AI "scientists" win their first battle: paper passes ICLR peer review, human research status challenged?

Sakana AI's "AI Scientist" generated paper passed the peer review of the ICLR workshop. What does this mean?

Sakana AI recently announced that papers generated by its "AI Scientist" system had passed peer review in a workshop at ICLR, the top conference in machine learning. This has sparked a wide-ranging discussion about whether AIs are capable of scientific research. Before we delve into the significance of this event, let's take a look at what the Sakana AI team themselves had to say about the paper.

Sakana AI's self-examination: potential, but still work to be done

Instead of being complacent about the passage of this paper, Sakana AI has self-analyzed in a very pragmatic manner. They admit that the paper was only accepted at ICLR's symposium published on the more authoritative main meeting On. In addition, of the three AI-generated papers they submitted, only one was accepted.

Researchers at Sakana AI conducted an internal review of the three papers and frankly noted that none of them met the standards for ICLR conference papers. This suggests that despite the progress AI has made in generating scientific papers, it still has a long way to go before it actually reaches the level of human scientists.

Sakana AI's internal reviews demonstrate its scientific rigor.

More intriguingly, Sakana AI also revealed some of the "low-level mistakes" AI scientists make in the research process, such as citation errors.

AI makes mistakes too, which reminds us that AI is still a tool, not an independent thinker.

Overall, Sakana AI's self-evaluation is objective and dispassionate. They see the potential of AI, but also recognize the current limitations. This pragmatic attitude deserves to be recognized.

AI Scientist Program: Transparency and Collaboration

Sakana AI conducted this research in full collaboration with ICLR leadership and workshop organizers, and with the approval of the University of British Columbia's Institutional Review Board (IRB). This open and transparent approach sets a good example for the AI research community.

Especially worth mentioning is that Sakana AI explicitly states that even if AI-generated papers are accepted, they will be withdrawn before publication. This shows that they are not pursuing the number of papers published, but are more focused on the ethics and norms of scientific research.

How are papers produced?

The reviewed paper was generated by the upgraded version of "AI Scientist" - "AI Scientist-v2". The entire process was done by the AI itself, including proposing hypotheses, designing experiments, writing code, running experiments, analyzing data, writing papers, etc. Human researchers only provided the general direction of the research. Human researchers only provided the general direction of the research.

Sakana AI worked with the ICLR workshop organizers to submit 3 AI-generated papers for double-blind review. The reviewers were aware of the AI-generated papers, but did not know exactly which one. In the end, only one paper scored above the acceptance threshold.

The paper, entitled "Combinatorial regularization: an unexpected obstacle to enhancing the generalization capabilities of neural networks," explores the challenges of AI in scientific research.

A vision for the future: the potential and challenges of AI research

This research by Sakana AI undoubtedly opens a window into the field of AI scientific research. It shows that AI can not only be used for data analysis, image recognition and other tasks, but also participate in the entire process of scientific research, and may even be able to complete scientific research projects independently.

However, we must also realize that AI research is still in its infancy, and the quality of AI-generated papers varies, and there is still a long way to go before they can be published in top journals. In addition, the ethical issues of AI research, data security issues, intellectual property rights issues, etc., all need to be seriously considered and resolved.

Sakana AI's vision is to "create AI that improves AI", which sounds a bit like science fiction, but as technology continues to evolve, who's to say it's not possible? This may sound like science fiction, but as technology continues to evolve, who's to say it's not possible - the future of AI research is full of possibilities, so let's see what happens.

footnotes

1. For more information about Sakana AI's competitors and their research, seeFootnotes to the original textThe
2. For the definition of "peer-reviewed," seeFootnotes to the original textThe
3. For a selection of ICBINB seminars, seeFootnotes to the original textThe