TAI #113; Sakanas AI Scientist  Are LLM Agents Ready To Assist AI Research?
Source: tai_blog

What happened this week in AI by LouieThis week  xAI joined the growing crowd of broadly GPT-4 class models  which now includes models from OpenAI  Anthropic  Deepmind  xAI  Meta  Mistral  and DeepSeek (but only the first 4 have multimodal capabilities). Anthropic also launched a context caching option saving up to 10x for reused input tokens costs. We recently flagged that context caching opens up many new opportunities  including for complex LLM agent pipelines  and on this note  this week  Sakana AI introduced The AI Scientist  an LLM agent for assisting machine learning research.   Sakanas agent begins by brainstorming new ideas using an initial topic and codebase (provided by a human researcher) and performs a literature search to review its ideas for novelty. It then plans and executes code-based experiments and gathers and visualizes data before writing a full research paper. It also includes an automated LLM peer review process that evaluates these papers. We think Sakanas agent includes a strong feedback loop that can drive continuous improvement. In particular  its peer reviewer agent can be used to filter and label good and bad examples of ideas  experiments  and papers  and the agent can learn from both in the future.   Currently  this agent has many shortcomings  and the papers it produces are not of great quality. Sakana measures the average cost of these papers at under $15  given plausible looking papers can be created at such a low cost  it can even pose a risk to research integrity with journals  and peer reviewer inboxes flooded with difficult to identify low-quality AI content submissions from people using these agents irresponsibly. However  the results are still impressive  and I see many obvious next steps to improve the agent  e.g.  multimodal capabilities  giving relevant papers to the model via long context  RAG  or fine-tuning and scaling up inference budget for parts of the pipeline.   Why should you care?I think Sakanas implementation is impressive and ties into the power of inference-time scaling laws we discussed in recent weeks. Many people criticize the scale is all you need hypothesis of LLMs march to AGI  but in reality  very few people believe in this on its own  and many different avenues are being pursued for progressing LLM capabilities. We can achieve new capabilities via agent pipelines or research breakthroughs without larger training budgets. In fact  one of the key benefits of the training compute vs capability scaling laws for LLMs is that even risking very small compute budgets on a small scale (and maybe LLM agent managed) experiments can potentially produce insights that can be scaled up 5+ orders of magnitude and integrated into SOTA models.   Sakanas agent does  however  touch on a sensitive subject; many people are resistant to the rush to handing over human work to AI and also very skeptical that we are remotely close to LLMs helping in actual scientific research. In this case  however  we still see Sakanas agent as primarily a human amplifier to aid in incremental research  which will work best with an experienced AI scientist proposing interesting ideas and code bases that they think are a promising research direction. As with any GenAI tools  many people are likely to be lazy and use these agents irresponsibly  however  I can imagine many ways to use an AI scientist agent effectively and diligently. For example  1) Giving it an interesting source idea/theme and codebase to experiment on  2) Using it to generate 100 ideas and running experiments on its self-selected most interesting ideas  generating the papers for all of these and ranking the final results. The human researchers can then review the top-ranked papers  do lots of work on improving and iterating on any interesting experimental results  and perhaps eventually get to something worth publishing in a fraction of the time it would have taken from scratch.   In addition to the scaling laws  there are other things that make ML research particularly well suited to LLM research agent assistants: 1) the high availability of open source code and papers  2) purely cloud-based experiments  3) the agents ML engineers can understand both the agent and the papers it produces to judge quality. Sakana is a respected AI research lab  and it wouldnt surprise me if other leading AI labs like OpenAI and DeepMind were working on similar technologies in-house. It remains to be seen  however  if any of these agents can really be used to aid scientists in truly novel research.    Louie Peters  Towards AI Co-founder and CEO   Since the release of Building LLMs for Production  many of you have asked us: How do we make sure the book is not outdated within months?   These comments are justified. We get it  AI is moving fast; there will be new and better models  better libraries  different tools  etc. But heres our take:   The book teaches many timeless principles and techniques  such as transformer architecture  prompting  deployment  and more.GPT-5 will still hallucinate. Hallucinations will stay as long as we dont reach consciousness. RAG and fine-tuning will remain  even though they will get better and better.The basics of LLMs are worth learning. Just like learning about the perceptron was (and is still) worthwhile. While the code will change  the idea and structure will stay quite similar.We also share a lot of additional up-to-date content/code notebooks/resources on our webpage for the book: towardsai.net/book.   Were already working on the second edition. And your thoughts  your insights  your real experiences with the book  theyre what will make the next version even better. If youve got a minute to drop a review  wed love to hear whats working and what we can do better.   Grab your copy  dive in  and share your thoughts!   Our friends in AI are hiring:CTO and Co-founder at stealth AI company for finance. Towards AI are working on a really exciting startup project in the financial services industry  launching a predictive intelligence assistant that will operate at the intersection of LLMs and data science. The project team has a truly impressive track record in the financial services and consulting industries; the founder has been a senior partner with two of the worlds top consulting firms  working with many of the worlds largest financial services firms over a 30-year career. We are now looking for a CTO to join the team full-time as a co-founder. The right individual will have a strong technical background in AI as well as a track record of commercial product development  although not necessarily in financial services. As CTO  you will drive product design  development  and innovation. Just as importantly  you will be a magnet for engineering talent and play a key role in engaging with investors  clients  and strategic partners. If you are looking for a new intellectual and entrepreneurial challenge  working with a fantastic team  please get in touch with us today at louie@towardsai.net!   Our friends at @Mira (Remote) are also hiring a Senior AI Engineer to help build their decentralized AI infrastructure platform.   Hottest News1.xAIs Grok-2 Beta release   xAI has launched Grok-2 Beta  featuring Grok-2 and Grok-2 mini models  now available to users on . Grok-2 demonstrates significant improvements over its predecessor  Grok-1.5  and joins the growing group of GPT-4 class text models and the smaller group of GPT-4v class multimodal models. Grok-2 scores 75.5% on MMLU-Pro  up from Grok-1.5s 51.0%  and even outperforms GPT-4o  which scores 72.6%. In the MMMU benchmark  Grok-2 achieves 66.1%  surpassing Grok-1.5s 53.6% but behind GPT-4os 69.1%. Both models will soon be available through an enterprise API  offering enhanced security and low-latency access globally.   2. Anthropic Introduced Prompt Caching   Prompt caching  which enables developers to cache frequently used context between API calls  is now available on the Anthropic API. Prompt caching reduces costs by up to 90% and latency by up to 85% for long prompts. It is currently available in public beta for Claude 3.5 Sonnet and Claude 3 Haiku.   3. Perplexity Answers 250 Million Questions a Month  Showing Growing Appetite for AI Search   AI search engine Perplexity saw a significant increase in users last month  handling 250 million queries in a month  reaching 500 million in 2023. While it lags behind Googles dominance and has 8.5 billion daily queries  this trend indicates a user shift towards AI-driven search options.   4. Runway ML Has Officially Released Gen-3 Alpha Turbo  the Latest Version of the AI Video Generation Model   After previewing it late last month  Runway ML has officially released Gen-3 Alpha Turbo  the latest version of the AI video generation model that it claims is seven times faster and half the cost of its predecessor  Gen-3 Alpha. Turbo is available for all plans  including a trial for free users. According to its Twitter (X) announcement  more improvements to the model  control mechanisms  and possibilities for real-time interactivity are to come.   5. Open AI Introduced SWE-Bench Verified   OpenAI released a subset of the SWE-Bench benchmark with human verification to more reliably evaluate AI models ability to solve real-world software issues. They worked with 93 software developers experienced in Python to manually screen SWE-bench samples for quality and annotated 1 699 random samples from the SWE-bench test set to produce SWE-bench Verified.   6. Xs New AI Image Generator Will Make Anything From Taylor Swift in Lingerie to Kamala Harris With a Gun   Grok 2  xAIs new chatbot released on Elon Musks platform X  caused some controversy due to its minimal restrictions on user requests. The chatbot currently integrates Black Forest Labs Flux model for image generation but is implemented with far fewer constraints than other providers. While some are concerned that this can risk digital safety and increase AI controversy and regulation  others think AI should be aligned to deliver what its users request and not be trained to circumvent their wishes with top-down rules from its creators.   7. Multion Introduced Agent Q  AI Agents With Planning & Self Healing Capabilities   MultiOn has launched a new type of autonomous AI agent called Agent Q. It is a self-supervised agent reasoning and search framework that can autonomously improve in real environments through self-play and reinforcement learning. It combines technologies such as Monte Carlo Tree Search (MCTS)  AI self-critique  and RLFH  enabling AI to engage in complex multi-step reasoning and decision-making in dynamic environments.   8. Googles Upgraded AI Image Generator Is Now Available   Google has released the latest version of Imagen 3  its AI text-to-image generator  to US users. The tool  which you can access on Googles AI Test Kitchen  is supposed to generate images with better detail  richer lighting  and fewer distracting artifacts compared to Googles previous models.   Seven 5-minute reads/videos to keep you learning1.How To Prune and Distill Llama-3.1 8B to an NVIDIA Llama-3.1-Minitron 4B Model   This is a guide on refining the Llama-3.1 8B language model into a compact 4B version using NVIDIAs structured compression techniques  including weight pruning and knowledge distillation. This approach yields a resource-efficient Llama-3.1-Minitron 4B that delivers high performance on benchmarks while cutting down on computational expenses.   2. Why I Bet on DSPy   DSPy is an open-source framework that facilitates the coordination of multiple LLM calls to tackle complex issues. It offers verifiable feedback to enhance practical solution deployment. The framework is currently improving reliability and user accessibility to strengthen its utility and continued development within the AI community. This article provides insight into how DSPy forces you to think about the problems with LLMs.   3. Review: ChatGPTs New Advanced Voice Mode   ChatGPTs new Advanced Voice Mode enhances speech understanding and production  outperforming predecessors and competitors like Siri and Alexa. In this article  the author reviewed the basics of Advanced Voice Mode and explored a few use cases that underscore the leap-forward nature of this technology.   4. The Workflow of PEFT   PEFT is a method designed to fine-tune large models more efficiently by focusing on a subset of parameters. This blog looks under the hood of the PEFT library to better understand how things work and explores how to create a base model and use it to build a LoRA model.   5. Free Tools Every ML Beginner Should Use   This article highlights some of the essential tools that every beginner  or person willing to get started  with ML should use. It introduces tools such as Jupyter Notebook  Hugging Face and Transformers  Kaggle  and more.   6. A Crash Course of Model Calibration  Part 1   Many experiments have revealed that modern neural networks are often not well-calibrated. A model is perfectly calibrated if the predicted probabilities of outcomes align closely with the actual outcomes. This article explores how to make ML models reflect true probabilities in their predictions.   7. Synthetic Data Solves AIs Biggest Problem   This article discusses how synthetic data is a useful application of AI technology already delivering real  tangible value to customers. Unlike fake data  synthetic data supports data-driven business systems throughout their lifecycle  mainly where ongoing access to production data is impractical or ill-advised.   Repositories & ToolsQwen 2 is the official repository of Qwen2-Audio chat & pretrained large audio language model proposed by Alibaba Cloud.Deep Live Cam allows real-time face swap and one-click video deepfake with only a single image.LongWriter dataset contains 6 000 SFT data with ultra-long output ranging from 2k-32k words.SWE Agent takes a GitHub issue and tries to automatically fix it using GPT-4 or your LM of choice.Fabric is an open-source framework for augmenting humans using AI.MiniCPM-V is a GPT-4V-level MLLM for a single image  multi-image  and video on your phone.Tinygrad is a deep learning framework that is like a blend of PyTorch and micrograd.Top Papers of The Week1. Imagen 3   This is the official paper for Googles Imagen 3  a latent diffusion model that generates high-quality images from text prompts. The paper discusses their quality and responsibility evaluations  issues around safety and representation  and methods used to minimize the potential harm of the models.   2. The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery   Researchers from Sakana AI  Oxford  University of British Columbia  and several other institutions published a paper unveiling the AI Scientist  a pipeline for open-ended scientific research using LLMs.   3. Mutual Reasoning Makes Smaller LLMs Stronger Problem-Solvers   Microsoft Research published a paper introducing rStar  a self-play multi-reasoning approach that improves reasoning capabilities in small language models. rStar uses a generation-discrimination process to decouple the different steps in the reasoning process   4. Causal Agent based on Large Language Model   This paper explores the difficulty of large language models in mastering causal reasoning and addresses the issue by introducing a Causal Agent. This agent  enhanced with causal reasoning techniques and memory components  shows proficiency in tackling various causal problems.   5. Tree Attention: Topology-Aware Decoding for Long-Context Attention on GPU Clusters   The paper presents a topology-aware decoding approach that improves long-context attention in transformer models on GPU clusters. It connects self-attention to energy-based models  leading to parallel GPU computation  significantly faster processing  reduced inter-GPU communication  and lower memory consumption.   6. Model Merging in LLMs  MLLMs  and Beyond: Methods  Theories  Applications and Opportunities   The paper reviews model merging strategies in machine learning  underscoring their cost-effectiveness and minimal resource usage. It introduces a new classification system for these techniques  detailing their use in language models  continual learning  and multi-task learning. It points out existing literature deficits  current obstacles  and potential areas for future study.   7. Med42-v2: A Suite of Clinical LLMs   This paper introduces Med42-v2  an advanced clinical large language model based on the Llama3 architecture. It is tailored for healthcare with specialized data and preference alignment and surpasses its predecessor and GPT-4 in medical query performance.   Quick Links1. Nvidia will train 100 000 California residents on AI in a first-of-its-kind partnership. The program focuses on training students  educators  and workers  supporting job creation and promoting innovation  and using AI to solve challenges that can improve the lives of Californians   2. Midjourney releases a new unified AI image editor on the web. It combines inpainting  outpaining/canvas extension  and more into a single view. The new web editor is now live and available to all users who have created at least ten images on the platform. Users can access this tool by visiting midjourney.com/imagine.   3. Lambda has partnered with Nous Research to launch Hermes 3  a new fine-tuned version of Metas open-source Llama 3.1405 billion parameter large language model (LLM). Hermes 3 offers an unlocked  uncensored  open weights model designed to be highly steerable  enabling users to tailor the models responses to their individual needs.   Whos Hiring in AIMachine Learning Engineer  Generative AI Inference  3+ Years of Experience @Snapchat (New York  NY  USA)   Lead Research Engineer @Thomson Reuters Holdings Inc. (Eagan  MN  USA/Hybrid)   Machine Learning Engineer (C++ & CUDA) @Dedrone (Remote)   Director  AI Red Team  Remote @Optum (Plymouth  MN  USA/Remote)   Head of AI @DESIGNLIBRO INC (Santa Clara  CA  USA)   Account Executive  AI Enablement @Invisible Technologies Inc. (Remote)   AI Trainer  Software Developer @Davidayo (Remote)   Interested in sharing a job opportunity here? Contact sponsors@towardsai.net.   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