Autoscience Institute

50 posts

Autoscience Institute

Autoscience Institute

@AutoScienceAI

Automating AI research

Katılım Ocak 2025
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Autoscience Institute
Autoscience Institute@AutoScienceAI·
Dario: “The biggest thing to watch is this issue of AI systems building AI systems.” Today, Autoscience is announcing $14M in funding by General Catalyst, Perplexity Fund and Toyota Ventures to create autonomous AI research labs. Human AI researchers can't keep up with the pace of new AI research. They’re out of time to run the experiments they want. So we’re building an autonomous AI lab that can. The era of human-scale R&D is over. Machine-scale development has begun. 🚀
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Lillian Ma
Lillian Ma@lillian_ma_·
Emerging autoresearch labs worth following: @AutoScienceAI (@eliot_cowan) One of the cleanest “AI builds AI” bets: agents that invent, test, and ship ML models; not just tune hyperparameters. @intology (@zhouandy_) Zochi and Locus are built for the full research loop: read, hypothesize, code, run thousands of experiments, learn from failures, repeat. @thesis_labs (@eigentopology) YC F25. Treating ML research as a compounding search problem, where every experiment improves the next one instead of dying in a Notion doc. @Recursive_SI (@RichardSocher, @_rockt, @jeffclune) A very ambitious new bet on AI systems that run open-ended experiments on how to make AI systems better. autoresearch with recursive consequences. @EdisonSci (@SGRodriques, @andrewwhite01) A newer FutureHouse spinout bringing AI scientists into biopharma R&D — where “deep research” has to survive real data, real experiments, and real timelines. @HarmonicMath (@tachim, @vladtenev) Math’s version of autoresearch: AI exploring new proofs, with formal verification as the anti-hallucination layer. @readysetpotato (@Nick___Edwards) An AI scientist for actual research workflows — papers, hypotheses, protocols, computational tools, and eventually lab automation. @EvoScientist (@_xizhang) A very early one to watch: multi-agent AI scientists with persistent memory, so failed ideas and experiments improve the next research cycle. @SakanaAILabs (@hardmaru) The original AI Scientist builders. Still one of the best technical follows for research artifacts, open source, and weird ideas that actually run. not exhaustive - add the early teams I’m missing in the comment
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Autoscience Institute
Autoscience Institute@AutoScienceAI·
The future will need more capable AI models. We’re removing the human bottleneck from AI R&D. Autoscience Institute
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Autoscience Institute
Autoscience Institute@AutoScienceAI·
@AnthropicAI just highlighted us in their latest research on AI-driven scientific discovery. This is where things are heading. 🧵
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Neil Natarajan
Neil Natarajan@neilenatarajan·
Super excited for and proud of the @AutoScienceAI team. Revolutionizing AI research with AI “autoscientists”!
Autoscience Institute@AutoScienceAI

Dario: “The biggest thing to watch is this issue of AI systems building AI systems.” Today, Autoscience is announcing $14M in funding by General Catalyst, Perplexity Fund and Toyota Ventures to create autonomous AI research labs. Human AI researchers can't keep up with the pace of new AI research. They’re out of time to run the experiments they want. So we’re building an autonomous AI lab that can. The era of human-scale R&D is over. Machine-scale development has begun. 🚀

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Perplexity Fund
Perplexity Fund@PerplexityFund·
Proud to back @AutoScienceAI -- Eliot and team are building autonomous AI research labs for the era of machine-scale discovery 🚀 from its system medaling at a Kaggle comp to authoring/publishing a peer-reviewed paper, Autoscience is definitely one to watch.
Autoscience Institute@AutoScienceAI

Dario: “The biggest thing to watch is this issue of AI systems building AI systems.” Today, Autoscience is announcing $14M in funding by General Catalyst, Perplexity Fund and Toyota Ventures to create autonomous AI research labs. Human AI researchers can't keep up with the pace of new AI research. They’re out of time to run the experiments they want. So we’re building an autonomous AI lab that can. The era of human-scale R&D is over. Machine-scale development has begun. 🚀

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Autoscience Institute
Autoscience Institute@AutoScienceAI·
Dario: “The biggest thing to watch is this issue of AI systems building AI systems.” Today, Autoscience is announcing $14M in funding by General Catalyst, Perplexity Fund and Toyota Ventures to create autonomous AI research labs. Human AI researchers can't keep up with the pace of new AI research. They’re out of time to run the experiments they want. So we’re building an autonomous AI lab that can. The era of human-scale R&D is over. Machine-scale development has begun. 🚀
Autoscience Institute tweet media
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Chris Abshire
Chris Abshire@ChrisAbshire_·
Unbelievably thrilled to partner with @AutoScienceAI as they work to scale 100,000 AI research scientists to human-level performance and beyond.
Autoscience Institute@AutoScienceAI

Dario: “The biggest thing to watch is this issue of AI systems building AI systems.” Today, Autoscience is announcing $14M in funding by General Catalyst, Perplexity Fund and Toyota Ventures to create autonomous AI research labs. Human AI researchers can't keep up with the pace of new AI research. They’re out of time to run the experiments they want. So we’re building an autonomous AI lab that can. The era of human-scale R&D is over. Machine-scale development has begun. 🚀

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Ahmet Erdem
Ahmet Erdem@a_erdem4·
Probably gets 217th place on a Kaggle competition while burning money equal to the 1st place prize. (It can get 1st place in the first week of a competition though)
Andrej Karpathy@karpathy

I packaged up the "autoresearch" project into a new self-contained minimal repo if people would like to play over the weekend. It's basically nanochat LLM training core stripped down to a single-GPU, one file version of ~630 lines of code, then: - the human iterates on the prompt (.md) - the AI agent iterates on the training code (.py) The goal is to engineer your agents to make the fastest research progress indefinitely and without any of your own involvement. In the image, every dot is a complete LLM training run that lasts exactly 5 minutes. The agent works in an autonomous loop on a git feature branch and accumulates git commits to the training script as it finds better settings (of lower validation loss by the end) of the neural network architecture, the optimizer, all the hyperparameters, etc. You can imagine comparing the research progress of different prompts, different agents, etc. github.com/karpathy/autor… Part code, part sci-fi, and a pinch of psychosis :)

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Keşfet

@eliot_cowan @intology @zhouandy_ @thesis_labs @eigentopology @Recursive_SI @RichardSocher @_rockt