Can AIs Generate Novel Research Ideas? with lead author Chenglei Si
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What is the central research question of Chenglei Si’s paper?
Hello and welcome to the Cognitive Revolution, where we interview visionary researchers, entrepreneurs, and builders working on the frontier of artificial intelligence. Each week we'll explore their revolutionary ideas, and together we'll build a picture of how AI technology will transform work, life, and society in the coming years. I'm Nathan LeBenz, joined by my co-host, Eric Tornberg. Hello and welcome back to the cognitive revolution. Today my guest is Chung Lee Si, a PhD student at Stanford who's developing ways to use large language models to automate research. He's lead author of a fascinating new paper that asks the question, can large language models generate novel research ideas? This question has become one of the most important in the entire field, as the ability to generate research ideas that are truly worth pursuing, particularly in the domain of AI, has long been considered a key precursor to recursive self improvement loops and a possible intelligence explosion.
Since the early days of GPT four, we've seen several notable attempts to create AI research assistance, including projects like CoScientist from Gabe Gomez's group at CMU, which we did a full episode on, and more recently the AI scientist paper from Japanese company Sakana AI as well. These systems demonstrated capabilities that would have seemed impossible just a couple years ago, including translating natural language instructions to chemistry protocols and using the Semantic Scholar API to assess research ideas for originality. Nevertheless, the question of whether AI systems can produce high value research ideas, true Eureka moments, has remained the subject of fierce debate. Cheng Lei and his collaborators set out to shine light on this subject with an ambitious study.
They asked more than a hundred PhD researchers working in AI for new research ideas, incentivizing quality with cash prizes for the best ideas, and then asked Claude to generate new ideas as well. After processing the text in an attempt to create a level playing field for evaluation, they then had expert reviewers rate all of the ideas without knowing their source. The results? The AI generated ideas scored significantly higher on both novelty and excitement. Now, as with many recent AI results, this paper has become something of a Rorschach test. Those inclined to believe in rapid AI progress see it as a major milestone, while skeptics criticize the methodology, somewhat unfairly in my view, but you can judge for yourself as you listen, and more persuasively in my mind, emphasize that this work, which focuses specifically on prompting techniques for language models, may not generalize to the harder sciences.
Personally, I agree that we cannot confidently project this one result onto other domains. But I find the experimental set appear to be quite well done, the statistically significant results seem credible, and I think Chung Lei's individual observations are worth taking very seriously as evidence too. Everyone listening to this show should be familiar with the AI maxim to look at your data. And nobody has spent more time with the raw outputs than Chung Lei has. So when he reports that nine of his ten favorite ideas from this entire project turned out to be AI generated, and that the AI ideas are generally more out of the box and less grounded in existing work than human ideas, I think we would do well to listen.
Overall, my feeling right now is that we're at a sort of tipping point, where the Claude three point five Sonnet and GPT four O class of models can sometimes, with great effort put into system design and many millions of tokens to burn, sometimes generate meaningful, novel research ideas, but not yet in a way that makes frontier research dramatically more accessible or scalable. The next generation of models, starting with the O1 series, seems to me pretty likely to change that. I've been coding with O1 and Cursor a lot lately, and I've been really struck by how effectively O1 can review my entire code base and my plan for a new feature, and return both a critique of my approach and a recommended alternative that is usually genuinely better.
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Chapters
8 chapters
1
What is the central research question of Chenglei Si’s paper?
0:00–9:46
2
How did the study compare AI‑generated ideas with human‑generated ideas?
9:46–14:47
3
What prompting and retrieval techniques were used to generate AI research ideas?
14:47–20:41
4
How did the team prevent duplicate ideas during large‑scale generation?
20:41–26:27
5
What evaluation metrics were used to score novelty, excitement, feasibility, and effectiveness?
26:27–39:12
6
What were the headline results showing AI ideas outperforming human ideas?
39:12–44:26
7
What insights did the authors gain about diversity versus quality of ideas?
44:26–59:37
8
What are the future directions and open challenges for automated research?
59:37–1:17:35
Speakers
2 identifiedMore from "The Cognitive Revolution"
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