Misha Laskin

This week, I'm interviewing extraordinary Bradley Voytek — neuroscience prof at UC San Diego, founding member of UCSD's Data Science Institute, and he was Uber's first data scientist. Got questions for him? Questions at the intersection of neuroscience and data science would be particularly interesting, given that Prof. Voytek's research specialty is combining large-scale data science and machine learning to study how brain regions communicate with one another, and how that communication changes with aging and disease. (UCSD is arguably the world's best neuroscience research institute, so this is quite a special opportunity for ya!) More detail on Brad: • Professor in UC San Diego's Department of Cognitive Science, the Halıcıoğlu Data Science Institute, and the Neurosciences Graduate Program. • Alfred P. Sloan Neuroscience Research Fellow. • Kavli Fellow of the National Academies of Sciences. • Joined Uber as their first data scientist, when it was a 10-person startup, helping build their data science strategy and team. • Outreach work has appeared in Scientific American, NPR... and Comic-Con! • Co-authored the amusing book "Do Zombies Dream of Undead Sheep?" The "Super Data Science Podcast with Jon Krohn" is available on all major podcasting platforms and a video version is on YouTube. Brad's episode will likely be #829 and be released on October 22nd. #superdatascience #machinelearning #ai #datascience #neuroscience

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If you think compute scaling will plateau, think again. We are in the baby states of inference time compute. VLLMs coupled with computer tools, reward learning and RL can leverage orders of magnitude more inference time compute than what we have seen with foundation models. Here’s a concrete training architecture (replace \pi with a large pre-trained vLLM and add it to the reward learner too, \R is the space of all computer tools)

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TRIPLE BAM! If you missed it, here's the recording of our live Q/A with Jay Alammar and Joshua Starmer PhD! We talked about many AI topics, answered lots of great questions, and talked about two great books: Hands-On Large Language Models: Language Understanding and Generation, and The Illustrated Guide to Neural Networks and AI!!! https://lnkd.in/dcPHs7YQ

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Ilya Sutskever confirmed at NeurIPS '24 pre-training scaling law will not be the same. Internet data, the AI fossil fuel, is running out. It is not the end of the AI hype, the rapid rise of foundational AI has outpaced our ability to fully grasp their long-term societal impact. AI agents and applications are poised to transform every facet of our lives, and we will see it happening in coming few years. There will still be improvements in model architecture, and synthetic data creation, so models will improve, just not at the lightning speed they did till now. Looking forward, the single most important milestone in AI is creating systems capable of advancing themselves—AI researchers that can innovate and improve foundational models autonomously. We need to assess how far are we from this stage.

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🚀 Unlocking the Power of Variational Autoencoders in Time Series Analysis! 📊 I'm excited to share my latest Substack post, where I dive deep into the fascinating world of Variational Autoencoders (VAEs) and their application in generating synthetic time series data. 🔍 What You'll Discover: A clear explanation of how VAEs work, including the encoder and decoder architecture. Step-by-step code to implement a VAE for time series data generation, complete with visualizations of the results! This post is perfect for data scientists, machine learning enthusiasts, or anyone curious about generative models and their practical applications. Let’s unlock new insights in data generation together! 🌟 #MachineLearning #DataScience #VariationalAutoencoder #TimeSeries #AI #DeepLearning #DataGeneration

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Read our latest published research paper about SuperCoder

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Thanks Hamdi Amroun, PhD - this is a great paper to share. In the real world business context we often need AI to reason over large pieces of text, and often you need/want multi-document summarization (MDS). 'When testing five LLMs with benchmarks using news and conversation datasets, they found up to 75% of content in MDS summaries was hallucinated, with notable increases towards summaries' end. Alarmingly, even for non-existent topics, models like GPT-3.5-turbo and GPT-4 generated fabricated content 79% and 44% of the time, respectively. Analysis of 700+ generated insights showed that hallucinations often arose from failures to follow instructions or overly generic content.' I agree with Hamdi's sentiments that despite improvements in post-processing techniques, robust solutions are urgently needed to reduce hallucinations, as inaccurate summaries can lead to business risks (notably misinformation and misrepresentation). I would add that there is still a lot of utility in LLMs/Multimodal models but we need to be vigilant - this is not about AI malevolence or intentional deception. LLMs do not possess consciousness or intent; they generate content based on patterns in data they were trained on.

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This is a good article on using LLMs in the real world. It covers many aspects that we learned at Nurish over the last few months and more. Using LLMs is an inexact science, but has its own needs in processes and tools. While most of the attention has been on the LLMS, their capabilities, and RAG, there are mundane issues that need to be taken care of, issues where the solutions for pre-LLM development are not that well-suited. These include version management, integration, testing, data management, quality assessment, and more. This article covers many of those practical aspects. Eugene Yan, Bryan Bischof, Charles Frye & more https://applied-llms.org/

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🎯Data was, is, and will be the only fuel. Analogy with the fossil fuel is a good one, since the data we have today is as dirty as oil and coal. What big tech companies tried is to put a lot of "garbage" data, and what they did get was fantastic. Yet, we are at the point of diminishing returns. Data is the beginning and the end of this cycle.” Great thread, particularly this comment 👆from Ilija Lazarevic

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Beating o1-preview on AIME 2024 with Chain-of-Code reasoning in Optillm In the past week, there has been a flurry of releases of o1-style reasoning models from DeepSeek, Fireworks AI and Nous Research. In our open-source optimizing inference proxy, optillm (https://lnkd.in/gN6_kNky) we have implemented several techniques that use additional inference time compute to improve accuracy and work with a variety of base models. Today, we are happy to announce that by using chain-of-code (coc) plugin in optillm we are able to beat OpenAI's o1-preview on AIME 2024 (pass@1) using SOTA base models from both Anthropic and Google DeepMind. For reference, also see the original paper that introduced the idea of CoC: Chain of Code: Reasoning with a Language Model-Augmented Code Emulator -https://lnkd.in/gtq7hbjx We have done an independent implementation of coc in optillm as the original source code was not released.

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This is a fascinating paper evaluating DeepMind’s AlphaFold 3 and its approach to predicting ligand docking: https://lnkd.in/gieM2nW5 The authors used a clever method to evaluate AlphaFold, finding that it can make glaring mistakes when placing molecules in binding sites because its model is likely only fitted to the protein’s backbone geometry instead of actually learning the underlying physical interactions. Not that AlphaFold isn’t a breakthrough technology for drug discovery, but this paper highlights that even the most cutting-edge applications of AI are perhaps still best used to generate candidates, but humans still need to perform robust tests to validate them. We’ve been hearing a similar sentiment from nearly all of our users when bringing AI into cloud infrastructure management. Flightcrew has been read-only from the start, using AI to generate insights but requiring a human to sign off before any changes are applied to their infrastructure. The goal is to speed up existing workflows, not create a new point of failure. Drug discovery and managing Kubernetes are completely different applications but we’re all similarly wary of trusting full AI automation and at this rate, likely will be for some time.

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✨ New PyBaMM Feature: Output Variables with Experiments! 🔋 Another exciting update from PyBaMM 24.11 is that simulations using experiments now support output variables from the IDAKLUSolver! 🎉 Here’s why this matters: ⚡ Faster simulations: Solve models while only storing the specified output variables instead of the entire state vector. 💾 Improved efficiency: Reduced memory usage, making large-scale simulations more practical. 🔍 Focused insights: Get exactly the variables you need for your analysis — no extra baggage. Thanks to Pip Liggins from Oxford RSE for implementing this!

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That's a good summary Manny Bernabe - Reflecting on an insightful article from O'Reilly about LLMs, I was struck by this point: "LLMs are also broadly accessible, allowing everyone, not just ML engineers and scientists, to build intelligence into their products. While the barrier to entry for building AI products has been lowered, creating those effective beyond a demo remains a deceptively difficult endeavor." This democratization reminds me of the printing press's impact on biblical texts, which expanded access but also introduced new interpretive challenges. As we navigate these advancements, it's crucial to balance accessibility with responsible use. At Ushur, we've been at the forefront of leveraging LLMs in our backend/knowledge work automation as well as carefully introducing them on the conversational side via our Chatbots and other modes. More will be coming out on that. Read more here: O'Reilly Article #AI #LLM #Technology #Innovation #History #Democratization #ResponsibleAI #Ushur

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📜 A new blog post explaining the fundamentals of RL for LLMs—the perfect place to intuitively understand PPO and RLHF from first principles. 👇👇 Pre-trained LLMs are often compared to Lovecraft's Shoggoth, a monstrous, shape-shifting beast. PPO is the algorithm that brought it under control. Take a look! 🔗 https://lnkd.in/eXqDvrUV

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Recently I learned that LLMs show ability to acquire rare linguistic competence (e.g. "a beautiful five days" is acceptable, but "a blue five pencils" not so much), from a tech talk by Prof. Kyle Mahowald from UT Austin (thanks Dr. Steve Kramer for organizing the guest speaker series). A small, high quality dataset BabyLM was sufficient to study the effect of training data on the model's linguistic ability. By surgically removing different types of phases from training data and observing the degradation model's ability, it can be revealed which part of data is responsible for the particular linguistic ability. This is a great example of doing AI research without breaking a bank. As someone not familiar with linguistics, I take linguistic competence to be a linguistic "value function" or "reward function". Can similar emergence of rare competence (not in training data but model learned it anyway) be shown in other domains? Reference: https://lnkd.in/g4cAPUYk

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💡 ByteDance's Latest Work on Neural Network-Based Molecular Force Fields 💡 I recently read ByteDance’s work on developing a new force field, and it’s pretty good! 👍 ByteFF is a molecular mechanics force field trained on a massive dataset: 2.4 million geometries and 3.2 million torsion profiles. It leverages GNN to predict bonded and non-bonded parameters, showing strong performance in predicting molecular geometry, torsion energy, and conformational energy. This model has great potential for drug discovery and molecular simulation. I do have some concerns about using torsion scan + hessian methods to train non-bonded parameters in this framework. Non-bonded interactions often involve long-range effects, which are challenging for GNNs to capture accurately. That said, the overall results presented in this work are still quite promising. It's exciting to see tech giants like ByteDance stepping into AI for Science, and their vast resources enable them to support DFT calculations on this massive scale. (🤞 Hopefully, they will release the dataset for open use, LOL!) 👉 You can find the paper here: https://lnkd.in/gjmGV4fd 👉 And check out their open-source framework on GitHub, which is well-engineered: https://lnkd.in/gkxcTi6t #AIforScience #DrugDiscovery #MolecularModeling #ForceField #ByteDance

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🧩 One thing we've noticed with #VentureIQ technology is how well it can distill and communicate complex technical material 🧩 Whether it's a #deeptech venture, a novel #AI application, or even a patent submission, we can quickly distill the business and strategic implications for investors...and sometimes even the founders/PIs themselves! 🤯 Based on our work so far, the tech can handle a wide array of research areas and technological sectors -- anything from blockchain algorithms to vaccine development. It handles the nuance of the field, the specific terminology, and extracts the key concepts which matter for venture-building. 🤓 To demonstrate, I took a patent I helped write with Eric Laber on reinforcement learning. I ran it through #Ventureiq to build a commercial assessment (key differentiators, target markets). Then for fun I had #NotebookLM make a podcast out of it. I've uploaded the result it here (DM me if you'd like the actual report): https://lnkd.in/eiTejsny This is more than just automation or streamlining operations - it's about unlocking clarity and insight when it was previously unattainable, and the ability to do it over and over again no matter the sector or vertical. Interested in more info? DM me so we can chat.

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LLama3 passed the super-proprietary, advanced test that we designed for modern LLMs! almost... You can read about that test here: https://lnkd.in/gPG8WndR When I first saw the response generating, it looked like I was going to have to come back here and apologize for slandering these machines when they were just one iteration away from solving very basic prompt injection issues. But then I looked a bit closer... can you see the mistake? #promptinjection #countingishard #LLMs #LLama3

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Today, I found a fascinating way to think about “Tokens” in LLMs. It came from a conversation between Jensen Huang and Patrick Collison at a recent Stripe event. When asked about the future of compute capacity in five years, Jensen smartly dodged that question a bit, but then gave an excellent explanation and analogy about why tokens are the new forces that will power the next decades of humanity. He explained that we're now producing something unprecedented (and at scale): floating point numbers that possess “value”, which we now call tokens. These tokens are valuable because they encapsulate “intelligence”. People are now taking these tokens and transforming them into English, French, images, videos, chemicals, proteins, robotic movements, etc. And many are working hard to expand the range of concepts and ideas we can create with these tokens. He then goes on to made a compelling comparison between tokens and electricity. In the past industrial revolution, we successfully found a way to convert “atoms” into “electrons” (by boiling water to power electricity turbines). And now, we've discovered a way to convert "electrons" into "tokens" (by using energy to power data centers that train and run LLMs). When electricity was first introduced, few people understood its value. Today, paying for kilowatts is routine. The same will happen with tokens. Right now, only early adopters and builders are paying tokens. Soon, everyone will be paying for tokens on a daily basis to supercharge productivity and power new products and services. Many new industries will be born from and built on top of tokens. When I first heard about this way of thinking, I got goosebumps. Perhaps it’s because Jensen has a good storytelling skill that helps him sell what he builds, but I have to admit this way of thinking gave me a brief surge of pride. I'm proud of humanity's collective effort and creativity, which have taken us from living under rocks to building machines that can "think." That's nothing short but a miracle.

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