AI Scientist

[@wolpumba4099]

*Summary* * *(**0:00**)* *AI Scientist is a system designed for automated machine learning research.* It can generate research ideas, write code, run experiments, and even write a paper summarizing its findings. * *(**20:00**)* *The system relies heavily on Large Language Models (LLMs) like GPT-4 and Claude.* Different LLMs were tested and compared for their effectiveness in different stages of the process. * *(**21:30**)* *It works by generating a variety of ideas, checking them for novelty, and then running small experiments based on the most promising ideas.* These experiments are often based on modifying an existing code base. * *(**29:30**)* *An LLM-based reviewer is also used to evaluate the generated papers.* This automated reviewer has shown to be effective at mimicking human review processes. * *(**37:30**)* *Concerns about the system include its potential to generate inaccurate or misleading papers, as well as ethical considerations about fully automating scientific research.* For example, one generated paper showed improvement simply by making the model bigger, without any genuine scientific innovation. * *(**47:40**)* *There are also concerns about the limitations of humans to understand and evaluate AI-generated research as AI systems become more advanced.* * *(**11:30**)* *The system highlights a shift from traditional coding to 'flow engineering,' where researchers design workflows and prompts for LLMs to execute.* * *(**1:15:00**)* *While the system shows promise for automating some aspects of research, it also raises important questions about the future role of human scientists and the nature of scientific progress itself.* *In short, AI Scientist is a compelling example of the potential for AI to automate research, but it also raises crucial questions and concerns about its limitations, ethics, and implications for the future of science.* I used Google Gemini 1.5 Pro exp 0801 to summarize the transcript. Cost (if I didn't use the free tier): $0.2134 Time: 87.28 seconds I added a 61 second delay to prevent a rate limit of the free tier. Input tokens: 58785 Output tokens: 725

[@sirishkumar-m5z]

In today's tech world, the position of an AI scientist is becoming more and more important. The way artificial intelligence (AI) is expanding the realm of scientific investigation is truly remarkable.

[@bycloudAI]

had to watch your take cuz the cope around this paper all around is kinda too much anddd good take as always, your analogies are on point

[@tljstewart]

You’re my fav llm 😅

[@aresaurelian]

Interesting. There is much to explore. Grab your packs, let's go.

[@vishalrajput9856]

One thing, LLMs can't come up with new idea in the sense that it doesn't know which one are interesting and which ones are not. It doesn't have a good understanding of feasibility of ideas.

[@Leto2ndAtreides]

That'll change with time. But also, you can prioritize a lot of things by just the projected benefit vs cost of execution. A lot of innovation is just a fair amount of iteration over multople areas... Until you achieve a qualitative change.

[@xiaojinyusaudiobookswebnov4951]

"Feasibility": This can be relatively easy if you have access to some simulation environment. "Interestingness": That is a lot more tricky. You could try to do something like "If the idea is novel, then it's interesting", but what constitutes novel depends on what the model has seen before. The fundamental issue is that what constitutes a "good idea" is not really a property of the idea itself, but of its context: The idea of building a combustion engine would have been useless 5000 years ago since you didn't have metallurgy, materials science, or machining capabilities. The idea of building a car would have been useless 200 years ago since you didn't have roads or paved surfaces.The idea of a self-driving car is probably pretty bad in places that have no infrastructure to support them (no street signs, poorly maintained roads, etc...). You could try to formalize this in something like a multi-agent system, where you have multiple "experts" that argue about the usefulness of an idea... and they all have to agree that the idea (generated by an LLM) is worth pursuing.

[@andydataguy]

​@@xiaojinyusaudiobookswebnov4951 bro dropping some serious alpha here 🔥

[@zzzzzzz8473]

great overview , really appreciate the evaluation of the generated "novel" paper as being an obfuscated "scale is all you need" and the potential for delusion of itself into believing it found something worthwhile . the flaws of humanity are baked into these LLMs , so different then what retrofuturism imagined as pure logic systems . i wonder if the process needs another agent who is a curmudgeon skeptic that points out all the flaws and the other agent is tasked to respond like a thesis defense . lots of potential training data from schmidhuber , " thats just an RNN with extra steps , i did all this 30 years ago "

[@wolpumba4099]

Summary starts at 1:47:43