“François gave an immense contribution to the arge scale diffusion of deep learning among scientists, researchers, and practitioners. His open source library, Keras, provides the right level of abstraction for describing even complex deep learning models in an elegant and very intuitive way. The library has emerged as natural standard among other frameworks, and the fact that it has been adopted as high level fronted by Google's Tensorflow, Microsoft's CNTK, Apple's core ML, Amazon's mxnet, and University of Montreal's theano, is a clear sign of the quality of his work. In addition to that, François is a very easy person to interact with and a valuable artist. ”
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Win prizes, fame and become part of history! While aiming to solve ARC 1, which is the target of the #ARCathon, Lab42 together with François…
Win prizes, fame and become part of history! While aiming to solve ARC 1, which is the target of the #ARCathon, Lab42 together with François…
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Dusty Chadwick
Hallucinations in LLMs are inevitable, no seriously! Josh Fritzsche and I have worked hard at Voze to find a way to tune, prompt and even plead 🙏 with LLMs to prevent them from the occasional 🍄 hallucination. It seemed like the better we have done the harder the edge cases have been to solve and eventually we realized that they tend to frequently happen when LLMs predict or make anticipations on "nothing". I remember at one point nearly going insane with frustration because when LLMs had a hallucination, they were EPIC! 😱 Then we pushed through it and benefited for the effort! How did we solve this common problem? We didn't (Not completely), we got better at identifying the issues as they happened. Once we identified in the data we were able to start looking for patterns. The same catalysts that cause it to happen once if left unchanged caused it over and over again. Our solution is astoundingly simple. Once you know that it's likely to happen..... let it! Accept it will 🍄, but also be preemptive in not focusing on it and use alternative data sources, generators or providers. Change the conditions that cause it but continue to monitor it as it happens. As Dan Caffee is fond of saying to me on a regular bases. "Dusty we need the system to be adaptive and provide mechanisms to learn from it's past." Well he is right! All we needed was data and the people that contribute to it's quality. Huge props 👏 to strong support from Kathy and Janelles audit teams. We were armed with a knowledge and a desire to learn from past mistakes. Armed with confidence that we could correct these problems we could focus on the data to prevent their impact going forward. When doing millions of LLM (Generative AI) calls regularly Josh and I have learned that hallucinations will happen. We also know exactly how we will handle and prevent it going forward! Failure is not doing anything and accepting defeat.
42 Comments -
Dirk-Jan van Veen
One of the best things about being in San Francisco 🌉 is the ability to drop in on great talks. Last week I had a chance to see NVIDIA's CEO Jensen Huang (JH) being interviewed by Stripe's CEO Patrick Collison. Jensen is among the longest-serving founder-CEOs in Silicon Valley at the moment (30+ years). With that comes a special clarity of mind and some hot takes. 🌶👇 🗣 Jensen doesn't believe in 'praise in public and criticism in private'. Instead, he reasons that most information in the organization should not be privy to certain people. By giving feedback in public, you allow everyone to learn. 'What is better than learning from your own mistakes? It's learning from someone else's mistakes!!' 🔥 JH also doesn't like to fire people. He would rather "torture them into greatness". He invests in people and he doesn't like to write off his investments. 👨💻 Asked whether he likes his job every day, he answers "no". The perfect job doesn't give you happiness 100% of the time. "No great things were done, that were easy all the way." ... "Even if I come home from a day where I did things I didn't enjoy, it could still be have been a great day." 👩👧👦 Jensen famously has 60 direct reports. "This allowed us to cut out probably 7 levels of management". His subordinates don't necessarily have such large teams. "It doesn't scale down, less senior people need more guidance". Looking at the numbers, this approach allowed NVidia to reach ~2T market cap with ~28.000 people vs. Microsoft's 3T with ~220.000 people and Apple's 2.6T with 160.000 people. ❔ Jensen answers almost every question in the same way: "Let's break it down, let's reason about it". This is a great response on so many levels. First, it allows him to not be put on the spot. Second, it allows him to teach his workforce how he reasons through a problem so that they can emulate it and create alignment. Third, it gives people a chance to point out flaws in his reasoning. He admits he is not right all the time. ON AI 👨🔧 "You will not lose your job to AI, your company will go out of business because another company used AI." "If you're not using AI you're doing it wrong." ⏱ The extent to which AI will enter many processes is largely underestimated. Right now we are focused on language - sounds in a time sequence. "Any time sequence can be tokenized." This will include robotic movement. 🥛 JH also expects that we won't be satisfied with foundation models "You're going to want to tune it into perfection because you care so deeply. Say if it's already at 99% you'll want to get it to say 99.3%" This last piece is also why we created Query Vary (YC W22). AI will never feel truly intelligent until it completely understands your data and context. It needs to be taught, regardless of how powerful the foundation model is. Finally, besides insights, it was remarkable how funny Jensen was, cracking a joke at roughly a 1 per 3 min rate. It was a very entertaining talk. #ai #sf
618 Comments -
Hai Huang
Stole a few points from Prof Christopher Manning, who talked about LLMs and language modeling in general in the latest TWIML AI Podcast: 📌 Humans acquire language skills in a way very different from LLMs. We need millions of words, compared to billions or even trillions of tokens for LLMs. LLM researchers may want to investigate and learn from how humans acquire language skills. 📌 LLMs cannot reason. However, there are other deep learning models, such as AlphaGo, that can. LLM researchers may want to look into how to integrate that type of reasoning/searching/planning capability into LLMs. 📌 LLMs' world models should enable search and discovery. Although Prof. Manning didn’t call this out explicitly, my understanding is more similar to a knowledge graph type of structure. 📌 Next-gen LLM idea: a soft form of locality and hierarchy. Transformers attend every token to every other token, which is very inefficient, while human language can be modeled by n-grams most of the time. #artificialintelligence #machinelearning #deeplearning https://lnkd.in/euzwMQ6p
8418 Comments -
Joelle Pineau
We just released Meta Llama 3: the most capable openly available LLM available to date! The 8B & 70B models are out now, and we expect to release models with larger context windows, additional model sizes and more capabilities in the coming months. Congratulations Ahmad Al-Dahle + the entire GenAI team + all of the incredible XFN across Meta that came together to ship these new models! More details on Llama 3 and benchmarks here: https://lnkd.in/e7P2mvgV
4149 Comments -
Alexey Gorodilov
It's interesting to watch. torchtitan is a proof-of-concept for Large-scale LLM training using native PyTorch. It is a repo to showcase PyTorch's latest distributed training features in a clean, minimal codebase. torchtitan is currently in a pre-release state and under extensive development. https://lnkd.in/evNebqu9
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Michael Gschwind
April was an exciting month for AI at Meta! We launched MTIA v2 https://lnkd.in/gvC-UgVN, Llama3 https://lnkd.in/gdbfXa-6, presented a tutorial and paper on the PyTorch2 compiler at ASPLOS https://lnkd.in/gsT9DUr5, released PyTorch 2.3 https://lnkd.in/gYn3Vnzm and, to top it off, we launched the PyTorch ecosystem solution for mobile and edge deployments, ExecuTorch Alpha https://lnkd.in/gWv96jpm optimized for Large Language Models. What better than to combine all of these... running Llama3 on an a mobile phone exported with the PT2 Compiler's torch.export, and optimized for mobile deployment. And you can do all of this in an easy-to-use self-service format starting today, for both iPhone and Android as well as many other mobile/edge devices. The video below shows Llama3 running on an iPhone. (Makers will love how well models run on Raspberry Pi 5!) PyTorch is being developed by a multi-disciplinary team comprising ML engineers, accelerator experts, compiler developers, hardware architects, chip designers, HPC developers, mobile developers, and specialists and generalists that are comfortable across many of the layers involved in building end-to-end solutions. Even better -- if you're excited by the possibilities of AI, and solving the system design challenges of making AI run well across all hardware types, we are looking for YOU! The Pytorch team has openings across PyTorch core, compilers, accelerators and HW/SW co-design https://lnkd.in/gu2Y_KpY https://lnkd.in/gQVXBP6P and a broad range of positions that involve PyTorch from model development all the way to hardware deployments https://lnkd.in/gjDdUh5w #PyTorch #ExecuTorch #Llama3 #AICompilers #MTIA #AcceleratedAI #MetaAI #Meta
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Srimouli B.
🚀 Weekend Read: Delving into Transformer Reasoning Capabilities with Graph Algorithms 🚀 Looking for a thought-provoking read this weekend? I’m excited to dive into "Understanding Transformer Reasoning Capabilities via Graph Algorithms" by the talented team at Google Research and Columbia University. 🔍 What’s Inside: Transformer Scaling Regimes: Discover how transformers handle different classes of algorithmic problems, especially graph reasoning tasks. Theoretical Foundations: Gain insights into the theoretical underpinnings of transformers’ representational capabilities based on their depth, width, and additional tokens. Empirical Validation: See how transformers perform on graph reasoning tasks using the GraphQA benchmark compared to specialized graph neural networks (GNNs). 📊 Why It’s a Good Read: Innovative Framework: The paper introduces a novel representational hierarchy categorizing graph reasoning tasks by complexity and transformer capabilities. Performance Insights: Learn about the superior performance of transformers in tasks requiring long-range dependency analysis. Practical Impact: Explore the potential real-world applications of these insights in AI, from language modeling to computer vision. Check out the paper in the first comment. Let’s make this weekend insightful reading an excellent research! #AI #MachineLearning #Transformers #GraphAlgorithms #WeekendRead #ResearchDiscussion
111 Comment -
ANUPAM DEBNATH
Open-source AI for evaluating language models Researchers from KAIST AI, LG AI Research, CMU, MIT, Allen Institute for AI, and UIC have introduced Prometheus 2, a groundbreaking open-source evaluator model that assesses language model outputs with accuracy rivaling expensive proprietary solutions like GPT-4. A few key highlights: Prometheus 2 combines models trained on direct assessment and pairwise ranking, enabling it to excel at both common evaluation formats. It leverages the new Preference Collection dataset with 1,000 evaluation criteria to boost performance. Prometheus 2 achieved the highest correlation with human judgments and proprietary models across 8 benchmarks, in some cases reaching 0.90 Pearson correlation and 85%+ accuracy. This helps narrow the gap between open-source and closed-source AI evaluation without sacrificing transparency and control As an AI and Data Strategist, I like that this will make high-quality AI evaluation more accessible and scalable for researchers and practitioners. Some potential strategic impacts: Accelerating AI/ML system development by enabling rapid, reliable evaluation of generated language outputs at lower cost Establishing clearer standards and benchmarks for assessing language model performance as open alternatives mature #ai #machinelearning #nlp #opensource #research #aistrategy Paper - https://lnkd.in/gsnGDwJd
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Dr. Aditya Raj
A recent breakthrough titled "Matrix Multiplication-Free LLMs" demonstrates a huge advancement in the area of Large Language Models (LLMs) by reducing computational costs. The authors have eliminated MatMul operations from LLMs, claiming to 10 times reduction in memory usage and a 25.6% increase in training speed, all while maintaining strong performance at billion-parameter scales. Paper link: https://lnkd.in/ggph8qXc #AI #machinelearning #deeplearning #LLMs
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Alon Faktor, PhD.
Hey, I'd like to give a shout-out to ClearML for helping us at Vimeo develop AI-based systems. We have been happy customers for a few years now and I'd like to share a bit about how we use ClearML. We use ClearML Datasets to cache a dataset of video transcripts, and run testing loading directly from ClearML Datasets. This allows us to simultaneously speed up the data loading and ensure consistency. Moreover we use ClearML to save our benchmark annotations in one place and track our system performance on the benchmark every-time we run an experiment or change our prompts. ClearML allows us to see the different parameters and prompts that were used for each experiment and to monitor improvements or regressions in our performance. We also use ClearML to run large-scale tests and help with statistical evaluation of our methods. For example, we developed a RAG (Retrieval Augmented Generation) Q&A system and wanted to verify that the LLM will not answer certain questions or user queries that are outside the scope of the video. We used ClearML to collect and analyze the RAG responses on many videos for predefined user queries that were outside the scope of the videos and got good visibility into the performance of the system. Also, the comparison feature on ClearML is great for tracking the improvement of our metrics along the progressing versions of our systems.
291 Comment -
Chia Jeng Yang
Excited to announce another major upgrade to WhyHow.AI’s Knowledge Graph SDK - tying vector chunks to graph nodes automatically, for a more deterministic and richer context window. Check out how we do it, why we did it, and an example benchmark of the increased completeness of the answer. Tired of just returning single-word triples from your knowledge graph? WhyHow.AI’s latest upgrade with vector chunk linking now lets you use a graph structure to determine which raw vector chunks to return to the context window, combining the best of knowledge graphs and vector search. “While the triples in a Knowledge Graph are useful in providing specific information that semantic similarity was unable to retrieve, we wanted to also allow leeway in the information represented and retrieved from the graph, to include the surrounding words and retrieving the relevant raw vector chunk tied to that graph node as well. By tying vector chunks to a knowledge graph, we get the advantages that lie in both vector and graph search.” - WhyHow.AI Design Partner WhyHow.AI builds workflow tools for data orchestration, and graph creation, and we work on top of any data extraction model you want to bring. In this case, we work on top of OpenAI, Neo4j, and Pinecone, and will be supporting the most popular data extraction models, LLMs, graph and vector databases. https://lnkd.in/eEJdUNPi
683 Comments -
Chris Glaze
Excellent paper by Snorkel AI's very own Amanda Dsouza on how to evaluate language models with long context windows (LCMs) for real-world applications: https://lnkd.in/eHYAPMD8 LCMs are currently being explored by a lot of businesses for their ability to perform tasks over really long documents and document repos, which requires long "context windows" to fit those documents into. But how good are these LCMs when you start to fill up the windows with more and more data? To date, the most commonly used test for LCM capabilities is the "Needle in a Haystack Test" (NIAH). Takeaways from Amanda's work: -NIAH can miss a lot of important LCM deficiencies when you start to fill up the window a lot. -These same deficiencies can be uncovered by an alternative test she's developed we call the "Snorkel Working Memory Test" (SWiM). -We can elegantly correct for the types of deficiencies uncovered by SWiM and provide open-source code to do so.
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Anshul Bhide
A couple of recent college graduates from India against AI finetuning giants like Anyscale and Run:ai? I met Arko C at a NASSCOM event last October. He was building Xylem AI (YC S24) , a platform that allows you to train and deploy LLMs in production. So naturally most Indian VCs rejected him. Because how could a team of three recent college grads compete with the likes SV startups like Anyscale that have raised hundreds of millions of dollars, have teams of experienced AI researchers and a stash of H100 GPUs? Arko hustled and closed two Fortune 500 companies for paid contracts. I personally know unicorns that haven't managed to do this. He then got into YC in the last round in May. There's a lot to still be proved out, but Arko exemplifies how valuable grit is in building startups. I’m doing a webinar with him tomorrow on challenges of using LLMs in production. Link to register in the comments. Disclaimer: I'm a small investor in Xylem.
1504 Comments -
Atin Sanyal
In LLMs, long input sequences presents a significant challenge in detecting hallucinations using smaller, cheaper models. One of the key innovations (that blows past LLM-as-judge techniques) introduces a novel windowing mechanism specifically designed to address this issue. Consider this -- conventionally, the standard procedure for models with limited context lengths includes Segmentation, Localized Prediction and Aggregation. This traditional approach proves suboptimal for hallucination detection. e.g. a scenario where the language model generates a statement, "X", which is substantiated by context, "Y". If segments are created such that "X" and "Y" reside in separate chunks, the model remains oblivious to the mutual reinforcement between "X" and "Y", thereby impairing accurate hallucination detection. The approach we take circumvents the limitation by deploying a more refined windowing strategy (RAG context and the model-generated responses getting divided into small overlapping windows). This allows a smaller, auxiliary model to evaluate every possible pair of possible context and response windows. This shift in the windowing methodology offers a more robust framework for hallucination detection, advancing the frontier of reliability in RAG deployments. If you're interested in learning more, read our research here: https://lnkd.in/gFAqr7wB #llmhallucinations #llms #generativeai #llmevaluation #llmevals #llmbenchmarking
352 Comments -
Danilo T.
if you know, you know NVIDIA annual shareholder meeting today: june 26 2024 09:00 pst in about an hour we'll have a chance to listen to new developments. it's breath-taking. the speed of not only NVIDIA's ability to navigate (well i guess they must be using their own (ai) to decide what where how to do and then some. huh?) more importantly just as Apple Microsoft defined the pervasiveness of computers. their applications. we believe NVIDIA holds that position currently. they have been handsomely rewarded in the markets. a valuation as the most valuable in the world. 3.3 trillys. and going. so every and all announcements are truly where the centre of the universe of chip design, manufacturing, exists. it's more than simply offering state of the art chip design for (ai) modelling. where Alphabet Inc. organizes the world's information. NVIDIA makes sense of it. and everyone else applies it, to their subject matter expertise. this is the change that is afoot. at this moment. where it was a race to offer advertisers of services/products a better understanding of their clientele, now the lens is sharper. there is no need to suggest age/sex/ethnicity/generational differences. there are behaviours. theses behaviours are independent of what data was used to point adverts to. to get these people to buy more sh^t. that's the base of the u.s. economy. it's apparently consumer driven. and so in this post pandemic world where it literally stopped. and we were forced to sit still. wait it out as the storm clouds passed. in hopes no one. not us. or loved ones would perish. that time was a generational shift. we all felt. it. no matter what class you were. NVIDIA is the fulcrum Google is the waterfall or Google is the head NVIDIA the neck wherever the neck turns that is where the head has their attention. the neck keeps the attention of the head there. so it appears as if in many ways Google has in essence offered the search part of it's business up for grabs. this is why Perplexity exists, and others. a new dynamic pricing auction model is about to emerge. it will as it is by nature adversarial. let's not fool ourselves. we are designed to be adversarial. it's our nature. and in coveting $$$$$ as the prime objective. money for the sake of money this will destroy a lot of thoughtful, mindful, intentionally beneficial innovation. why? well. it's not playing according to the whims of a recommendation machine that rewards those that are part of it's invisible army. today is a crowning. it's a crown. upon the head of jensen. this is akin to king charles and his crowning. we wonder who will be at the crowning. to recognize this eventful day. it's good to be king. ;) < . > source: https://lnkd.in/eumX4num
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Vrushank Desai
I spent a couple months at the beginning of this year learning about GPU programming through trying to optimize inference for Cheng Chi’s awesome Diffusion Policy paper. I was able to improve inference time for the convolutional U-Net by ~3.4x over Pytorch eager mode and ~2.65x over Pytorch compile mode! For anyone interested in GPU optimizations for deep learning, I wrote a 9-part blog post that builds up from the physical structure of DRAM/SRAM cells all the way up to integrating custom CUDA kernels in Pytorch: https://lnkd.in/dBMSqh4g I also have a Twitter thread of the most interesting tid-bits here: https://lnkd.in/db_hEqbD This video (requires audio) is unrelated to the Diffusion inference stuff but imo, more amusing… I was able to get my Nvidia RTX 3090 inductor coils to play ‘Twinkle Twinkle Little Star’ using kernels (GPU programs) that modulate power draw at the right frequencies! What’s happening here is each kernel launch triggers a surge of in-rush current in the GPU’s inductor coils. The Lorentz force due to the change in current (proportional to change in current divided by the change in time) causes the coil to move slightly. If we play with the kernel launch frequencies we can vibrate the coils and get noises in the audible range. Unfortunately we can’t make sounds lower than 2000Hz because the ‘change in time’ part of the equation becomes too large, and the resulting vibration is too weak to make audible noise. So we end up with Twinkle Twinkle shifted up many octaves 😀
22415 Comments -
Gokul Rajaram
The most robust, reliable, and productive uses of AI are when many models are used in coordination with each other, with "regular" programming mixed in. This leads to more capable, more reliable, and more interpretable AI systems. Most people building with AI already know this. But (unless you work for Google) the main barrier to realizing multi-step AI workloads is an infrastructure one. For the rest of us, we’re left creating an unwieldy mess of chained API calls to multiple providers, with many roundtrips in between. This state of affairs is stifling progress. Nobody wants to deal with more tooling and infrastructure… but everyone would benefit from simple, intuitive interfaces that abstract away a powerful system underneath. Substrate is the first inference API optimized for multi-step AI workloads. With Substrate, you can write less code, run more inference, and build high performance AI applications with zero infrastructure to manage. No tooling, no infrastructure – just elegant abstractions. Excited to support Ben Guo, Rob Cheung and the amazing Substrate team on their mission to democratize how AI applications are built.
404 Comments -
Ulyana Tkachenko
I'm so excited to share that TLM v1.0 has been officially launched! 🎉 TLM is where Data Curation 🤝 LLMs. Thanks to the wonderful efforts of the Cleanlab team, we've created an awesome product that can: - Improve LLM response reliability - Quantify the trustworthiness of responses from any LLM LLMs will always have some hallucinations, but by providing a trustworthiness score with every output, Cleanlab TLM lets you identify when the LLM is hallucinating to enable reliable deployment of LLM-based applications. Read more about it in our research blog: https://lnkd.in/dTZUVTmG Check it out for yourself here: https://tlm.cleanlab.ai/
311 Comment
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