][^6][^7] autoregressive transformers,[^8][^9] and diffusion models.

[^10][^11][^12] These works often focus on a narrow category of visual data, on shorter videos, or on videos of a fixed size.

Sora is a generalist mo…

Based on collaboration and information sharing with Microsoft, we disrupted five state-affiliated malicious actors: two China-affiliated threat actors known as Charcoal Typhoon and Salmon Typhoon; the Iran-affiliated threat actor known as Crimson Sandstorm; the North Korea-affiliated actor known as Emerald Sleet; and the Russia-affiliated actor known as Forest Blizzard.

The identified OpenAI accounts associated with these actors were terminated.

Salmon Typhoon used our services to translate technical papers, retrieve publicly available information on multiple intelligence agencies and regional threat actors, assist with coding, and research common ways processes could be hidden on a system.…

We’re testing memory with ChatGPT.

Remembering things you discuss across all chats saves you from having to repeat information and makes future conversations more helpful.

You're in control of ChatGPT's memory.

You can explicitly tell it to remember something, ask it what it remembers, and tell it to forget conversationally or through settings.

We are rolling out to a small portion of ChatGPT free and Plus users this week to learn how useful it is.

The other one actually is some token actually is very, very hard to be predicted during the pretraining.

And the important thing for the data is about data filtering.

If we’re able to build a better model actually is able to benefit so many different kinds of application.

And definitely there’s a lot of things about how to build a better data [that] is unsolved yet in the literature.

And the other thing actually, we are working on something that’s very exciting.

FOSTER: So this is a, kind of, a theoretical work on reinforcement learning, or RL.

FOSTER: Yeah, so if you look at these sort of RL problems with latent dynamics, this is something that’s actually received a lot of investigation in theory.

Like, can we take existing algorithms and use them to solve rich-observation RL problems in a modular fashion?

TINGLE: Dylan, I’d like to know—and I’m sure our audience would, too—what this work means when it comes to real-world application.

TINGLE: Well, Dylan Foster, thank you for joining us today to discuss your paper on reinforcement learning under latent dynamics.

The drawback of this approach is that it often misses the cultural nuances of local languages.

CHITALE: Now that we have created a benchmark, the next step is to evaluate how these multimodal models are performing on this benchmark.

So what we observed is there is a huge gap when it comes … in performance when we compare these proprietary offerings versus the open-source models.

These open-source models significantly lag behind the proprietary models.

CHITALE: CVQA is significant because it addresses a fundamental gap in how we evaluate vision-language and multimodal models today.

My guest on this episode is Nicole Immorlica, a senior principal research manager at Microsoft Research New England, where she leads the Economics and Computation Group.

HUIZINGA: Right, right, right …IMMORLICA: In terms of other inspirations, I’ve really admired throughout my career … this is maybe more structurally how different individuals operate their careers.

And it’s just really, really cool.

HUIZINGA: Right, right, right.

HUIZINGA: Yeah, yeah.

Welcome to Research Focus, a series of blog posts that highlights notable publications, events, code/datasets, new hires and other milestones from across the research community at Microsoft.

However, rapid advancements in large language models (LLMs) are continually pushing the boundaries of language comprehension and generation.

Read the paperMicrosoft Research in the news Can AI spot the next bomb cyclone far in advance?

How Microsoft's next-gen BitNet architecture is turbocharging LLM efficiency VentureBeat | November 13, 2024 One-bit large language models (LLMs) have emerged as a promising approach to making generative AI more accessible and affordable.

In a new paper, Microsoft researc…

Microsoft Research has used this approach to develop the large market model (LMM) and the Financial Market Simulation Engine (MarS) for the financial domain.

These innovations have the potential to empower financial researchers to customize generative models for diverse scenarios, establishing a new paradigm for applying generative models to downstream tasks in financial markets.

Applying generative models to financial marketsIn recent years, generative foundation models have achieved notable success in fields like natural language processing and media generation.

: Orders, as the atomic data in the financial market, provide a comprehensive and detailed representation of the real market.

Bu…

In this blog, we discuss prompting strategies to make the most of o1-preview models and other factors to consider as well as directions forward for run-time strategies.

While the results for GPT-4o with Medprompt fall short of o1-preview model performance, the combination offers a more cost-effective alternative.

The o1-preview model exhibits distinct run-time behaviors compared to the GPT series.

While some of our more dynamic prompting strategies performed better than expected with o1-preview models, our most tried-and-true strategy was anything but consistent throughout our evaluation.

We’re excited by the performance gains from GPT models to o1-preview models.

TamGen offers a new approach to drug discovery by applying the principles of generative AI to molecular design.

Compared with the traditional screening-based approach to drug discovery, a generative AI-based approach enables the discovery of novel compounds.

We evaluated these methods using the CrossDocked benchmark, a dataset used in AI research to assess the quality of molecule generation conditioned on a target protein.

We proposed the Design-Refine-Test pipeline to effectively identify molecular compounds for TB drug discovery.

AI’s potential in drug discoveryTamGen showcases the transformative potential of generative AI in drug design, combining advanced molecular modeling with researc…

# Factors to Consider When Selecting a Health Insurance Plan During Open Enrollment for 2024Selecting a health insurance plan during the open enrollment period can be a complex process, but understanding the key factors to consider can help individuals make informed decisions.

These professionals can help individuals understand their options and make informed decisions without recommending specific plans [Data: Sources (47424, 47425, 47426)].

### Types of Health Insurance PlansWhen selecting a health insurance plan during the open enrollment period for 2024, individuals in the United States have several options to consider:1.

For HDHPs, the deductible is at least $1,600 for individual cover…

Joining me today to discuss the state of DNA data storage and some of our contributions are several members of the DNA Data Storage Project at Microsoft Research: Principal Researcher Bichlien Nguyen, Senior Researcher Jake Smith, and Partner Research Manager Sergey Yekhanin.

Once we do this, we return to our original data and we’ve completed, let’s call it, one DNA data storage cycle.

So, like, I mean, coding is an important aspect of this whole idea of DNA data storage because we have to deal with errors—it’s a new medium—but talking about error-correcting codes in the context of DNA data storage, so, I mean, usually, like … what are error-correcting codes about?

In DNA data storage, the …

He reflects on his journey, the evolution of technology, and the opportunities ahead for Microsoft Research Asia – Tokyo.

Yasuyuki Matsushita, Microsoft Research Asia – TokyoWhy return to Microsoft Research Asia?

You worked at Microsoft Research Asia in Beijing from 2003 to 2015 before transitioning to academia.

Yasuyuki Matsushita: Microsoft Research Asia has always been an exceptional place for conducting cutting-edge research, especially in the AI era.

Understanding embodied AI beyond roboticsQuestion: Your current research interests include embodied AI, which is also one of the key areas at Microsoft Research Asia – Tokyo.

In this blog, we introduce BiomedParse (opens in new tab), a new approach for holistic image analysis by treating object as the first-class citizen.

Through joint pretraining of object recognition, detection, and segmentation, BiomedParse opens new possibilities for holistic image analysis and image-based discovery in biomedicine.

Image parsing: a unifying framework for holistic image analysisBack in 2005, researchers first introduced the concept of “image parsing”—a unified approach to image analysis that jointly conducts object recognition, detection, and segmentation.

With our model, BiomedParse, we have created a foundation for biomedical image parsing that leverages interdependencies a…

Here, we introduce dynamic community selection to the global search algorithm, which leverages the knowledge graph structure of the indexed dataset.

Figure 1: Dynamic community selection workflowThe dynamic global search approach has two main benefits.

* Note that we only evaluate answers from dynamic search at community level 3, which contains more community reports than static search at level 1.

### Common Trends in Vaccination Rates for Major Diseases #### Decline in Vaccination Rates A significant trend observed across the dataset is the decline in vaccination rates for various diseases, including measles, mumps, rubella (MMR), and polio.

Generated response from static search (level 1) …

Orca-AgentInstruct is another step in this direction, where we explore using agentic flows to generate diverse and high-quality data at scale.

Synthetic Data Accelerated LLM Development: Over the past year, using synthetic data has greatly advanced the training of large language models (LLMs).

Successful use of synthetic data involves significant human effort in curating and filtering the data to ensure high quality.

AgentInstruct can create:High-quality data: AgentInstruct uses GPT-4, coupled with tools like search and code interpreters, to create high-quality data.

This has the potential to drive AI advances across multiple industries by making high-quality model training more efficient a…

I’m Bonnie Kruft, partner and deputy director of Microsoft Research AI for Science and your host for today.

KRUFT: Microsoft Research is one of the earliest institutions to apply AI in biomolecular simulation research.

Classical MD is fast but less accurate, while quantum MD is very accurate but computationally prohibitive for the protein study.

Thus, applying AI in biomolecular simulation can become the third way to achieve both ab initio—or first principles—accuracy and high efficiency.

For AI, AI2BMD proves AI can make a big difference to the dynamic protein structure study beyond AI for the protein static structure prediction.

What are the apps that are really going to change how we do things?”What are the measurable effects of AI?

Since 1947, U.S. GDP growth has averaged about 3 percent annually, with productivity growth at about 2 percent annually.

Some predictions have claimed AI will double growth or at least create a higher growth trajectory than usual.

“My argument is that we currently have the wrong direction for AI,” Acemoglu says.

“Market fundamentalism and technology fundamentalism might claim you should always go at the maximum speed for technology,” Acemoglu says.

People struggling with their mental health are more likely to browse negative content online, and in turn, that negative content makes their symptoms worse, according to a series of studies by researchers at MIT.

The researchers found that participants expressed better moods after browsing less-negative web pages, and participants with worse pre-browsing moods tended to browse more-negative web pages.

“The results contribute to the ongoing debate regarding the relationship between mental health and online behavior,” the authors wrote.

Those who were provided with labels were less likely to choose negative content and more likely to choose positive content.

“People with worse mental health t…

The details and specs of these tests, including the aerodynamics of a given car design, are typically not made public.

In looking at the process of new car design, the researchers found that, while there are AI models that could crank through many car designs to generate optimal designs, the car data that is actually available is limited.

Some researchers had previously assembled small datasets of simulated car designs, while car manufacturers rarely release the specs of the actual designs they explore, test, and ultimately manufacture.

Library of carsIn their new study, the team applied a morphing operation to each of the baseline car models.

The researchers also ran complex, computational…

In previous years, MIT faculty had participated sporadically in the discussions.

The conference also gathered attendees from governments, nongovernmental organizations, businesses, other academic institutions, and practitioners focused on stopping global biodiversity loss and advancing the 23 goals of the Kunming-Montreal Global Biodiversity Framework (KMGBF), an international agreement adopted in 2022 to guide global efforts to protect and restore biodiversity through 2030.

“There is an urgent need to deepen the relationship between academia and local governments of cities located in biodiversity hotspots,” said Angel.

The fund aims to support biodiversity conservation, ecosystem restorati…

MIT researchers explored the relationships and differences between the algorithms used to generate 2D images and 3D shapes, identifying the root cause of lower-quality 3D models.

Creating realistic 3D models for applications like virtual reality, filmmaking, and engineering design can be a cumbersome process requiring lots of manual trial and error.

But diffusion models underperform at directly generating realistic 3D shapes because there are not enough 3D data to train them.

However, 3D shapes produced this way tend to look blurry or oversaturated.

“By doing this, as the analysis in the paper predicts, it generates 3D shapes that look sharp and realistic,” he says.

Building on a decade of research, scientists from MIT and elsewhere have developed a new photonic chip that overcomes these roadblocks.

They demonstrated a fully integrated photonic processor that can perform all the key computations of a deep neural network optically on the chip.

But in addition to these linear operations, deep neural networks perform nonlinear operations that help the model learn more intricate patterns.

Optical data had to be converted into electrical signals and sent to a digital processor to perform nonlinear operations.

A fully-integrated networkAt the outset, their system encodes the parameters of a deep neural network into light.

The book outlines how grassroots data science and citizen data activism are generally rising forms of civic participation.

Out of the grassrootsD’Ignazio has long cultivated an interest in data science, digital design, and global matters.

Still, much of her work has focused on data architecture, data visualization, and the analysis of the relationship between data production and society.

“So much grassroots labor goes into the production of data,” D’Ignazio says.

There is much progress to be made in the application of data science to society, often by developing new tools for people to use.

Captivated as a child by video games and puzzles, Marzyeh Ghassemi was also fascinated at an early age in health.

Growing up in Texas and New Mexico in an engineering-oriented Iranian-American family, Ghassemi had role models to follow into a STEM career.

She had trained models to predict outcomes using health data, “and the mindset at the time was to use all available data.

First, she and her research group showed that learning models could recognize a patient’s race from medical images like chest X-rays, which radiologists are unable to do.

“I work on the robustness of machine learning models, and how a lack of robustness can combine with existing biases.

The team compared these generated images with actual satellite images taken of the same regions after Harvey hit.

The team’s physics-reinforced method generated satellite images of future flooding that were more realistic and accurate.

The first “generator” network is trained on pairs of real data, such as satellite images before and after a hurricane.

The team first tested how generative AI alone would produce satellite images of future flooding.

They trained a GAN on actual satellite images taken by satellites as they passed over Houston before and after Hurricane Harvey.

As the global conversation around assisted and automated vehicles (AVs) evolves, the MIT Advanced Vehicle Technology (AVT) Consortium continues to lead cutting-edge research aimed at understanding how drivers interact with emerging vehicle technologies.

“But building lasting trust requires us to go deeper, examining how drivers interact with these systems in practice.

AVT research aims to compare and contrast the benefits of various manufacturers’ embodiments of technologies.

“We’re not just interested in whether people are open to using assistive and automated vehicle technologies,” adds Reimer.

“As vehicle technologies evolve, it’s crucial to understand how they intersect with the everyda…

White House science advisor Arati Prabhakar expressed confidence in U.S. science and technology capacities during a talk on Wednesday about major issues the country must tackle.

“Let me start with the purpose of science and technology and innovation, which is to open possibilities so that we can achieve our great aspirations,” said Prabhakar, who is the director of the Office of Science and Technology Policy (OSTP) and a co-chair of the President’s Council of Advisors on Science and Technology (PCAST).

Much of Prabhakar’s talk focused on three major issues in science and technology development: cancer prevention, climate change, and AI.

Mavalvala, in turn, said MIT was “especially honored” …

To boost the reliability of reinforcement learning models for complex tasks with variability, MIT researchers have introduced a more efficient algorithm for training them.

The algorithm strategically selects the best tasks for training an AI agent so it can effectively perform all tasks in a collection of related tasks.

For their method, they choose a subset of tasks and train one algorithm for each task independently.

To identify which tasks they should select to maximize expected performance, the researchers developed an algorithm called Model-Based Transfer Learning (MBTL).

The MBTL algorithm has two pieces.

The project has produced the first-ever large-scale database of 600,000 environmentally aware, simulation-ready tree models across North America.

Now, as cities worldwide grapple with rising temperatures, this research offers a new window into the future of urban forests.

It’s a breezeWhile Tree-D fusion marks some major “growth” in the field, trees can be uniquely challenging for computer vision systems.

The Tree-D fusion models are “simulation-ready” in that they can estimate the shape of the trees in the future, depending on the environmental conditions.

“This marks just the beginning for Tree-D Fusion,” says Jae Joong Lee, a Purdue University PhD student who developed, implemented and d…

A crowd gathered at the MIT Media Lab in September for a concert by musician Jordan Rudess and two collaborators.

Each time the model took its turn, a range of expressions moved across Rudess’ face: bemusement, concentration, curiosity.

The researchers set out to develop a machine learning model channeling Rudess’ distinctive musical style and technique.

Rudess contributed the data on which Blanchard trained the AI model.

Because the samples he recorded to train the model were similar to ear-training exercises he’s used with students, he thinks the model itself could someday be used for teaching.

Since the 1970s, the field has evolved from writing sophisticated programs to using deep learning, teaching robots to learn directly from human behavior.

As robots become more sophisticated, this hands-on approach hits a scaling problem: the demand for high-quality training data far outpaces humans’ ability to provide it.

Dreams In Motion does this by considering the 3D geometry of the scene and the relative changes in the robot’s perspective.

“Although collecting demonstrations is easy, scaling a real-world robot teleoperation setup to thousands of skills is challenging because a human has to physically set up each scene.

But when robots collected their own training data through LucidSim, …

Virtual Personas for Language Models via an Anthology of BackstoriesWe introduce Anthology, a method for conditioning LLMs to representative, consistent, and diverse virtual personas by generating and utilizing naturalistic backstories with rich details of individual values and experience.

What does it mean for large language models (LLMs) to be trained on massive text corpora, collectively produced by millions and billions of distinctive human authors?

In this work, we introduce Anthology, an approach for steering LLMs to representative, consistent, and diverse virtual personas by providing richly detailed life narratives of individuals as conditioning context to models.

By grounding langu…

Linguistic Bias in ChatGPT: Language Models Reinforce Dialect DiscriminationSample language model responses to different varieties of English and native speaker reactions.

Over 1 billion people around the world speak varieties such as Indian English, Nigerian English, Irish English, and African-American English.

Then, we compared the language model responses to the “standard” varieties and the non-“standard” varieties.

Here, we included the original GPT-3.5 responses, plus responses from GPT-3.5 and GPT-4 where the models were told to imitate the style of the input.

That can reinforce barriers against speakers of non-“standard” varieties as AI models become increasingly used in …

How to Evaluate Jailbreak Methods: A Case Study with the StrongREJECT BenchmarkWhen we began studying jailbreak evaluations, we found a fascinating paper claiming that you could jailbreak frontier LLMs simply by translating forbidden prompts into obscure languages.

This blog post shows how to use a new, state-of-the art jailbreak benchmark - StrongREJECT - to accurately and robustly evaluate jailbreak methods.

PAP instructs an attacker model to persuade a victim model to give it harmful information using techniques like misrepresentation and logical appeals.

We conducted two experiments to test this hypothesis:We used StrongREJECT to evaluate 37 jailbreak methods on an unaligned model; Dolp…

Launching VHs: The Visual Haystacks Benchmark!

Humans excel at processing vast arrays of visual information, a skill that is crucial for achieving artificial general intelligence (AGI).

Visual Haystacks: the first "visual-centric" Needle-In-A-Haystack (NIAH) benchmark designed to rigorously evaluate Large Multimodal Models (LMMs) in processing long-context visual information.

The first NIAH benchmark for visual reasoning was introduced by Google in the Gemini-v1.5 technical report.

What is the Visual Haystacks (VHs) Benchmark?

TinyAgent: Function Calling at the EdgeThe ability of LLMs to execute commands through plain language (e.g.

The framework is open sourced and available at https://github.com/SqueezeAILab/TinyAgentTeaching LLMs to do Function CallingFigure 1: Overview of the LLMCompiler Function Calling Planner.

Once this function calling plan is generated, we can parse it and call each function based on the dependencies.

With our dataset in place, we can now proceed to fine-tune off-the-shelf SLMs to enhance their function calling capability.

Latency is the end-to-end latency of the function calling planner, including the prompt processing time and generation.

As computer vision researchers, we believe that every pixel can tell a story. However, there seems to be a writer’s block settling into the field when it comes to dealing with large images. Large images are no longer rare—the cameras we carry in our pockets and those orbiting our planet snap pictures so big and detailed that they stretch our current best models and hardware to their breaking points when handling them. Generally, we face a quadratic increase in memory usage as a function of image size.

Today, we make one of two sub-optimal choices when handling large images: down-sampling or cropping. These two methods incur significant losses in the amount of information and context present…

Modeling Extremely Large Images with xTAs computer vision researchers, we believe that every pixel can tell a story.

However, there seems to be a writer’s block settling into the field when it comes to dealing with large images.

Today, we make one of two sub-optimal choices when handling large images: down-sampling or cropping.

Why bother handling large images anyways?

That’s basically what we do with large images with $x$T.

2024 BAIR Graduate DirectoryEvery year, the Berkeley Artificial Intelligence Research (BAIR) Lab graduates some of the most talented and innovative minds in artificial intelligence and machine learning.

Our Ph.D. graduates have each expanded the frontiers of AI research and are now ready to embark on new adventures in academia, industry, and beyond.

These fantastic individuals bring with them a wealth of knowledge, fresh ideas, and a drive to continue contributing to the advancement of AI.

Join us in celebrating the achievements of BAIR’s latest PhD graduates.

Thank you to our friends at the Stanford AI Lab for this idea!

Every year, the Berkeley Artificial Intelligence Research (BAIR) Lab graduates some of the most talented and innovative minds in artificial intelligence and machine learning. Our Ph.D. graduates have each expanded the frontiers of AI research and are now ready to embark on new adventures in academia, industry, and beyond.

These fantastic individuals bring with them a wealth of knowledge, fresh ideas, and a drive to continue contributing to the advancement of AI. Their work at BAIR, ranging from deep learning, robotics, and natural language processing to computer vision, security, and much more, has contributed significantly to their fields and has had transformative impacts on society.

This…

In this post, we analyze the trend toward compound AI systems and what it means for AI developers.

We argue that compound AI systems will likely be the best way to maximize AI results in the future, and might be one of the most impactful trends in AI in 2024.

We define a Compound AI System as a system that tackles AI tasks using multiple interacting components, including multiple calls to models, retrievers, or external tools.

Developing Compound AI SystemsWhile compound AI systems can offer clear benefits, the art of designing, optimizing, and operating them is still emerging.

However, new compound AI systems contain non-differentiable components like search engines or code interpreters, a…

AI caught everyone’s attention in 2023 with Large Language Models (LLMs) that can be instructed to perform general tasks, such as translation or coding, just by prompting. This naturally led to an intense focus on models as the primary ingredient in AI application development, with everyone wondering what capabilities new LLMs will bring.

As more developers begin to build using LLMs, however, we believe that this focus is rapidly changing: state-of-the-art AI results are increasingly obtained by compound systems with multiple components, not just monolithic models.

For example, Google’s AlphaCode 2 set state-of-the-art results in programming through a carefully engineered system that uses L…

Mistral-NeMo-Instruct-2407 and Mistral-NeMo-Base-2407 overviewMistral NeMo, a powerful 12B parameter model developed through collaboration between Mistral AI and NVIDIA and released under the Apache 2.0 license, is now available on SageMaker JumpStart.

PrerequisitesTo try out both NeMo models in SageMaker JumpStart, you will need the following prerequisites:Discover Mistral NeMo models in SageMaker JumpStartYou can access NeMo models through SageMaker JumpStart in the SageMaker Studio UI and the SageMaker Python SDK.

In SageMaker Studio, you can access SageMaker JumpStart by choosing JumpStart in the navigation pane.

The search results will list Mistral NeMo Instruct and Mistral NeMo Base.

…

As generative AI continues to drive innovation across industries and our daily lives, the need for responsible AI has become increasingly important.

Responsible AI builds trust, and trust accelerates adoption and innovation.

With trust as a cornerstone of AI adoption, we are excited to announce at AWS re:Invent 2024 new responsible AI tools, capabilities, and resources that enhance the safety, security, and transparency of our AI services and models and help support customers’ own responsible AI journeys.

Amazon Nova Canvas and Amazon Nova Reel come with controls to support safety, security, and IP needs with responsible AI.

You can explore all 16 AI Service Cards on Responsible AI Tools an…

In this post, we show how to build a RAG application on Amazon SageMaker JumpStart using Facebook AI Similarity Search (FAISS).

By using pre-trained models and optimized hardware, SageMaker JumpStart allows you to quickly deploy both LLMs and embeddings models without spending too much time on configurations for scalability.

Embeddings model – We need an embeddings model to convert our document corpus into textual embeddings.

We use this notebook to demonstrate advanced RAG techniques with Meta Llama 3 8B on SageMaker JumpStart using the FAISS embedding store.

Deploy the modelBefore you start building the end-to-end RAG workflow, it’s necessary to deploy the LLM and embeddings model of your…

Capacity provisioned through SageMaker training plans can be used with either SageMaker training jobs or SageMaker HyperPod.

The following diagram provides an overview of the main steps involved in requesting capacity using SageMaker training plans for SageMaker training jobs.

Create a SageMaker training plan using the SageMaker consoleThe SageMaker console user experience for creating a training plan is similar for both training jobs and SageMaker HyperPod.

Use a training plan with SageMaker HyperPod with the AWS CLIComplete the following steps to use your training plan with the AWS CLI:Create a SageMaker HyperPod cluster from scratch.

For instructions, refer to the Amazon SageMaker HyperP…

Organizations can use these models securely, and for models that are compatible with the Amazon Bedrock Converse API, you can use the robust toolkit of Amazon Bedrock, including Amazon Bedrock Agents, Amazon Bedrock Knowledge Bases, Amazon Bedrock Guardrails, and Amazon Bedrock Flows.

Organizations can use these models securely, and for models that are compatible with the Amazon Bedrock Converse API, you can use the robust toolkit of Amazon Bedrock, including Amazon Bedrock Agents, Amazon Bedrock Knowledge Bases, Amazon Bedrock Guardrails, and Amazon Bedrock Flows.

Getting started with Bedrock Marketplace and NemotronTo get started with Amazon Bedrock Marketplace, open the Amazon Bedrock co…

Accelerate your AI development and deployment with Amazon SageMaker and TectonAll that manual complexity gets simplified with Tecton and Amazon SageMaker.

Together, Tecton and SageMaker abstract away the engineering needed for production, real-time AI applications.

It doesn’t matter if it’s batch, streaming, or real-time data or whether it’s offline or online serving.

The next section examines a fraud detection example to show how Tecton and SageMaker accelerate both training and real-time serving for a production AI system.

To get started, refer to Getting Started with Amazon SageMaker & Tecton’s Feature Platform, a more detailed guide on how to use Tecton with Amazon SageMaker.

You can now use Amazon Bedrock features such as Amazon Bedrock Knowledge Bases and Amazon Bedrock Guardrails with models deployed through SageMaker JumpStart.

In this post, we show you how to deploy FMs through SageMaker JumpStart, register them with Amazon Bedrock, and invoke them using Amazon Bedrock APIs.

Deploy a model with SageMaker JumpStart and register it with Amazon BedrockThis section provides a walkthrough of deploying a model using SageMaker JumpStart and registering it with Amazon Bedrock.

ConclusionIn this post, you learned how to deploy FMs through SageMaker JumpStart, register them with Amazon Bedrock, and invoke them using Amazon Bedrock APIs.

This integration between SageM…

At preview, Amazon Bedrock Model Distillation offers support for three model providers: Amazon, Anthropic, and Meta.

We first introduce the general concept of model distillation in Amazon Bedrock, and then focus on the important steps in model distillation, including setting up permissions, selecting the models, providing input dataset, commencing the model distillation jobs, and conducting evaluation and deployment of the student models after model distillation.

Amazon Bedrock Model Distillation workflowAmazon Bedrock offers two options for using Amazon Bedrock Model Distillation.

Model selectionCurrently, Amazon Bedrock Model Distillation supports student-teacher combinations within the s…

Formerly known as Amazon Bedrock Studio, Amazon Bedrock IDE is now incorporated into the Amazon SageMaker Unified Studio (currently in preview).

SageMaker Unified Studio combines various AWS services, including Amazon Bedrock, Amazon SageMaker, Amazon Redshift, Amazon Glue, Amazon Athena, and Amazon Managed Workflows for Apache Airflow (MWAA), into a comprehensive data and AI development platform.

In this blog post, we’ll focus on Amazon Bedrock IDE and its generative AI capabilities within the Amazon SageMaker Unified Studio environment.

Building a generative AI applicationSageMaker Unified Studio offers tools to discover and build with generative AI.

You can then remove resources from you…

Scaling machine learning (ML) workflows from initial prototypes to large-scale production deployment can be daunting task, but the integration of Amazon SageMaker Studio and Amazon SageMaker HyperPod offers a streamlined solution to this challenge.

In this post, we walk you through the process of scaling your ML workloads using SageMaker Studio and SageMaker HyperPod.

For instructions, refer to the Amazon SageMaker HyperPod workshop or Tutorial for getting started with SageMaker HyperPod.

aws —region sagemaker delete-domain \ --domain-id \ --retention-policy HomeEfsFileSystem=DeleteConclusionIn this post, we explored an approach to streamline your ML workflows using SageMaker Studio.

He s…

Amazon Kendra GenAI Index is a new index in Amazon Kendra designed for RAG and intelligent search to help enterprises build digital assistants and intelligent search experiences more efficiently and effectively.

Option 1: Use Amazon Kendra Gen AI Index within Amazon Kendra standaloneThe steps to create an Amanzon Kendra GenAI index are similar to Creating an index as described in the Amazon Kendra Developer Guide.

Option 2: Use Amazon Kendra GenAI Index as a retriever with Amazon Q BusinessOne of the main benefits of the Amazon Kendra GenAI Index is the usability of the index across multiple AWS services.

To create an Amazon Q Business application, refer to Creating an Amazon Q Business app…

They use fully managed services such as Amazon SageMaker AI to build, train and deploy generative AI models.

Introducing Amazon SageMaker partner AI appsToday, we’re excited to announce that AI apps from AWS Partners are now available in SageMaker.

You can now find, deploy, and use these AI apps privately and securely, all without leaving SageMaker AI, so you can develop performant AI models faster.

Available in SageMaker AI and SageMaker Unified Studio (preview)Data scientists and ML engineers can access these applications from Amazon SageMaker AI (formerly known as Amazon SageMaker) and from SageMaker Unified Studio.

You can access all available SageMaker partner AI apps directly from Sag…

Solution overviewThe QuickSight feature in Amazon Q Business is available on the Amazon Q Business console as well as through Amazon Q Business APIs.

At least one Amazon Q Business Pro user that has admin permissions to set up and configure Amazon Q Business.

Ask queries to Amazon Q BusinessTo start chatting with Amazon Q Business, complete the following steps:On the Amazon Q Business console, navigate to your application.

Clean upIf you no longer want to use this Amazon Q Business feature, delete the resources you created to avoid future charges:Delete the Amazon Q Business application: On the Amazon Q Business console, choose Applications in the navigation pane.

To learn more about Amazon…

New Relic is addressing these challenges by creating the New Relic AI custom plugin for Amazon Q Business.

Solution OverviewThe New Relic custom plugin for Amazon Q Business centralizes critical information and actions in one interface, streamlining your workflow.

When a user asks a question in the Amazon Q interface, such as “Show me problems with the checkout process,” Amazon Q queries the RAG ingested with the customers’ runbooks.

The custom plugin allows you to manage incidents without switching between New Relic AI and Amazon Q during critical moments.

The custom plugin allows you to manage incidents without switching between New Relic AI and Amazon Q during critical moments.

These updates build on the capabilities introduced in the original launch of the inference optimization toolkit (to learn more, see Achieve up to ~2x higher throughput while reducing costs by ~50% for generative AI inference on Amazon SageMaker with the new inference optimization toolkit – Part 1).

Amazon SageMaker Studio UI experienceIn this section, let’s walk through the Amazon SageMaker Studio UI experience to run an inference optimization job.

For speculative decoding, there is no additional optimization cost involved; the SageMaker inference optimization toolkit will package the right container and parameters for the deployment on your behalf.

ConclusionTo get started with the inferen…

Southeast Asia is embracing sovereign AI.

During his visit to the region, Huang also joined Bangkok-based cloud infrastructure company SIAM.AI Cloud onstage for a fireside chat on sovereign AI.

Canada, Denmark and Indonesia are among the countries that have announced initiatives to develop sovereign AI infrastructure powered by NVIDIA technology.

Highlighting the importance of sovereign AI development, Huang said, “The digital data of Thailand encodes the knowledge, the history, the culture, the common sense of your people.

Learn more about sovereign AI.

As MONAI celebrates its fifth anniversary, we’re witnessing the convergence of our vision for open medical AI with production-ready enterprise solutions.

For more information, see Advancing Cell Segmentation and Morphology Analysis with NVIDIA AI Foundation Model VISTA-2D and access the implementation through MONAI Model Zoo.

Each builds on the current momentum while pushing into new territories critical for the advancement of medical AI.

Multi-modal integrationThe future of medical AI lies in combining multiple data streams, from medical imaging and electronic health records to real-time sensor data.

Join us in building the futureTogether, we’re creating more than just software—we’re build…

This is achieved through CUDA Unified Virtual Memory (UVM), which provides a unified address space spanning both host (CPU) and device (GPU) memory.

Details on UVMUnified Virtual Memory (UVM), introduced in CUDA 6.0, creates a single virtual address space shared between the CPU and GPU, simplifying memory management for developers.

Conversely, when GPU memory is full, less-used pages are evicted back to host memory.

For a deeper dive into Unified Memory, including its benefits and practical examples of optimizations like prefetching and memory advice ( cudaMemAdvise ), refer to the technical blog Unified Memory for CUDA Beginners.

Using Unified Virtual Memory (UVM), speedups vary by operati…

Quantum computing — all trials, no errors: Quantum computing will make significant strides as researchers focus on supercomputing and simulation to solve the greatest challenges to the nascent field: errors.

BRYAN CATANZAROVice President of Applied Deep Learning ResearchPutting a face to AI: AI will become more familiar to use, emotionally responsive and marked by greater creativity and diversity.

The first generative AI models that drew pictures struggled with simple tasks like drawing teeth.

As agentic AI workloads grow — requiring communication across multiple interconnected AI models working together rather than monolithic and localized AI models — compute fabrics will be essential to d…

GeForce NOW is wrapping a sleigh-full of gaming gifts this month, stuffing members’ cloud gaming stockings with new titles and fresh offers to keep holiday gaming spirits merry and bright.

An Epic, Globetrotting AdventureUncover one of history’s greatest mysteries, streaming Indiana Jones and the Great Circle from the cloud.

Members can indulge their inner explorer by streaming Indiana Jones and the Great Circle on GeForce NOW at release.

Making the Cloud Merry and BrightFor gamers looking to take their cloud gaming journey even further, unlock the power of GeForce RTX-powered cloud gaming with a monthly GeForce NOW membership.

Whether looking to conquer new worlds, compete at the highest l…

Expanding its collaboration with NVIDIA, Amazon Web Services (AWS) revealed today at its annual AWS re:Invent conference that it has extended NVIDIA NIM microservices across key AWS AI services to support faster AI inference and lower latency for generative AI applications.

These prebuilt containers are built on robust inference engines, such as NVIDIA Triton Inference Server, NVIDIA TensorRT, NVIDIA TensorRT-LLM and PyTorch, and support a broad spectrum of AI models — from open-source community ones to NVIDIA AI Foundation models and custom ones.

NIM microservices now available directly from AWS include:NVIDIA Nemotron-4 , available in Amazon Bedrock Marketplace, Amazon SageMaker Jumpstart…

Announcement highlights include the availability of NVIDIA DGX Cloud on AWS and enhanced AI, quantum computing and robotics tools.

NVIDIA DGX Cloud on AWS for AI at ScaleThe NVIDIA DGX Cloud AI computing platform is now available through AWS Marketplace Private Offers, offering a high-performance, fully managed solution for enterprises to train and customize AI models.

NVIDIA CUDA-Q on Amazon Braket: Quantum Computing Made PracticalNVIDIA CUDA-Q is now integrated with Amazon Braket to streamline quantum computing development.

Cloudera is using NVIDIA AI on AWS to enhance its new AI inference solution, helping Mercy Corps improve the precision and effectiveness of its aid distribution techno…

NVIDIA Isaac Sim is now available on cloud instances of NVIDIA L40S GPUs in Amazon EC2 G6e instances, offering a 2x boost for scaling robotics simulation, and faster AI model training.

These leading robotics startups are all making advances using NVIDIA Isaac Sim on Amazon Web Services.

NVIDIA announced at AWS re:Invent today that Isaac Sim now runs on Amazon Elastic Cloud Computing (EC2) G6e instances accelerated by NVIDIA L40S GPUs.

Physical AI describes AI models that can understand and interact with the physical world.

Cobot has used Isaac Sim with its AI-powered cobot, Proxie, to optimize logistics in warehouses, hospitals, manufacturing sites, and more.

Research shows that antibody models must capture a range of possible conformations to reflect their biological behavior accurately, not just a single structure.

AlphaBind employs a stochastic greedy optimization approach to improve antibody binding affinity.

The optimized sequences were then grouped based on their edit distance from the parental antibody, with 2 to 11 mutations per group.

AlphaBind, powered by NVIDIA and AWS technologyAlphaBind integrates technologies from NVIDIA and AWS to optimize its performance.

ResultsAlphaBind demonstrated impressive performance across the following diverse antibody optimization campaigns:Generated thousands of high-affinity candidates for each parent…

The NVIDIA-Certified Professional: AI Infrastructure certification is designed for practitioners seeking to showcase advanced skills in deploying AI infrastructure.

The NVIDIA-Certified Professional: AI Operations certification is tailored for individuals seeking proficiency in managing and optimizing AI operations.

The workshop also provides practical experience with NVIDIA AI software and solutions, including NGC containers and the NVIDIA AI Enterprise software suite, making it ideal for professionals looking to deepen their AI operations expertise.

Both of these professional-level certifications build upon the foundational knowledge covered in the NVIDIA-Certified Associate: AI Infrastru…

In this episode of the NVIDIA AI Podcast, U.S. Special Advisor on International Disability Rights at the U.S. Department of State Sara Minkara and Timothy Shriver, chairman of the board of Special Olympics, discuss AI’s potential to enhance special education and disability inclusion.

They highlight the critical need to include the voices from disability communities in AI development and policy conversations.

Time Stamps2:12: Minkara and Shriver’s work on disability inclusion9:47: Benefits of AI for people with disabilities20:46: Notes from the recent G7 ministerial meeting on inclusion and disability24:51: Challenges and future directions of AI in disability inclusionYou Might Also Like…Tak…

(Medical AI for Synthetic Imaging) is a latent diffusion generative AI foundation model that can simulate high-resolution, full-format 3D CT images and their anatomic segmentations.

M3 is a framework that extends any multimodal LLM with medical AI experts such as trained AI models from MONAI’s Model Zoo.

MathWorks has integrated MONAI Label with its Medical Imaging Toolbox, bringing medical imaging AI and AI-assisted annotation capabilities to thousands of MATLAB users engaged in medical and biomedical applications throughout academia and industry.

has integrated MONAI Label with its Medical Imaging Toolbox, bringing medical imaging AI and AI-assisted annotation capabilities to thousands of…

The giving season rolls on with a SteelSeries discount only for GeForce NOW members and six new games to stream.

Turn Black Friday into Green Thursday with a new deal on GeForce NOW Ultimate and Performance memberships this week.

Those looking to try out the cloud gaming service can now level up their gaming with 50% off new Ultimate and Performance memberships for the first three months.

Performance members can stream at up to 1440p at 60 frames per second, and Ultimate members can stream up to 4K at 120 fps or 1080p at 240 fps.

Don’t let this festive deal slip away — give the gift of gaming this holiday season with GeForce NOW’s Black Friday sale.

Matching pandas requires stable ordering.

Supporting stable ordering in distinctFor deduplication in cudf.pandas, recall that stable ordering is required to match pandas.

Finally, use that Boolean mask to filter the input data, and its input order is retained.

Figure 1 shows data throughput for KEEP_ANY and KEEP_FIRST across a range of data sizes and data cardinalities.

For the fastest way to try GPU-accelerated DataFrame processing, check out RAPIDS cuDF pandas accelerator mode and RAPIDS cuDF for accelerated data science on Google Colab.

Agentic AI takes this one step further — using sophisticated, autonomous reasoning and iterative planning to help solve complex, multi-step problems.

Accelerated on NVIDIA RTX AI PCs, AnythingLLM has launched a new Community Hub where users can share prompts, slash commands and AI agent skills while experimenting with building and running AI agents locally.

Once prompted, an AI agent can gather and process data from various sources, including databases.

When the data it generates is fed back into the system, the AI agent becomes smarter and faster.

Accelerated by NVIDIA RTX AI PCs, these agents can perform inferencing and execute tasks faster than any other PC.

Meta’s ad recommendation engine, powered by deep learning recommendation models (DLRMs), has been instrumental in delivering personalized ads to people.

Learning from sequences: developing new sequence learning architectures to replace traditional DLRM neural network architectures.

A paradigm shift with learning from sequences for recommendation systemsMeta’s new system for ads recommendations uses sequence learning at its core.

Scaling the new sequence learning paradigmFollowing the redesign to shift from sparse feature learning to event-based sequence learning, the next focus was scaling across two domains — scaling the sequence learning architecture and scaling event sequences to be long…

At Open Compute Project Summit (OCP) 2024, we’re sharing details about our next-generation network fabric for our AI training clusters.

We’ve expanded our network hardware portfolio and are contributing two new disaggregated network fabrics and a new NIC to OCP.

At Meta, we believe that open hardware drives innovation.

At Meta, we envision a future of AI hardware systems that are not only scalable, but also open and collaborative.

We encourage anyone who wants to help advance the future of networking hardware for AI to engage with OCP and Meta to help share the future of AI infrastructure.

At the Open Compute Project (OCP) Global Summit 2024, we’re showcasing our latest open AI hardware designs with the OCP community.

These innovations include a new AI platform, cutting-edge open rack designs, and advanced network fabrics and components.

The open future of AI infraMeta is committed to open source AI.

We must also prioritize open and standardized models so we can leverage collective expertise, make AI more accessible, and work towards minimizing biases in our systems.​Just as important, we also need open AI hardware systems.

By addressing AI’s infrastructure needs together, we can unlock the true promise of open AI for everyone.​

Why we need better population mapsAccurate estimates of population are taken for granted in many countries.

As the world’s natural resource and energy demands scale, accurate population estimates also offer significant opportunities to improve sustainability efforts.

In addition to total population counts, Meta’s population maps also include demographic breakdowns for groups such as the number of children under five, women of reproductive age, youth, and the elderly.

AI-powered population estimates have been scientifically evaluated to be among the most accurate in the world for mapping population distribution for a variety of geographies and use-cases.

Please visit the Data for Good websit…

Meta is revamping its new data center design to optimize for artificial intelligence and the same methodology will be applicable for future data center optimizations as well.

As Meta is revamping its new data center design to optimize for artificial intelligence, the same methodology will be applicable for future data center optimizations as well to improve operational efficiency.

A reinforcement learning approach to data center coolingReinforcement learning (RL) is good at modeling control systems as sequential state machines.

There are also various RL approaches reported such as, transforming cooling optimization via deep reinforcement learning and data center cooling using model-predicti…

How PyTorch powers AI training and inferenceLearn about new PyTorch advancements for LLMs and how PyTorch is enhancing every aspect of the LLM lifecycle.

In this talk from AI Infra @ Scale 2024, software engineers Wanchao Liang and Evan Smothers are joined by Meta research scientist Kimish Patel to discuss our newest features and tools that enable large-scale training, memory efficient fine-tuning, and on-device LLM capabilities.

First, they cover the importance of memory-efficient fine-tuning and a few common architectural and algorithmic techniques to enable fine-tuning on consumer-grade hardware.

Then they discuss the challenges of deploying large models for on-device deployment and how …

In this talk from AI Infra @ Scale 2024, Joel Colburn, a software engineer at Meta, technical lead Junqiang Lan, and software engineer Jack Montgomery discuss the second generation of MTIA, Meta’s in-house training and inference accelerator.

They cover the co-design process behind building the second generation of Meta’s first-ever custom silicon for AI workloads, including the PyTorch software ecosystem, and the model architectures for Meta’s key applications.

They demonstrate how MTIA achieves the performance, efficiency, and developer experience to successfully launch models into production.

They also highlight several co-design examples where special silicon features are utilized to acc…

At AI Infra @ Scale 2024, Meta engineers discussed every step of how we built and brought Llama 3 to life, from data and training to inference.

Joe Spisak, Product Director and Head of Generative AI Open Source at Meta, talks about the history of Llama and Meta’s overarching vision for open source AI.

He’s joined by Delia David, a software engineer at Meta, to discuss all things data-related for GenAI.

Kaushik Veeraraghavan, a software engineer at Meta, discusses how Meta trains Llama at scale and delves into the data center, networking, and software investments that have enabled the development of Meta’s Llama 3 models.

Finally, Ye (Charlotte) Qia, a production engineer at Meta, discusses …

Delivering new AI technologies at scale also means rethinking every layer of our infrastructure – from silicon and software systems and even our data center designs.

For the second year in a row, Meta’s engineering and infrastructure teams returned for the AI Infra @ Scale conference, where they discussed the challenges of scaling up an infrastructure for AI as well as work being done on our large-scale GPU clusters, open hardware designs for next-generation data center hardware, and how Meta is building custom silicon like the Meta Training and Inference Accelerator (MTIA) to handle some of our AI training workloads.

Aparna Ramani, VP of Engineering at Meta, responsible for AI infrastructu…

Meta AI’s animate feature, which lets people generate a short animation of a generated image, carried unique challenges in this regard.

Here’s how we were able to deploy Meta AI’s animate feature using a combination of latency optimizations, traffic management, and other novel techniques.

We started by looking at the data for previous traffic on our AI-generated media both at their launches and over time.

With these changes, the preponderance of requests remained in region and latency dropped to roughly what we would expect.

The service tries to take a chunk of that region’s requests and offload them to a nearby region that can handle them without becoming more overloaded.

Our paper, “ RDMA over Ethernet for Distributed AI Training at Meta Scale ,” provides the details on how we design, implement, and operate one of the world’s largest AI networks at scale.

These RoCE clusters support an extensive range of production distributed GPU training jobs, including ranking, content recommendation, content understanding, natural language processing, and GenAI model training, among other workloads.

However, our experience with distributed AI training workloads provides a different perspective on tailoring the congestion control algorithms.

Moving forwardThe design and operation of large-scale RoCE networks for distributed AI training workloads have evolved to meet the …

What happens when a team of mechanical engineers get tired of looking at flat images of 3D models over Zoom?

Meet the team behind Caddy, a new CAD app for mixed reality.

They join Pascal Hartig (@passy) on the Meta Tech Podcast to talk about teaching themselves to code, disrupting the CAD software space, and how they integrated Caddy with Llama 3, and so much more!

Download or listen to the podcast episode below:You can also find the episode wherever you get your podcasts, including:The Meta Tech Podcast is a podcast, brought to you by Meta, where we highlight the work Meta’s engineers are doing at every level – from low-level frameworks to end-user features.

And if you’re interested in lea…

The key to developer velocity across AI lies in minimizing time to first batch (TTFB) for machine learning (ML) engineers.

AI Lab prevents TTFB regressions whilst enabling experimentation to develop improvements.

Optimizing TTFB helps ML engineers move fastThe overhead induced from TTFB is on the critical path for most ML development.

Here, we see the true utility of a framework like AI Lab and how it was used to facilitate this sweeping change.

O(Releases): Running a more holistic set of AI Lab tests prior to release and performing a bisect-like attribution process to find the root cause.

Improving tail utilization – the utilization level of the top 5% of the servers when ranked by utilization– within our infrastructure is imperative to operate our fleet efficiently and sustainably.

Challenges of load balancingThere are two approaches to load balancing:Routing load balancing – load balancing across replicas of a single model.

Placement load balancing – balancing load on hosts by moving replicas of a model across hosts.

It also exposed a deeper problem like spiky tail utilization, which was hidden behind the high tail utilization and was fixed once identified .

Optimizing tail utilization within IPnext thereby delivering these benefits to a broader range of expanding machine …

In our paper, Learn from Failure: Fine-Tuning LLMs with Trial-and-Error Data for Intuitionistic Propositional Logic Proving, we explored this problem experimentally.

Our goal was to assess the influence of trial-and-error information in the training data on the performance of LLMs in theorem proving.

However, at the time we published our paper, current approaches to training LLMs for ATPs only utilized data on correct proof attempts.

We hope our work can raise the community’s awareness of the importance of trial-and-error data for automated theorem proving.

We believe this advancement is largely due to the substantial trial-and-error data included in the model’s training process.

We will explore these challenges and provide an example of fine-tuning the Llama 3 8B Instruct model utilizing the neptune.ai experiment tracker.

The Llama 3 training data is seven times larger than what Meta used for training Llama 2.

For pre-training, Meta combined four types of parallelization, an approach they dubbed “4D parallelism”: data, model, pipeline, and context.

Hands-on guide: resource-efficient fine-tuning of Llama 3 on Google ColabFine-tuning Llama 3 8B is challenging, as it requires considerable computational resources.

We’ll use the Llama 3 8B model, which is sufficient for this task despite being the smallest Llama 3 model.

This is the process we will be going through in the following section to answer the question: When do I decide to run LLMs locally?

PrivacyA very obvious argument in favor of running LLMs locally is that, in some cases, there is no alternative.

Related ML/AI Platform Build vs Buy Decision: What Factors to Consider Read moreWhat does it take to run LLMs locally?

However, recent advancements in optimization techniques, such as quantization and attention mechanism optimizations, have made it possible to run LLMs locally, even on a CPU.

LM StudioLM Studio is a user-friendly application designed to run LLMs locally.

Like Union, Neptune excels in scalability, making it the ideal tracking solution for teams working on large-scale model training.

The new Neptune Flyte plugin enables you to use Neptune to track, visualize, and manage your models.

In this blog post, you’ll learn how to use the Neptune plugin on Union.

Orchestrate and track your models with Flytekit’s Neptune PluginIn Union, data and compute are fundamental building blocks for developing all workflows.

With flytekit’s Neptune plugin, you can easily track your experiments, visualize results, and debug your models.

What are LLM hallucinations?

LLM hallucinations become a problem in LLM-based applicationsMost of the time, if you use an LLM, you probably won’t use a base LLM but an LLM-based assistant whose goal is to help with your requests and reliably answer your questions.

Before we dive into this further, I’d like to stress that when thinking about LLM hallucinations, it’s important to keep in mind the difference between a base LLM and an LLM-based assistant.

When we talk about LLM hallucinations as a problematic phenomenon, it’s in the context of an LLM-based assistant or system.

While it’s unlikely that this process introduces new hallucinations, hallucinations seeded upstream are amplified.

LLM guardrails prevent models from generating harmful, biased, or inappropriate content and ensure that they adhere to guidelines set by developers and stakeholders.

LLM guardrails are small programs that validate and correct the modes’ outputs to ensure they align with your application’s specific requirements and context.

We’ll approach the broad and constantly evolving field of LLM guardrails in three stages:First, we’ll talk about the key vulnerabilities threatening AI applications.

We’ll explore these different kinds of LLM guardrails using the Guardrails AI framework, an open-source tool for building reliable AI applications.

| SourceRule-based data validationThe simplest type of LLM g…

TL;DR Reinforcement Learning from Human Feedback (RLHF) unlocked the full potential of today’s large language models (LLMs).

Reinforcement Learning from Human Feedback (RLHF) has turned out to be the key to unlocking the full potential of today’s large language models (LLMs).

Related LLM Evaluation For Text Summarization Read moreThe RLHF processThe RLHF process consists of three steps:Collecting human feedback.

Collecting human feedbackThe first step in RLHF is to collect human feedback in the so-called preference dataset.

We analyzed the three steps of the RLHF training pipeline: collecting human feedback, training the reward model, and fine-tuning the LLM.

However, when we look for data in a specific domain or organization, we often end up finding structured data.

The most likely reason is that structured data is still the de facto standard for quantitative information.

Consequently, in the age of Large Language Models (LLM), structured data still is and will continue to be relevant—even Microsoft is working on adding Large Language Models (LLMs) to Excel!

LLMs are mostly used with unstructured data, particularly text, but with the proper tools, they can also help tackle tasks with structured data.

Use case 3: Synthetic structured data generationWhen working with structured datasets, it is common to need more data with the same characteristic…

In this article, I’ll explore the following questions:What are the basic and advanced strategies for prompt engineering, and when should they be used?

Basic prompt engineering strategiesWhile there is no universal way to design a prompt, there are strategies we can follow to create prompts that yield LLM outputs closer to what we expect.

The basic strategies we cover in this section are straightforward to implement in a lot of different tasks, and they involve minimal customization.

Use Cases: When to use prompt templatesFinally, I recommend trying this approach for your project if:You encounter repetitive tasks, prompt templates ensure uniformity and save a lot of time.

Lessons learnedAs w…

TL;DR Evaluating text summarization is difficult because there is no one correct solution, and summarization quality often depends on the summary’s context and purpose.

How does LLM text summarization work?

METEOR is recall-oriented and ensures that the generated text captures as much information from the reference text.

Example: Source Text: {{Document}} Summary: {{Summary}} Evaluation Form (scores ONLY): – Relevance: “””Different prompts for different evaluation criteria are available.

The extensive use of LLMs for text summarization, including the integration of summarization features in search engines, makes research in this field highly popular and relevant.

The demand for observability and the scale of the required infrastructure vary significantly along the LLMOps value chain.

The distributed structure of agentic networks adds another level of complexity, which is not yet addressed fully by LLM observability tools and practices.

The value chain of LLMOpsThe LLMOps value chain starts with training foundation models and subsequent task-specific finetuning.

Each step has different observability needs and requires different scales of observability tooling and infrastructure.

Full talk Observability in LLMOps: Different Levels of Scale Watch on Youtube

Large Language Model (LLM) observability comprises methods for monitoring, tracing, and analyzing an LLM system’s behavior and responses.

Before we discuss the different pillars of LLM observability in detail, let’s clarify how LLM observability relates to LLM monitoring and ML observability.

LLM monitoring vs. LLM observabilityThe difference between LLM monitoring and LLM observability is analogous to the one between traditional monitoring and observability.

LLM observability vs ML observabilityMachine learning (ML) observability is an established practice, so it is natural to ask why LLM applications require a new approach.

LangfuseLangfuse is an open-source LLM observability platform tha…

End-to-end (E2E) MLOps platforms promise to simplify the complicated process of building, deploying, and maintaining ML models in production.

However, while E2E MLOps platforms promise convenience and integration, they may not always align with an organization’s specific needs, existing infrastructure, or long-term goals.

She brings a wealth of experience to the discussion on end-to-end MLOps platforms.

However, she highlighted a core problem:The main challenge of using end-to-end ML platforms for your MLOps stack is that nothing works exactly as you need.

As an avid podcast fan, I’ve also learned much from listening to experienced MLOps engineers share their experiences building platforms …

Defense strategies like adversarial learning, monitoring, defensive distillation, and differential privacy improve robustness against adversarial attacks.

In this article, we’ll review common attack strategies and dive into the latest defense mechanisms for shielding machine learning systems against adversarial attacks.

Regardless of the level of access to the targeted machine learning model, adversarial attacks can be further categorized as:Evasion attacks,Data-poisoning attacks,Byzantine attacks,Model-extraction attacks.

Related Adversarial Attacks on Neural Networks: Exploring the Fast Gradient Sign Method Read moreData-poisoning attacksData-poisoning attacks are another flavor of advers…

A deep dive into the TokenFormer and an opinion about its impact, novelty, and relation to prior work. Paper: https://arxiv.org/abs/2410.23168 Abstract:

Transformers have become the predominant architecture in foundation models due to their excellent performance across various domains. However, the substantial cost of scaling these models remains a significant concern. This problem arises primarily from their dependence on a fixed number of parameters within linear projections. When architectural modifications (e.g., channel dimensions) are introduced, the entire model typically requires retraining from scratch. As model sizes continue growing, this strategy results in increasingly high com…

This paper (by Apple) questions the mathematical reasoning abilities of current LLMs and designs a synthetic template-based dataset distribution to investigate various aspects around LLM performance of high-school level math questions. Paper: https://arxiv.org/abs/2410.05229 Abstract:

Recent advancements in Large Language Models (LLMs) have sparked interest in their formal reasoning capabilities, particularly in mathematics. The GSM8K benchmark is widely used to assess the mathematical reasoning of models on grade-school-level questions. While the performance of LLMs on GSM8K has significantly improved in recent years, it remains unclear whether their mathematical reasoning capabilities hav…

This paper posits the interesting question: How much of the performance of Mamba, S4, and other state-space-like models is actually just attributable to some very core concepts - rather than their elaborate architectures. The authors construct minimal versions of GRUs and LSTMs and report competitive performance. Paper: https://arxiv.org/abs/2410.01201 Abstract:

The scalability limitations of Transformers regarding sequence length have renewed interest in recurrent sequence models that are parallelizable during training. As a result, many novel recurrent architectures, such as S4, Mamba, and Aaren, have been proposed that achieve comparable performance. In this work, we revisit traditional …

How can one best use extra FLOPS at test time? Paper: https://arxiv.org/abs/2408.03314 Abstract:

Enabling LLMs to improve their outputs by using more test-time computation is a critical step towards building generally self-improving agents that can operate on open-ended natural language. In this paper, we study the scaling of inference-time computation in LLMs, with a focus on answering the question: if an LLM is allowed to use a fixed but non-trivial amount of inference-time compute, how much can it improve its performance on a challenging prompt? Answering this question has implications not only on the achievable performance of LLMs, but also on the future of LLM pretraining and how one s…

#llm #privacy #finetuning Can you tamper with a base model in such a way that it will exactly remember its fine-tuning data? This paper presents a method of doing exactly that, and implements it in modern transformers. OUTLINE:

0:00 - Intro & Overview

10:50 -Core idea: single-use data traps

44:30 - Backdoors in transformer models

58:00 - Additional numerical tricks

1:00:35 - Experimental results & conclusion Paper: https://arxiv.org/abs/2404.00473

Code: https://github.com/ShanglunFengatETHZ/PrivacyBackdoor Abstract:

Practitioners commonly download pretrained machine learning models from open repositories and finetune them to fit specific applications. We show that this practice introduces a…

Matrix multiplications (MatMuls) are pervasive throughout modern machine learning architectures. However, they are also very resource intensive and require special accelerators (GPUs). This paper explores architectures that do away with MatMuls and use quantization and recurrence to keep performance up. OUTLINE:

0:00 - Intro

2:30 - MatMul is everywhere

5:55 - Ternary accumulation as a substitute for matrix multiplication

16:35 - Replacing attention layers with recurrent layers

32:40 - Replacing dense layers with ternary channel mixing

38:30 - Language modelling results & scaling laws

45:00 - Other experimental results

48:20 - Conclusion Paper: https://arxiv.org/abs/2406.02528

Code: https://…

#rag #hallucinations #legaltech An in-depth look at a recent Stanford paper examining the degree of hallucinations in various LegalTech tools that incorporate LLMs. OUTLINE:

0:00 - Intro

1:58 - What are legal research tools and how are large language models used by them?

5:30 - Overview and abstract of the paper

9:29 - What is a hallucination and why do they occur?

15:45 - What is retrieval augmented generation (RAG)?

25:00 - Why LLMs are a bad choice when reasoning is involved

29:16 - The products that were tested

32:00 - Some shady practices by the researchers in the back and forth with the legal research companies

37:00 - Legal technology companies’ marketing claims to eliminate or solve…

xLSTM is an architecture that combines the recurrency and constant memory requirement of LSTMs with the large-scale training of transformers and achieves impressive results. Paper: https://arxiv.org/abs/2405.04517 Abstract:

In the 1990s, the constant error carousel and gating were introduced as the central ideas of the Long Short-Term Memory (LSTM). Since then, LSTMs have stood the test of time and contributed to numerous deep learning success stories, in particular they constituted the first Large Language Models (LLMs). However, the advent of the Transformer technology with parallelizable self-attention at its core marked the dawn of a new era, outpacing LSTMs at scale. We now raise a sim…

#gpt4o #sky #scarlettjohansson After the release of their flagship model GPT-4o, OpenAI finds itself in multiple controversies and an exodus of senior personnel - notably Ilya Sutskever References:

https://openai.com/index/gpt-4o-and-more-tools-to-chatgpt-free/

https://openai.com/index/hello-gpt-4o/

https://x.com/LiamFedus/status/1790064963966370209?t=rx2YBT9AdDdKPhI6dUH4zA&s=09

https://x.com/lmsysorg/status/1790097588399779991?t=rx2YBT9AdDdKPhI6dUH4zA&s=09

https://x.com/bindureddy/status/1790127425705120149?t=mMUBqFBRphx-bDuZ1j3mjQ&s=09

https://openai.com/index/improvements-to-data-analysis-in-chatgpt/

https://openai.com/index/openai-and-reddit-partnership/

https://archive.ph/jHlMm

https:/…

Paper: https://arxiv.org/abs/2403.07691 Abstract:

While recent preference alignment algorithms for language models have demonstrated promising results, supervised fine-tuning (SFT) remains imperative for achieving successful convergence. In this paper, we study the crucial role of SFT within the context of preference alignment, emphasizing that a minor penalty for the disfavored generation style is sufficient for preference-aligned SFT. Building on this foundation, we introduce a straightforward and innovative reference model-free monolithic odds ratio preference optimization algorithm, ORPO, eliminating the necessity for an additional preference alignment phase. We demonstrate, both empiri…

OUTLINE:

0:00 - Intro

0:19 - Our next-generation Meta Training and Inference Accelerator

01:39 - ALOHA Unleashed

03:10 - Apple Inks $50M Deal with Shutterstock for AI Training Data

04:28 - OpenAI Researchers, Including Ally of Sutskever, Fired for Alleged Leaking

05:01 - Adobe's Ethical Firefly AI was Trained on Midjourney Images

05:52 - Trudeau announces $2.4billion for AI-related investments

06:48 - RecurrentGemma: Moving Past Transformers for Efficient Open Language Models

07:15 - CodeGemma - an official Google release for code LLMs

07:24 - Mistral AI: Cheaper, Better, Faster, Stronger

08:08 - Vezora/Mistral-22B-v0.1

09:00 - WizardLM-2, next generation state-of-the-art-LLM

09:31 - Idefic…

Paper: https://arxiv.org/abs/2404.09173 Abstract:

While Transformers have revolutionized deep learning, their quadratic attention complexity hinders their ability to process infinitely long inputs. We propose Feedback Attention Memory (FAM), a novel Transformer architecture that leverages a feedback loop to enable the network to attend to its own latent representations. This design fosters the emergence of working memory within the Transformer, allowing it to process indefinitely long sequences. TransformerFAM requires no additional weights, enabling seamless integration with pre-trained models. Our experiments show that TransformerFAM significantly improves Transformer performance on long-…

OUTLINE:

0:00 - Intro

0:21 - Debunking Devin: "First AI Software Engineer" Upwork lie exposed!

07:24 - NeurIPS 2024 will have a track for papers from high schoolers.

13:29 - Opus can operate as a Turing machine.

13:47 - An AI-Powered, Self-Running Propaganda Machine for $105

14:27 - TechScape: How cheap, outsourced labour in Africa is shaping AI English

16:25 - Is ChatGPT Transforming Academics' Writing Style? References:

https://news.ycombinator.com/item?id=40008109&s=09

https://www.youtube.com/watch?v=tNmgmwEtoWE

https://www.youtube.com/watch?v=xE2fxcETP5E

https://twitter.com/itsandrewgao/status/1779369373737668669?t=omW3DvRNmZyce8oo0Ehf1g&s=09

https://twitter.com/0interestrates/status/17…

Google researchers achieve supposedly infinite context attention via compressive memory. Paper: https://arxiv.org/abs/2404.07143 Abstract:

This work introduces an efficient method to scale Transformer-based Large Language Models (LLMs) to infinitely long inputs with bounded memory and computation. A key component in our proposed approach is a new attention technique dubbed Infini-attention. The Infini-attention incorporates a compressive memory into the vanilla attention mechanism and builds in both masked local attention and long-term linear attention mechanisms in a single Transformer block. We demonstrate the effectiveness of our approach on long-context language modeling benchmarks, 1M …

Meta's Llama 3 is out. New model, new license, new opportunities. References:

https://llama.meta.com/llama3/

https://ai.meta.com/blog/meta-llama-3/

https://github.com/meta-llama/llama3/blob/main/MODEL_CARD.md

https://llama.meta.com/trust-and-safety/

https://ai.meta.com/research/publications/cyberseceval-2-a-wide-ranging-cybersecurity-evaluation-suite-for-large-language-models/

https://github.com/meta-llama/llama-recipes/tree/main/recipes/responsible_ai

https://llama.meta.com/llama3/license/

https://about.fb.com/news/2024/04/meta-ai-assistant-built-with-llama-3/?utm_source=twitter&utm_medium=organic_social&utm_content=thread&utm_campaign=imagineflash

https://twitter.com/minchoi/status/178277…

Hey everyone! I am super excited to share a quick notebook on using Generative Feedback Loops to chunk code files and better structure how they are indexed in the Weaviate Vector Database! Chunking is one of the key topics in Vector Search. We need to break up long documents into smaller parts that we can encode with a pre-trained embedding model and index in a vector index, such as HNSW-PQ. Most solutions use some form of a rolling token window such as taking every 300 tokens as a chunk, with say 50 tokens overlapping between each window. Unfortunately, this solution doesn't work that well for code particularly. We don't want the chunk to cut off in the middle of a function or class defini…

Hey everyone! Thanks so much for watching this video exploring Gemini Pro 1.5 and Gemini Flash! Long Context LLMs!! This video covers 3 key tests, the classic "Lost in the Middle" exploration, using Long Context LLMs as Re-rankers in Search, and finally, testing Many-Shot In-Context Learning! I am really excited about the potential of Many-Shot In-Context Learning with DSPy's `BootstrapFewShot` and Gemini, curious to know what you think! Notebook: https://github.com/weaviate/recipes/blob/main/integrations/dspy/llms/Gemini-1.5-Pro-and-Flash.ipynb Gemini 1.5 Technical Report: https://storage.googleapis.com/deepmind-media/gemini/gemini_v1_5_report.pdf Chapters

0:00 Gemini 1.5!!

1:25 Setup and …

Hey everyone! Thank you so much for watching this overview of Llama 3 looking at the release notes and seeing a demo of how to integrate it with DSPy through Ollama and how to use DSPy's MIPRO to find the optimal prompt when using this new large language model for RAG! We are hosting an event in San Francisco on May 1st with Arize AI and Cohere, featuring a talk from Omar Khattab, the lead author of DSPy! Hope to see you there! https://lu.ma/dspy Introducing Meta Llama 3: https://ai.meta.com/blog/meta-llama-3/ Ollama Llama 3: https://ollama.com/library/llama3 Weaviate Recipes: https://github.com/weaviate/recipes/blob/main/integrations/dspy/llms/Llama3.ipynb Chapters

0:00 Llama3!!

1:28 Relea…

Hey everyone! Thank you so much for watching this overview of Command R+ showing you how you can use the new model in DSPy and a quick RAG demo, as well as walking through the details of the release post! Congratulations to the Cohere team! Super exciting times to be working with LLM systems! Introducing Command R+: A Scalable LLM Built for Business - https://txt.cohere.com/command-r-plus-microsoft-azure/ Link to demo notebook - https://github.com/weaviate/recipes/blob/main/integrations/dspy/llms/Command-R-Plus.ipynb Chapters

0:00 Welcome! Command R+!

1:12 Demo with Cohere, DSPy, and Weaviate

6:06 Command R+ Announcement Post

9:24 LLM Evals

Unfortunately, Large Language Models will not consistently follow the instructions that you give them. This is a massive problem when you are building AI systems that require a particular type of output from the previous step to feed into the next one! For example, imagine you are building a blog post writing system that first takes a question and retrieved context to output a list of topics. These topics have to be formatted in a particular way, such as a comma-separated list or a JSON of Topic objects, such that the system can continue writing the blog post! I am SUPER excited to share the 4th video in my DSPy series, diving into 3 solutions to structuring outputs in DSPy programs: (1) **…

Hey everyone! Thank you so much for watching the 3rd edition of the DSPy series, Adding Depth to DSPy Programs!! You can find the examples and links to community resources / news on https://github.com/weaviate/recipes! Chapters

0:00 Intro

0:50 Chapters Overview

5:06 Weaviate Recipes

5:24 DSPy News and Community Notes

13:51 Adding Depth to RAG Programs

18:40 Multi-Model DSPy Programs

20:18 DSPy Optimizers

25:30 Deep Dive Optimizers

27:55 Into the Optimizer Code!

37:48 Demo #1: Adding Depth to RAG

1:05:25 Demo #2: Questions to Blogs

1:07:48 Thank you so much for watching!

The full video dives deeper into the field of math studying this called holomorphic dynamics: https://youtu.be/LqbZpur38nw

From this 7-minute LLM explainer: https://youtu.be/LPZh9BOjkQs

Dig deeper here: https://www.youtube.com/playlist?list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi

Technical details as a talk: https://youtu.be/KJtZARuO3JY

Made for an exhibit at the Computer History Museum: https://computerhistory.org/

Instead of sponsored ad reads, these lessons are funded directly by viewers: https://3b1b.co/support Timestamps:

0:00 - Who this was made for

0:41 - What are large language models?

7:48 - Where to learn more No secret end-screen vlog for this one, the end-screen real estate was all full! ------------------ These animations are largely made using a custom Python library, manim. See the FAQ comments here:

https://3b1b.co/faq#manim

https://github.com/3b1b/manim

https:/…

From this full video: https://youtu.be/piJkuavhV50

Full video: https://youtu.be/GNcFjFmqEc8

Full video: https://youtu.be/-RdOwhmqP5s

The referenced stack exchange post by usename 2'5 9'2, http://math.stackexchange.com/questions/30046/alternative-notation-for-exponents-logs-and-roots

Full video: https://youtu.be/piJkuavhV50

I learned this from Matt Parker, who then made a full video: https://youtu.be/ga9Qk38FaHM

A series of delightful geometry puzzlesBonus video with extra puzzles: https://www.patreon.com/posts/115570453

The artwork at the end is by Kurt Bruns

Thanks to Daniel Kim for sharing the first two puzzles with me.

The idea to include the tetrahedron volume example was based on a conversation with Po Shen Lo about these puzzles, during which he mentioned the case of one dimension lower.

I received the cone correction to the proof of Monge's theorem via Akos Zahorsky. If any of you know the original source, please let me know! I referenced quaternions at the end, and if you're curious to learn more, here are a few options. This video walks through concretely what the computation is for using…

Full video: https://youtu.be/wjZofJX0v4M

A behind-the-scenes look at how I animate videos.

Code for all the videos: https://github.com/3b1b/videos

Manim: https://github.com/3b1b/manim

Community edition: https://github.com/ManimCommunity/manim/

Example scenes shown near the end: https://github.com/3b1b/manim/blob/master/example_scenes.py I added some more details about the workflow shown in this video to the readme of the videos repo: https://github.com/3b1b/videos?tab=readme-ov-file#workflow These lessons are funded directly by viewers: https://3b1b.co/support Timestamp:

0:00 - Intro

2:39 - Hello World

10:32 - Coding up a Lorenz attractor

23:46 - Add some tracking points

28:52 - The globals().update(locals()) hack

32:57 - Final st…

Full video: https://youtu.be/EmKQsSDlaa4

Full video: https://youtu.be/EmKQsSDlaa4

3d scenes on 2d film, and a diffraction lesson along the way.

Instead of sponsored ad reads, these lessons are funded directly by viewers: https://3b1b.co/support

An equally valuable form of support is to share the videos. Gabor's Nobel Prize lecture:

https://www.nobelprize.org/uploads/2018/06/gabor-lecture.pdf A few resources we found helpful for this video

Seeing the Light, by Falk, Brill, and Stork

https://amzn.to/3Ngdiqh Practical Holography, by Saxby and Zarcharovas

https://amzn.to/3ZR2MNN Principles of Holography by Howard Smith

https://amzn.to/3ZOihFZ Timestamps

0:00 - What is a Hologram?

3:28 - The recording process

11:45 - The simplest hologram

17:12 - Diffraction gratings

25:15 - …

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambdalabs.com/papers 📝 The paper "Scintilla: Simulating Combustible Vegetation for Wildfires" is available here:

https://storage.googleapis.com/pirk.io/projects/scintilla/index.html 📝 My paper on simulations that look almost like reality is available for free here:

https://rdcu.be/cWPfD Or this is the orig. Nature Physics link with clickable citations:

https://www.nature.com/articles/s41567-022-01788-5 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

Alex Balfanz, Alex Haro, B Shang, Benji Rabhan, Gaston Ingaramo, Gordon Child, John Le, Juan Benet, Kyle Davis, Loyal Alchemist, Luk…

❤️ Check out Weights & Biases and sign up for a free demo here: https://wandb.me/papersllm 📝 The blog post and paper are available here:

https://blogs.nvidia.com/blog/fugatto-gen-ai-sound-model/

https://d1qx31qr3h6wln.cloudfront.net/publications/FUGATTO.pdf 📝 My paper on simulations that look almost like reality is available for free here:

https://rdcu.be/cWPfD Or this is the orig. Nature Physics link with clickable citations:

https://www.nature.com/articles/s41567-022-01788-5 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

Alex Balfanz, Alex Haro, B Shang, Benji Rabhan, Gaston Ingaramo, Gordon Child, John Le, Juan Benet, Kyle Davis, Loyal Alche…

❤️ Try Macro for free and supercharge your learning: https://macro.com/papers 📝 Blender 4.3 is available here:

https://www.blender.org/download/releases/4-3/ 📝 My procedural brush synthesis paper: https://users.cg.tuwien.ac.at/zsolnai/gfx/procedural-brush-synthesis-paper/ 📝 Showcased SLIM paper: https://igl.ethz.ch/projects/slim/ 📝 My paper on simulations that look almost like reality is available for free here:

https://rdcu.be/cWPfD Or this is the orig. Nature Physics link with clickable citations:

https://www.nature.com/articles/s41567-022-01788-5 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

Alex Balfanz, Alex Haro, B Shang, Benji Rabhan, G…

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambdalabs.com/papers 📝 Unreal Engine 5.5 is available here:

https://www.unrealengine.com/en-US/blog/unreal-engine-5-5-is-now-available 📝 My paper on simulations that look almost like reality is available for free here:

https://rdcu.be/cWPfD Or this is the orig. Nature Physics link with clickable citations:

https://www.nature.com/articles/s41567-022-01788-5 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

Alex Balfanz, Alex Haro, B Shang, Benji Rabhan, Gaston Ingaramo, Gordon Child, John Le, Juan Benet, Kyle Davis, Loyal Alchemist, Lukas Biewald, Martin, Michael Albrecht, Michael T…

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambdalabs.com/papers Oasis: A Universe in a Transformer - try it out now:

https://oasis.decart.ai/welcome More info:

https://oasis-model.github.io/ 📝 My paper on simulations that look almost like reality is available for free here:

https://rdcu.be/cWPfD Or this is the orig. Nature Physics link with clickable citations:

https://www.nature.com/articles/s41567-022-01788-5 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

Alex Balfanz, Alex Haro, B Shang, Benji Rabhan, Gaston Ingaramo, Gordon Child, John Le, Juan Benet, Kyle Davis, Loyal Alchemist, Lukas Biewald, Martin, Michael Albrec…

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambdalabs.com/papers 📝 The paper is available here:

https://openai.com/index/introducing-simpleqa/ 📝 My paper on simulations that look almost like reality is available for free here:

https://rdcu.be/cWPfD Or this is the orig. Nature Physics link with clickable citations:

https://www.nature.com/articles/s41567-022-01788-5 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

Alex Balfanz, Alex Haro, B Shang, Benji Rabhan, Gaston Ingaramo, Gordon Child, John Le, Juan Benet, Kyle Davis, Loyal Alchemist, Lukas Biewald, Martin, Michael Albrecht, Michael Tedder, Owen Skarpness, Richard Sundv…

❤️ Try Macro for free and supercharge your learning: https://macro.com/papers 📝 The paper "3D Gaussian Ray Tracing: Fast Tracing of Particle Scenes" is available here:

https://gaussiantracer.github.io/ 📝 My paper on simulations that look almost like reality is available for free here:

https://rdcu.be/cWPfD Or this is the orig. Nature Physics link with clickable citations:

https://www.nature.com/articles/s41567-022-01788-5 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

Alex Balfanz, Alex Haro, B Shang, Benji Rabhan, Gaston Ingaramo, Gordon Child, John Le, Juan Benet, Kyle Davis, Loyal Alchemist, Lukas Biewald, Martin, Michael Albrecht, Michael T…

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambdalabs.com/papers

📝 The paper is available here:

https://openai.com/index/simplifying-stabilizing-and-scaling-continuous-time-consistency-models/ 📝 My paper on simulations that look almost like reality is available for free here:

https://rdcu.be/cWPfD Or this is the orig. Nature Physics link with clickable citations:

https://www.nature.com/articles/s41567-022-01788-5 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

Alex Balfanz, Alex Haro, B Shang, Benji Rabhan, Gaston Ingaramo, Gordon Child, John Le, Juan Benet, Kyle Davis, Loyal Alchemist, Lukas Biewald, Martin, Michael Albre…

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambdalabs.com/papers 📝Apple reasoning paper: https://arxiv.org/pdf/2410.05229 📝 My paper on simulations that look almost like reality is available for free here:

https://rdcu.be/cWPfD Or this is the orig. Nature Physics link with clickable citations:

https://www.nature.com/articles/s41567-022-01788-5 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

Alex Balfanz, Alex Haro, B Shang, Benji Rabhan, Gaston Ingaramo, Gordon Child, John Le, Juan Benet, Kyle Davis, Loyal Alchemist, Lukas Biewald, Martin, Michael Albrecht, Michael Tedder, Owen Skarpness, Richard Sundvall, Taras Bobrovytsk…

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambdalabs.com/papers Guide on how I use Lambda to make images: https://docs.lambdalabs.com/on-demand-cloud/how-to-serve-the-flux.1-prompt-to-image-models-using-lambda-cloud-on-demand-instances 📝 The paper "Interactive Character Control with Auto-Regressive Motion Diffusion Models" is available here:

https://xbpeng.github.io/projects/AMDM/index.html

https://yi-shi94.github.io/amdm_page/ 📝 My paper on simulations that look almost like reality is available for free here:

https://rdcu.be/cWPfD Or this is the orig. Nature Physics link with clickable citations:

https://www.nature.com/articles/s41567-022-01788-5 🙏 We would like to …

❤️ Check out Weights & Biases and sign up for a free demo here: https://wandb.me/papersllm 📝 The paper is available here:

https://visualcomputing.ist.ac.at/publications/2024/PDNSF/ 📝 My paper on simulations that look almost like reality is available for free here:

https://rdcu.be/cWPfD Or this is the orig. Nature Physics link with clickable citations:

https://www.nature.com/articles/s41567-022-01788-5 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

Alex Balfanz, Alex Haro, B Shang, Benji Rabhan, Gaston Ingaramo, Gordon Child, John Le, Juan Benet, Kyle Davis, Loyal Alchemist, Lukas Biewald, Martin, Michael Albrecht, Michael Tedder, Owen Skarpness…

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambdalabs.com/papers 📝 The papers are available here:

https://allenai.github.io/discoveryworld/

https://www.youtube.com/watch?v=hcBFhJKdAvk

https://cg.informatik.uni-freiburg.de/publications/2023_CGF_monolithic_contact_friction.pdf 📝 My paper on simulations that look almost like reality is available for free here:

https://rdcu.be/cWPfD Or this is the orig. Nature Physics link with clickable citations:

https://www.nature.com/articles/s41567-022-01788-5 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

Alex Balfanz, Alex Haro, B Shang, Benji Rabhan, Gaston Ingaramo, Gordon Child, Joh…

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambdalabs.com/papers 📝 The paper "fVDB: A Deep-Learning Framework for Sparse, Large-Scale, and High-Performance Spatial Intelligence" is available here:

https://research.nvidia.com/labs/prl/publication/williams2024fvdb/

https://developer.nvidia.com/fvdb

https://blogs.nvidia.com/blog/fvdb-bigger-digital-models/ LLama 3.2: https://ai.meta.com/blog/llama-3-2-connect-2024-vision-edge-mobile-devices/

Gaussian Haircut paper: https://eth-ait.github.io/GaussianHaircut/ NVLM vision model:

https://research.nvidia.com/labs/adlr/NVLM-1/

https://huggingface.co/nvidia/NVLM-D-72B 📝 My paper on simulations that look almost like reality is a…

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambdalabs.com/papers Unreal Engine + 5.5 preview:

https://www.unrealengine.com/en-US

https://forums.unrealengine.com/t/unreal-engine-5-5-preview/2048423 📝 My paper on simulations that look almost like reality is available for free here:

https://rdcu.be/cWPfD Or this is the orig. Nature Physics link with clickable citations:

https://www.nature.com/articles/s41567-022-01788-5 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

Alex Balfanz, Alex Haro, B Shang, Benji Rabhan, Gaston Ingaramo, Gordon Child, John Le, Juan Benet, Kyle Davis, Loyal Alchemist, Lukas Biewald, Martin, Michael A…

Mikita Shchutski | Lead Machine Learning Engineer, Quantori Training large medical models using electronic health records in order to create a highly informative medical embedding space

02:00-05:38 What do you think were the most outstanding underestimated news and achievements in AI field in 2020?

05:41-11:28 What do you think about trends in ML like transformers trying to replace LSTMs in NLP?

11:29-16:06 Are you working on any new types of models right now?

16:07-20:41 What is your opinion on the most underestimated ML subfield like Reinforcement Learning?

20:42-22:17 Your best recommendation for our community is to look into AI in the real physical world, right?

22:18-33:10 Do you think it is possible to achieve great results in creative AI, particularly in subjective beauty?

33:17-35:50 What prevents chat bots from reaching more intelligent levels?

36:03-39:39 What is…

DataFest Online 2020

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Это доклад Степана Комкова, старшего разработчика службы синтеза речи в Яндекс Поиске, на Practical ML Conf 2024. В своём выступлении Степан рассказал об опыте создания виртуального рассказчика в Букмейте. Зачем его создавали и чего хотят пользователи, как выжать максимум из технологий уходящего поколения и внедрить длинный контекст в low-resource real-time модель. И как GPT и диффузионные модели произвели революцию в синтезе речи. Подписывайтесь на телеграм-канал Яндекса для ML-специалистов: https://t.me/yandexforml

Это фрагмент из доклада Виктора Плошихина, руководителя ML-лаборатории в Yandex Platform Engineering. В своём выступлении на Practical ML Conf 2024 Виктор рассказал, как команда создавала AI-ассистента для разработчиков. Как дообучали модели на реальном коде, почему решили предсказывать именно стейтменты, а ещё какие метрики и способы оценки качества продукта разработали. Ищите доклад целиком на нашем канале.

Это доклад руководителя управления качества в Яндекс Поиске Кати Серажим на Practical ML Conf 2024. В своём выступлении Катя рассказала об архитектуре и процессе обучения LLM для больших продуктовых проектов Яндекса. Из доклада вы узнаете, что находится под капотом у Нейро и какие сложности решала команда на каждом из этапов его создания. Подписывайтесь на телеграм-канал Яндекса для ML-специалистов: https://t.me/yandexforml

На видео — CTO Яндекс Поиска Алексей Гусаков и его вступительное слово на Practical ML Conf 2024. Это хардовая конференция Яндекса для экспертов в машинном обучении. Внутри: качественные технические доклады от ключевых инженеров, максимум пользы и знаний о практическом применении ML. Смотрите дальше и узнаете, как уже сейчас использовать ML с пользой для бизнеса. Подписывайтесь на телеграм-канал Яндекса для ML-специалистов: https://t.me/yandexforml

Это доклад Георгия Морозова, ведущего аналитика рисков в Т-Банке, на Practical ML Conf 2024. В своём выступлении Георгий рассказал, как в Т-Банке оценили влияние страховых продуктов на другие части экосистемы с помощью методов причинно-следственного анализа. Подписывайтесь на телеграм-канал Яндекса для ML-специалистов: https://t.me/yandexforml

Это доклад Виктора Плошихина, руководителя ML-лаборатории в Yandex Platform Engineering, на Practical ML Conf 2024. В своём выступлении Виктор рассказал, как команда создавала AI-ассистента для разработчиков. Как дообучали модели на реальном коде, почему решили предсказывать именно стейтменты, какие метрики и способы оценки качества разработали. Подписывайтесь на телеграм-канал Яндекса для ML-специалистов: https://t.me/yandexforml

Это фрагмент из доклада Виктора Плошихина, руководителя ML-лаборатории в Yandex Platform Engineering. В своём выступлении на Practical ML Conf 2024 Виктор рассказал, как команда создавала AI-ассистента для разработчиков. Как дообучали модели на реальном коде, почему решили предсказывать именно стейтменты, а ещё какие метрики и способы оценки качества продукта разработали. Ищите доклад целиком на нашем канале.

Это фрагмент из доклада Виктора Плошихина, руководителя ML-лаборатории в Yandex Platform Engineering. В своём выступлении на Practical ML Conf 2024 Виктор рассказал, как команда создавала AI-ассистента для разработчиков. Как дообучали модели на реальном коде, почему решили предсказывать именно стейтменты, а ещё какие метрики и способы оценки качества продукта разработали. Ищите доклад целиком на нашем канале.

Это фрагмент из доклада Виктора Плошихина, руководителя ML-лаборатории в Yandex Platform Engineering. В своём выступлении на Practical ML Conf 2024 Виктор рассказал, как команда создавала AI-ассистента для разработчиков. Как дообучали модели на реальном коде, почему решили предсказывать именно стейтменты, а ещё какие метрики и способы оценки качества продукта разработали. Ищите доклад целиком на нашем канале.

Это фрагмент выступления руководителя управления качества в Яндекс Поиске Кати Серажим и CTO Яндекс Поиска Алексея Гусакова. На Practical ML Conf 2024 Катя рассказала об архитектуре и процессе обучения LLM для больших продуктовых проектов Яндекса и вместе с Алексеем ответила на вопросы из зала. Посмотреть доклад целиком можно на нашем канале. Из него вы узнаете, что находится под капотом у Нейро и какие сложности решала команда на каждом из этапов его создания.

Это фрагмент доклада Степана Комкова, старшего разработчика службы синтеза речи в Яндекс Поиске. На Practical ML Conf 2024 Степан рассказал об опыте создания виртуального рассказчика в Яндекс Книгах. Зачем его создавали и чего хотят пользователи, как выжать максимум из технологий уходящего поколения и внедрить длинный контекст в low-resource-real-time-модель. И как GPT и диффузионные модели произвели революцию в синтезе речи. Смотрите доклад целиком на нашем канале.

Это фрагмент доклада Степана Комкова, старшего разработчика службы синтеза речи в Яндекс Поиске. На Practical ML Conf 2024 Степан рассказал об опыте создания виртуального рассказчика в Яндекс Книгах. Зачем его создавали и чего хотят пользователи, как выжать максимум из технологий уходящего поколения и внедрить длинный контекст в low-resource-real-time-модель. И как GPT и диффузионные модели произвели революцию в синтезе речи. Смотрите доклад целиком на нашем канале.

Это доклад с ML Party в Белграде. Его прочитал Антон Клочков, руководитель подгруппы распознавания текста в VLM. Антон рассказал, как в визуальных языковых моделях развивают навыки распознавания символов на картинке. И показал кейсы, где это используется (кроме расшифровки мемов). Больше интересных материалов по ML ищите тут: https://t.me/yandexforml

Это доклад с ML Party в Белграде. Его прочитал Дмитрий Солодуха, руководитель команды Алисы и умных устройств Яндекса. Дмитрий рассказал, как они с ребятами учат умные устройства активироваться по интонации и быстрым командам, чтобы пользователю нужно было меньше «алискать». Больше интересных материалов по ML ищите тут: https://t.me/yandexforml

Это доклад с ML Party в Белграде. С ним выступил Илья Ирхин, руководитель аналитики Яндекс Еды. Илья показал, как решается задача ранжирования ресторанов-партнёров при продвижении в выдаче сервиса. Больше интересных материалов по ML ищите тут: https://t.me/yandexforml

Спикер: Сиракан Багдасарян, MLOps-инженер, OZON Банк Data Halloween 2024: https://ods.ai/events/halloween2024_spb

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Спикер: Алексей Козлов, Евгений Тайчинов, ML-инженер, Работа.ру Data Halloween 2024: https://ods.ai/events/halloween2024_spb

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Спикер: Марк Паненко, CDS, OZON Банк Data Halloween 2024: https://ods.ai/events/halloween2024_spb

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Data Fest Siberia 5: https://ods.ai/events/datafestsiberia5

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Спикер: Маршалова Аня, Тихобаева Оля

Название: Вспомнить всё по короткой подсказке разбор статьи Rethinking LLM Memorization through the Lens of Adversarial Compression Data Fest Siberia 5: https://ods.ai/events/datafestsiberia5

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Спикер: Марина Платонова

Название: Новые горизонты лесной отрасли: применение ML для распознавания пород и состояния деревьев Data Fest Siberia 5: https://ods.ai/events/datafestsiberia5

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Спикер: Мазине Джулия

Название: Сможет ли ChatGPT получить диплом? Оценка уязвимости высшего образования к ИИ-ассистентам Data Fest Siberia 5: https://ods.ai/events/datafestsiberia5

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Спикер: Евгений Павловский Data Fest Siberia 5: https://ods.ai/events/datafestsiberia5

Трек ML и бизнес: https://ods.ai/tracks/sibfest5-ml-business

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Patreon: https://www.patreon.com/primerlearning Source and further reading on the common side-blotched lizard:

Sinervo, B.; C.M. Lively (1996). "The rock–paper–scissors game and the evolution of alternative male strategies". Nature. 380 (6571): 240–243.

https://en.wikipedia.org/wiki/Common_side-blotched_lizard Made with Godot

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Javier Milei is the President of Argentina.

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Dario Amodei is the CEO of Anthropic, the company that created Claude.

Amanda Askell is an AI researcher working on Claude’s character and personality.

Chris Olah is an AI researcher working on mechanistic interpretability.

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(3:49:02) – Character training(3:50:01) – Nature of truth(3:54:38) – Optimal rate of failure(4:01:49) – AI consciousness(4:16:20) – AGI(4:24:58) – Chris Olah – Mechanistic Interpretability(4:29:49) – Features, Circuits, Universality(4:47:23) – Superposition(4:58:22) – Monosemanticity(5:05…

Rick Spence is a historian specializing in the history of intelligence agencies, espionage, secret societies, conspiracies, the occult, and military history.

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Go to https://drinkLMNT.com/lexOUTLINE:(00:00) – Introduction(08:51) – MLK Jr(11:43) – Corruption in politics(23:00) – Healthcare in US(31:33) – 2016 election(37:32) – Barack Obama(43:26) – Capitalism(51:35) – Response to attacks(56:32) – AOC and progressive politics(1:04:24) – Mortality(1:06:30) – Hope fo…

Graham Hancock a journalist and author who for over 30 years has explored the controversial possibility that there existed a lost civilization during the last Ice Age, and that it was destroyed in a global cataclysm some 12,000 years ago.

He is the presenter of the Netflix documentary series “Ancient Apocalypse”, the 2nd season of which has just been released.

Thank you for listening ❤ Check out our sponsors: https://lexfridman.com/sponsors/ep449-scSee below for timestamps, and to give feedback, submit questions, contact Lex, etc.

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Go to https://creators.riverside.fm/LEXLMNT: Zero-sugar electro…

Jordan Peterson is a psychologist, author, lecturer, and podcast host.

Thank you for listening ❤ Check out our sponsors: https://lexfridman.com/sponsors/ep448-scSee below for timestamps, transcript, and to give feedback, submit questions, contact Lex, etc.

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Go to https://drinkLMNT.com/lexOUTLINE:(00:00) – Introduction(07:07) – Nietzsche(14:48) – Power and propaganda(19:54) – Nazism(24:54) – Religion(41:18) – Communism(47:03) – Hero myth(49:12) – Belief in God(59:24) – Advice for young people(1:12:02) – Sex(1:32:00) – Good and evil(1:44:46) – Psychopathy(1…

Aman Sanger, Arvid Lunnemark, Michael Truell, and Sualeh Asif are creators of Cursor, a popular code editor that specializes in AI-assisted programming.

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Ed Barnhart is an archaeologist and explorer specializing in ancient civilizations of the Americas.

He is the Director of the Maya Exploration Center, host of the ArchaeoEd Podcast, and lecturer on the ancient history of North, Central, and South America.

Ed is in part known for his groundbreaking work on ancient astronomy, mathematics, and calendar systems.

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Vivek Ramaswamy is a conservative politician, entrepreneur, and author of many books on politics, including his latest titled Truths: The Future of America First.

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Go to https://eightsleep.com/lexOUTLINE:(00:00) – Introduction(12:50) – Conservatism(16:06) – Progressivism(21:41) – DEI(26:33) – Bureaucracy(33:25) – Government efficiency(48:34) – Education(1:02:59) – Military Industrial Complex(1:25:18) – Illegal immigration(1:46:53) – Donald Trump(…

Vejas Liulevicius is a historian specializing in Germany and Eastern Europe, who has lectured extensively on Marxism and the rise, the reign, and the fall of Communism.

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Gregory Aldrete is a historian specializing in ancient Rome and military history.

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Donald Trump is the 45th President of the United States and the Republican candidate in the 2024 US Presidential Election.

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Cenk Uygur is a progressive political commentator and host of The Young Turks.

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Pieter Levels (aka levelsio on X) is a self-taught developer and entrepreneur who has designed, programmed, launched over 40 startups, many of which are highly successful.

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The other one actually is some token actually is very, very hard to be predicted during the pretraining.

And the important thing for the data is about data filtering.

If we’re able to build a better model actually is able to benefit so many different kinds of application.

And definitely there’s a lot of things about how to build a better data [that] is unsolved yet in the literature.

And the other thing actually, we are working on something that’s very exciting.

The drawback of this approach is that it often misses the cultural nuances of local languages.

CHITALE: Now that we have created a benchmark, the next step is to evaluate how these multimodal models are performing on this benchmark.

So what we observed is there is a huge gap when it comes … in performance when we compare these proprietary offerings versus the open-source models.

These open-source models significantly lag behind the proprietary models.

CHITALE: CVQA is significant because it addresses a fundamental gap in how we evaluate vision-language and multimodal models today.

FOSTER: So this is a, kind of, a theoretical work on reinforcement learning, or RL.

FOSTER: Yeah, so if you look at these sort of RL problems with latent dynamics, this is something that’s actually received a lot of investigation in theory.

Like, can we take existing algorithms and use them to solve rich-observation RL problems in a modular fashion?

TINGLE: Dylan, I’d like to know—and I’m sure our audience would, too—what this work means when it comes to real-world application.

TINGLE: Well, Dylan Foster, thank you for joining us today to discuss your paper on reinforcement learning under latent dynamics.

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Joining me today to discuss the state of DNA data storage and some of our contributions are several members of the DNA Data Storage Project at Microsoft Research: Principal Researcher Bichlien Nguyen, Senior Researcher Jake Smith, and Partner Research Manager Sergey Yekhanin.

Once we do this, we return to our original data and we’ve completed, let’s call it, one DNA data storage cycle.

So, like, I mean, coding is an important aspect of this whole idea of DNA data storage because we have to deal with errors—it’s a new medium—but talking about error-correcting codes in the context of DNA data storage, so, I mean, usually, like … what are error-correcting codes about?

In DNA data storage, the …

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Researcher Siddharth Suri and professor David Holtz give a brief history of prompt engineering, discuss the debate behind their recent collaboration, and share what they found from studying how people’s approaches to prompting change as models advance.

Learn more:

Chris and Jay, thank you for joining us today for Abstracts and congratulations!

HAWBLITZEL: Yeah, so I think, you know, traditionally verification or this formal software verification that we’re doing has been considered a little bit of a pie-in-the-sky research agenda.

They’ve discovered that you can get the high performance that you want for systems code without having to sacrifice the ability to reason about ownership and lifetimes, concurrency.

TINGLE: Well, finally, Chris, what are some of the open questions or future opportunities for formal software verification research, and what might you and your collaborators tackle next?

I’m Amber Tingle, and we hope you’ll join us again for Ab…

And at that time, large language model was really in its infancy and people just started exploring what large language model can help us in terms of improving software reliability.

But of course, you know, large language model is a field that is moving so fast.

And that’s one of the reasons we used large language models, because traditional static analysis or traditional program analysis cannot capture this.

I’m actually working on how to leverage large language model to verify the correctness of code, code that may be generated by large language model itself.

STOICA: So we’re thinking of, as Shan mentioned, exploring what large language models can do in this bug-finding/testing arena furth…

Today, I’m speaking with my intern, Angela Busheska, about her work this summer and her experience as a Microsoft Research intern.

And I think I’m really grateful that we took this direction because we had a chance to understand at a granular level what is actually happening.

RANGANATHAN: And I think we realized this and you can chime in, Angela, but I feel that the traceability is a vehicle for data.

BUSHESKA: Yeah, so this will be a fall really spent with a lot of applications because of, like, graduate school.

As all the listeners can probably see, I’m a person who really speaks a lot, even more than needed sometimes.

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[MUSIC FADES]Today I’m talking to Dr. Richard Black, a senior principal research manager and the research director of Project Silica at Microsoft Research.

Richard and Dexter are involved in a unique multidisciplinary, multi-institutional, and multigenerational collaboration called Avenues Golden Record, a current effort to communicate with extraterrestrial intelligence.

So the project we’re talking about today is Avenues Golden Record, but it’s not the first Golden Record to exist.

GREENE: And I guess moving on to future attempts … so what we’re doing, my team, is we’re working on creating an updated Golden Record.

HUIZINGA: Yeah, yeah.

Model maker and fabricator Lex Story helps bring research to life through prototyping.

He discusses his take on failure; the encouragement and advice that has supported his pursuit of art and science; and the sabbatical that might inspire his next career move.

Learn more:

In this episode, Microsoft Product Manager Shrey Jain and OpenAI Research Scientist Zoë Hitzig join host Amber Tingle to discuss “Personhood credentials: Artificial intelligence and the value of privacy-preserving tools to distinguish who is real online.” In their paper, Jain, Hitzig, and their coauthors describe how malicious actors can draw on increasingly advanced AI tools to carry out deception, making online deception harder to detect and more harmful.

Bringing ideas from cryptography into AI policy conversations, they identify a possible mitigation: a credential that allows its holder to prove they’re a person––not a bot––without sharing any identifying information.

This exploratory r…

Brendan and Mert are exploring the economic impact of job automation and generative AI as part of Microsoft’s AI, Cognition, and the Economy, or AICE, research initiative.

And more recently, my research focuses on AI, both, like, the adoption of AI and the productivity impact of AI.

HUIZINGA: Right, right.

Like, we need to predict what will be the effect of AI, how AI is going to affect the economy, especially in the long run.

HUIZINGA: Right, right.

Curious about the safety of LLMs?

🤔 Join us for an insightful new episode featuring Suchin Gururangan, Young Investigator at Allen Institute for Artificial Intelligence and Data Science Engineer at Appuri.

🚀 Don't miss…

In this episode, the data scientist Wentao Su shares his experience in AB testing on social media platforms like LinkedIn and TikTok. We talk about how network science can enhance AB testing by accounting for complex social interactions, especially in environments where users are both viewers and content creators. These interactions might cause a "spillover effect" meaning a possible influence across experimental groups, which can distort results. To mitigate this effect, our guest presents heuristics and algorithms they developed ("one-degree label propagation”) to allow for good results on big data with minimal running time and so optimize user experience and advertiser performance in soc…

Alex Bisberg, a PhD candidate at the University of Southern California, specializes in network science and game analytics, with a focus on understanding social and competitive success in multiplayer online games. In this episode, listeners can expect to learn from a network perspective about players interactions and patterns of behavior. Through his research on games, Alex sheds light on how network analysis and statistical tests might explain positive contagious behaviors, such as generosity, and explore the dynamics of collaboration and competition in gaming environments. These insights offer valuable lessons not only for game developers in enhancing player experience, engagement and rete…

In this episode we discuss the GitHub Collaboration Network with Behnaz Moradi-Jamei, assistant professor at James Madison University. As a network scientist, Behnaz created and analyzed a network of about 700,000 contributors to Github's repository. The network of collaborators on GitHub was created by identifying developers (nodes) and linking them with edges based on shared contributions to the same repositories. This means that if two developers contributed to the same project, an edge (connection) was formed between them, representing a collaborative relationship network consisting of 32 million such connections. By using algorithms for Community Detection, Behnaz's analysis reveals in…

In this episode we discuss the GitHub Collaboration Network with Behnaz Moradi-Jamei, assistant professor at James Madison University. As a network scientist, Behnaz created and analyzed a network of about 700,000 contributors to Github's repository. The network of collaborators on GitHub was created by identifying developers (nodes) and linking them with edges based on shared contributions to the same repositories. This means that if two developers contributed to the same project, an edge (connection) was formed between them, representing a collaborative relationship network consisting of 32 million such connections. By using algorithms for Community Detection, Behnaz's analysis reveals in…

We are joined by Abhishek Paudel, a PhD Student at George Mason University with a research focus on robotics, machine learning, and planning under uncertainty, using graph-based methods to enhance robot behavior. He explains how graph-based approaches can model environments, capture spatial relationships, and provide a framework for integrating multiple levels of planning and decision-making.

We are joined by Maciej Besta, a senior researcher of sparse graph computations and large language models at the Scalable Parallel Computing Lab (SPCL). In this episode, we explore the intersection of graph theory and high-performance computing (HPC), Graph Neural Networks (GNNs) and LLMs.

In this episode, we sit down with Yuanyuan Tian, a principal scientist manager at Microsoft Gray Systems Lab, to discuss the evolving role of graph databases in various industries such as fraud detection in finance and insurance, security, healthcare, and supply chain optimization.

Our new season "Graphs and Networks" begins here! We are joined by new co-host Asaf Shapira, a network analysis consultant and the podcaster of NETfrix – the network science podcast. Kyle and Asaf discuss ideas to cover in the season and explore Asaf's work in the field.

Join us for our capstone episode on the Animal Intelligence season. We recap what we loved, what we learned, and things we wish we had gotten to spend more time on. This is a great episode to see how the podcast is produced. Now that the season is ending, our current co-host, Becky, is moving to emeritus status. In this last installment we got to spend a little more time getting to know Becky and where her work will take her after this. Did Data Skeptic inspire her to learn more about machine learning? Tune in and find out.

David Obembe, a recent University of Tartu graduate, discussed his Masters thesis on integrating LLMs with process mining tools. He explained how process mining uses event logs to create maps that identify inefficiencies in business processes. David shared his research on LLMs' potential to enhance process mining, including experiments evaluating their performance and future improvements using Retrieval Augmented Generation (RAG).

Our guest today is Risa Shinoda, a PhD student at Kyoto University Agricultural Systems Engineering Lab, where she applies computer vision techniques. She talked about the OpenAnimalTracks dataset and what it was used for. The dataset helps researchers predict animal footprint. She also discussed how she built a model for predicting tracks of animals. She shared the algorithms used and the accuracy they achieved. She also discussed further improvement opportunities for the model.

This episode features an interview with Mélisande Teng, a PhD candidate at Université de Montréal. Her research lies in the intersection of remote sensing and computer vision for biodiversity monitoring.

In this interview with author Deborah Gordon, Kyle asks questions about the mechanisms at work in an ant colony and what ants might teach us about how to build artificial intelligence. Ants are surprisingly adaptive creatures whose behavior emerges from their complex interactions. Aspects of network theory and the statistical nature of ant behavior are just some of the interesting details you'll get in this episode.

This season it’s become clear that computing skills are vital for working in the natural sciences. In this episode, we were fortunate to speak with Madlen Wilmes, co-author of the book "Computing Skills for Biologists: A Toolbox". We discussed the book and why it’s a great resource for students and teachers. In addition to the book, Madlen shared her experience and advice on transitioning from academia to an industry career and how data analytic skills transfer to jobs that your professionals might not always consider. Join us and learn more about the book and careers using transferable skills.

In this episode, we talked shop with Hager Radi about her biodiversity monitoring work. While biodiversity modeling may sound simple, count organisms and mark their location, there is a lot more to it than that! Incomplete and biased data can make estimations hard. There are also many species with very few observations in the wild. Using machine learning and remote sensing data, scientists can build models that predict species distributions with limited data. Listen in and hear about Hager’s work tackling these challenges and the tools she has built.

In this Five-Minute Friday, Jon interviews Chris Bennett and Joseph Balsamo on the importance of flexibility in the way we deploy AI models, Dell’s brand positioning in the AI space, and whether GenAI’s business applications stand up to the hype. Additional materials: www.superdatascience.com/842 Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information.

Andrew Ng 00:10:12You know, that's interesting question.

Can you elaborate on why planning, using multiple tools, and code generation are so crucial for building effective vision AI applications?

And I see, I actually think there's a lot of media companies with a lot of, well, this is raising my hand, right?

If I click somewhere else, you know, there's no gray wolf, right, elsewhere.

Yeah, that's, that's very cool, Andrew to see.

What do AI, robotics, and premium wine grapes have in common? Everything, as it turns out. In this episode, we explore viticultural robotics a revolutionary project combining machine learning, spectroscopic sensors, and VR-controlled robotics to tackle one of agriculture’s trickiest challenges: harvesting delicate wine grapes worth over $6,000 per tonne. From vineyards in the UK to cutting-edge labs, discover how these innovations could transform not just viticulture but the entire future of precision agriculture. Additional materials: www.superdatascience.com/840 Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information.

Would you recommend to data professionals, data analysts, data scientists, maybe software developers, our listeners in general, who should be creating social media content?

I was like, "I'm going to get a PhD or a Master's degree when I grow up."

I've been taking notes through this episode and I'm going to be taking more notes afterward as well.

Her popular social media content on SQL, Data Analytics, Data Science, tech advancements, and maximizing professional growth has led her to amassing over 300,000 followers across LinkedIn, Instagram, and TikTok.

I was scared to post again and my coworkers were like, "No, Jess you're going to do fine, you'll be great."

Podcast TranscriptJon Krohn: 00:02This is episode number 838 with Jennifer K. Hill, co-founder and CEO of OptiMatch.

It's programmed to be friendly, whereas the other humans in the bar-Jennifer K. Hill: 10:05That's how you'll know it's fake.

Jennifer K. Hill: 17:45May I offer some positive resistance of the opening talk of Web Summit?

Jennifer K. Hill: 42:30And that consciousness is fundamental, and that space-time only arises as an artifact of consciousness.

Jennifer K. Hill: 43:22How do we know that we're not somebody else's science experiment or that we're not somebody else's computer program running?

Deepali is Senior Partner and Global Head of the Data, AI and Financial Technology Practice of Korn Ferry, one of the world's largest executive search firms.

00:04:31At Korn Ferry, you lead executive search and leadership consulting for strategic AI data and analytics leaders across industries.

And so there are things that you're going to have to do to show where those soft skills are more transferable.

And so they really adopted it really, really well.

I'm going to lean on my industry friends and I'm going to bring this to the younger generation."

Economist and social-impact innovator Dr. Nat Ware reveals how our expectations shape happiness and why chasing it often leaves us unfulfilled. He shares insights on the “hedonic treadmill” and the effects of constant comparison on our well-being. Find out how to build a more meaningful life by making memories, taking chances, and focusing on genuine connections. Additional materials: www.superdatascience.com/836 Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information.

And now we're seeing this translate into B2B use cases for us, which are where it gets really, really exciting as well.

And then for years after, it was, oh, it was obvious to me that we needed to get really, really large context windows because you need to have longer context.

But an AI system can scale way, way, way, way more than us.

Bryan McCann: 01:26:43 Kind of, general topology, and just seeing the world in terms of stuff and functions.

Bryan McCann: 01:28:29 I'm most active right now on LinkedIn, so you can follow me and connect with me there.

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Meanwhile we invite you to check out our podcasts in:Subscribe on Website, Apple Podcasts, Spotify, Stitcher Radio or TuneIn

It is really, really good at extracting characters.

That's really, really critical.

I don't think the industry has really, or the field has really, really figured this out.

It's open source model and is really, really interesting.

I've really, really enjoyed it.

(01:48): The aspect of Dario’s article that made the biggest splash was his explicit defining the term “Powerful AI”.

Instead, Powerful AI has all the interfaces available to a human that’s working virtually – text, audio, video, mouse and keyboard control, and internet access.

And while the Powerful AI doesn't have a physical body, it can control existing tools, robots, and laboratory equipment through computer interfaces.

Potentially within as few as 5-10 years after developing this Powerful AI system, we could see transformative changes across multiple domains.

(00:02): This is Episode #832 on the Anthropic CEO’s “Powerful AI” Utopia.

Jon Krohn: 00:09:57So you are the CTO of Lightning AI, the makers of AI Studio, PyTorch Lightning, and many more open source products.

So PyTorch Lightning was released, was born many years before, created by William Falcon, the CEO and founder of Lightning AI.

And incidentally, I got to know PyTorch Lightning before I got to know William because my company in Italy, Orobix landed on PyTorch Lightning to standardize their training code.

And in fact, PyTorch Lightning became one of the ways PyTorch lands in organizations because PyTorch Lightning doesn't wrap PyTorch.

To recap, in today's episode, Dr. Luca Antiga filled us in on how Lightning AI offers tools like PyTorch Lightning, Lightning…

Geoffrey Hinton and Sir Demis Hassabis: The Nobel Prize committee is an achievement of the highest order, awarding physicists, chemists, physiologists, medical practitioners, writers, pacifists and economists perhaps the greatest honor in their respective fields. In this week’s Five-Minute Friday, Jon Krohn discusses how two AI pioneers came to win prizes in chemistry and physics. Additional materials: www.superdatascience.com/830 Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information.

Neuroscientist Bradley Voytek outlines to Jon Krohn the incredible use of data science and machine learning in his research and how recent discoveries in action potentials and neurons have completely skyrocketed the field to a new understanding of the brain and its functions. You’ll also hear what Bradley thinks is most important when hiring data scientists and his contributions to Uber’s algorithm when it was still a startup. This episode is brought to you by epic LinkedIn Learning instructor Keith McCormick, and by Gurobi, the Decision Intelligence Leader. Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information. In this e…

The citizen data scientist: Fact or fiction? Jon Krohn holds a conversation across episodes in this Five-Minute Friday, with today’s guest Keith McCormick, in part responding to Nick Elprin’s interview in episode 811: Scaling Data Teams Effectively. Additional materials: www.superdatascience.com/828 Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information.

Together, they explore the growing importance of distinguishing human-written from AI-generated text, discussing real-world examples from social media to news.

How reliable are current detection tools like DetectGPT?

What are the ethical and technical challenges ahead as AI continues to advance?

And is the balance between innovation and regulation tipping in the right direction?

Tune in for insights on the future of AI text detection and the broader implications for media, academia, and policy.

In this episode of Data Science at Home, we dive into the hidden costs of AI’s rapid growth — specifically, its massive energy consumption.

With tools like ChatGPT reaching 200 million weekly active users, the environmental impact of AI is becoming impossible to ignore.

Each query, every training session, and every breakthrough come with a price in kilowatt-hours, raising questions about AI’s sustainability.

Join us, as we uncovers the staggering figures behind AI’s energy demands and explores practical solutions for the future.

From efficiency-focused algorithms and specialized hardware to decentralized learning, this episode examines how we can balance AI’s advancements with our planet’s …

Subscribe to our new channel https://www.youtube.com/@DataScienceatHomeIn this episode of Data Science at Home, we confront a tragic story highlighting the ethical and emotional complexities of AI technology.

This devastating event has sparked urgent discussions on the mental health risks, ethical responsibilities, and potential regulations surrounding AI chatbots, especially as they become increasingly lifelike.

🎙️ Topics Covered:AI & Emotional Attachment: How hyper-realistic AI chatbots can foster intense emotional bonds with users, especially vulnerable groups like adolescents.

Mental Health Risks: The potential for AI to unintentionally contribute to mental health issues, and the challe…

Ever feel like VC advice is all over the place?

That’s because it is.

In this episode, I expose the madness behind the money and how to navigate their confusing advice!

Watch the video at https://youtu.be/IBrPFyRMG1QSubscribe to our new Youtube channel https://www.youtube.com/@DataScienceatHome00:00 – Introduction00:16 – The Wild World of VC Advice02:01 – Grow Fast vs. Grow Slow05:00 – Listen to Customers or Innovate Ahead09:51 – Raise Big or Stay Lean?

14:20 – The Real VC Secret: Focus on Your Team and Vision17:03 – Outro

In this episode of Data Science at Home, Frag dives deep into the wild claims that Large Language Models (LLMs) like Chinchilla 70B are beating traditional lossless compression algorithms.

🧠💥But before you toss out your FLAC collection, let’s break down Shannon’s Source Coding Theorem and why entropy sets the ultimate limit on lossless compression.

We explore: ⚙️ How LLMs leverage probabilistic patterns for compression 📉 Why compression efficiency doesn’t equal general intelligence 🚀 The practical (and ridiculous) challenges of using AI for compression 💡 Can AI actually BREAK Shannon’s limit—or is it just an illusion?

If you love AI, algorithms, or just enjoy some good old myth-busting, thi…

Are AI giants really building trustworthy systems?

A groundbreaking transparency report by Stanford, MIT, and Princeton says no.

In this episode, we expose the shocking lack of transparency in AI development and how it impacts bias, safety, and trust in the technology.

We’ll break down Gary Marcus’s demands for more openness and what consumers should know about the AI products shaping their lives.

Check our new YouTube channel https://www.youtube.com/@DataScienceatHome and Subscribe!

We’re revisiting one of our most popular episodes from last year, where renowned financial expert Chris Skinner explores the future of money.

In this fascinating discussion, Skinner dives deep into cryptocurrencies, digital currencies, AI, and even the metaverse.

He touches on government regulations, the role of tech in finance, and what these innovations mean for humanity.

Now, one year later, we encourage you to listen again and reflect—how much has changed?

Are Chris Skinner’s predictions still holding up, or has the financial landscape evolved in unexpected ways?

In this episode, join me and the Kaggle Grand Master, Konrad Banachewicz, for a hilarious journey into the zany world of data science trends.

From algorithm acrobatics to AI, creativity, Hollywood movies, and music, we just can’t get enough.

It’s the typical episode with a dose of nerdy comedy you didn’t know you needed.

Buckle up, it’s a data disco, and we’re breaking down the binary!

SponsorsIntrepid AI is an AI assisted all-in-one platform for robotics teams.

In this episode we delve into the dynamic realm of game development and the transformative role of artificial intelligence (AI).

Join Frag, Jim and Mike as they explore the current landscape of game development processes, from initial creative ideation to the integration of AI-driven solutions.

With Mike’s expertise as a software executive and avid game developer, we uncover the potential of AI to revolutionize game design, streamline development cycles, and enhance player experiences.

SponsorsIntrepid AI ( https://intrepid.ai ) is an AI assisted all-in-one platform for robotics teams.

Build robotics applications in minutes, not months.

In this episode, we dive into the wild world of Large Language Models (LLMs) and their knack for… making things up.

Can they really generalize without throwing in some fictional facts?

Or is hallucination just part of their charm?

Let’s separate the genius from the guesswork in this insightful breakdown of AI’s creativity problem.

TL;DR;LLM Generalisation without hallucinations.

The hype around Generative AI is real, but is the bubble about to burst?

Join me as we dissect the recent downturn in AI investments and what it means for the tech giants like OpenAI and Nvidia.

Could this be the end of the AI gold rush, or just a bump in the road?

In this insightful episode, we dive deep into the pressing issue of data privacy, where 86% of U.S. consumers express growing concerns and 40% don’t trust companies to handle their data ethically.

Join us as we chat with the Vice President of Engineering at MetaRouter, a cutting-edge platform enabling enterprises to regain control over their customer data.

We explore how MetaRouter empowers businesses to manage data in a 1st-party context, ensuring ethical, compliant handling while navigating the complexities of privacy regulations.

SponsorsIntrepid AI ( https://intrepid.ai ) is an AI assisted all-in-one platform for robotics teams.

Build robotics applications in minutes, not monthsReferenc…

Join us as David Marom, Head of Panoply Business, explores the benefits of all-in-one data platforms.

Learn how tech stack consolidation boosts efficiency, improves data accuracy, and cuts costs.

David shares insights on overcoming common challenges, enhancing data governance, and success stories from organizations thriving with Panoply.

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Join us in this exciting episode of the Data Science at Home podcast.

It’s all about GPUs.

We’ll take you on a journey through the inner workings of these powerful processors, explaining how they handle complex computations and drive everything from gaming graphics to scientific simulations.

Whether you’re a budding programmer or a tech enthusiast, understanding GPUs is key to unlocking new levels of performance and efficiency in your projects.

Tune in and get ready to turbocharge your tech knowledge!

In this episode, we sit down with Ryan Smith, Founder of QFunction LLC, to explore how AI and machine learning are revolutionizing cybersecurity.

With over 8 years of experience, including work at NASA’s Jet Propulsion Laboratory, Ryan shares insights on the future of threat detection and prevention, the challenges businesses face in maintaining effective cybersecurity, and the ethical considerations of AI implementation.

Learn about cost-effective strategies for small businesses, the importance of collaboration in combating cyber threats, and how QFunction tailors its AI solutions to meet diverse industry needs.

QFunction does cybersecurity differently.

By relying on scientific breakthroug…