Mark Hammond

Flow has raised a $50M Series B at a $750M valuation, co-led by Antonio Gracias (Valor) and Gavin Baker (Atreides) with Sequoia Capital, Roelof Botha (SpaceX, Block), and more. When I became a mechanical engineer, I wanted to invent. In reality, 90% of the day to day execution work (CAD, excel, sim) was digital manual labour. That’s about to flip. The engineer’s role will change more profoundly than at any point since the invention of CAD, with humans focusing on invention, architecture, tradeoffs and judgement calls, and agents taking the grunt work. Anduril, Joby, Stoke Space, Rivian & many more - Flow is now the default platform for requirements and verification for frontier hardware teams. But it’s not just next-gen anymore. Industry giants including General Motors PPU and Volkswagen & Rivian’s joint venture (RV Tech) are reshaping their core development practices with Flow’s AI. The revolution is just starting. Models are getting better quickly at CAD, analysis, simulation, and tool use. In the next year, AI will move from integrating the work to doing the work. One day, this shift won't just enable us to iterate faster, it will enable us to design a class of products that we could not have dreamt of before. We’re hiring.

Perhaps one day the field will stop chasing the red herring of dataset size - data that has the quality to reliably answer mechanistic questions is the scarce resource for understanding disease, data quantity by itself without any qualifiers is irrelevant. A few bytes representing the result of one clean experiment resting on few assumptions can easily outweigh the causal information content of terabytes of associational data.

[schema]'s saturation of the ARC-AGI-3 public set is only a starting point. There is much more to explore! Full blog: schema-harness.github.io Agent traces: huggingface.co/datasets/sche… Amazing team effort with @guanningzeng, @Jiani_Wang_, @wenjie_ma, @shaofeng_y27736, @ChenyangWa70207, @lustralisk95, @akanazawa, @wodenimoni, @xiuyu_l and @Zanette_ai

Larry Ellison says the Stargate Project will construct the largest computer ever built which will enable AI to create cancer vaccines, personalized medicine and pandemic prevention

I've heard people claim that Sam is just drumming up hype, but from what I've seen everything he's saying matches the ~median view of @OpenAI researchers on the ground.

Claude 3.5 sonnet outperform openai o1 in terms of reasoning. Prompt : Begin by enclosing all thoughts within <thinking> tags, exploring multiple angles and approaches. Break down the solution into clear steps within <step> tags. Start with a 20-step budget, requesting more for complex problems if needed. Use <count> tags after each step to show the remaining budget. Stop when reaching 0. Continuously adjust your reasoning based on intermediate results and reflections, adapting your strategy as you progress. Regularly evaluate progress using <reflection> tags. Be critical and honest about your reasoning process. Assign a quality score between 0.0 and 1.0 using <reward> tags after each reflection. Use this to guide your approach: 0.8+: Continue current approach 0.5-0.7: Consider minor adjustments Below 0.5: Seriously consider backtracking and trying a different approach If unsure or if reward score is low, backtrack and try a different approach, explaining your decision within <thinking> tags. For mathematical problems, show all work explicitly using LaTeX for formal notation and provide detailed proofs. Explore multiple solutions individually if possible, comparing approaches in reflections. Use thoughts as a scratchpad, writing out all calculations and reasoning explicitly. Synthesize the final answer within <answer> tags, providing a clear, concise summary. Conclude with a final reflection on the overall solution, discussing effectiveness, challenges, and solutions. Assign a final reward score.

Introducing The AI Scientist: The world’s first AI system for automating scientific research and open-ended discovery! sakana.ai/ai-scientist/ From ideation, writing code, running experiments and summarizing results, to writing entire papers and conducting peer-review, The AI Scientist opens a new era of AI-driven scientific research and accelerated discovery. Here are 4 example Machine Learning research papers generated by The AI Scientist. We published our report, The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery, and open-sourced our project! Paper: arxiv.org/abs/2408.06292 GitHub: github.com/SakanaAI/AI-Scien… Our system leverages LLMs to propose and implement new research directions. Here, we first apply The AI Scientist to conduct Machine Learning research. Crucially, our system is capable of executing the entire ML research lifecycle: from inventing research ideas and experiments, writing code, to executing experiments on GPUs and gathering results. It can also write an entire scientific paper, explaining, visualizing and contextualizing the results. Furthermore, while an LLM author writes entire research papers, another LLM reviewer critiques resulting manuscripts to provide feedback to improve the work, and also to select the most promising ideas to further develop in the next iteration cycle, leading to continual, open-ended discoveries, thus emulating the human scientific community. As a proof of concept, our system produced papers with novel contributions in ML research domains such language modeling, Diffusion and Grokking. We (@_chris_lu_, @RobertTLange, @hardmaru) proudly collaborated with the @UniOfOxford (@j_foerst, @FLAIR_Ox) and @UBC (@cong_ml, @jeffclune) on this exciting project.

Sebastian Cocioba (@ATinyGreenCell) built a laboratory in his home using machines bought off eBay. His dream is to design flowers. He's already engineered air-purifying houseplants and helped make the world’s first genetically modified Olympic mascot. His story, in photos...

1) there is no early stage funding gap in the UK 2) this is a bit rich from the TTO that has a track record of offering UK spinouts some of the worst terms out there OUI has probably incinerated more potential value than any other technology transfer office in the UK

Excited to share my first work from my internship @SakanaAILabs! We used LLMs to design and implement new preference optimization algorithms for training LLMs, discovering cutting-edge methods! Co-led with @samianholt and Claudio Fanconi. Details in thread 🧵 (1/N)

Quantization hurts! Was playing llama3 8B from @SambaNovaAI and @GroqInc with some financial questions. The @SambaNovaAI api (bf16) get me answer correctly with $9.5 million, whereas @GroqInc (quantized) answer missed a “million” (🫨!) and also gets the number $1.8 wrong #llm #ai

This is an interesting overview of what it is like to actually build production software with today's LLMs. It also shows how weird LLMs are to work with from a software perspective & how much we have to learn. It is why I stress co-intelligence for now. oreilly.com/radar/what-we-le…