“The AI Scientist automates the entire research lifecycle,” Sakana claims. “From generating novel research ideas, writing any necessary code, and executing experiments, to summarizing experimental results, visualizing them, and presenting its findings in a full scientific manuscript.”
According to this block diagram created by Sakana AI, “The AI Scientist” starts by “brainstorming” and assessing the originality of ideas. It then edits a codebase using the latest in automated code generation to implement new algorithms. After running experiments and gathering numerical and visual data, the Scientist crafts a report to explain the findings. Finally, it generates an automated peer review based on machine-learning standards to refine the project and guide future ideas.
According to this block diagram created by Sakana AI, “The AI Scientist” starts by “brainstorming” and assessing the originality of ideas. It then edits a codebase using the latest in automated code generation to implement new algorithms. After running experiments and gathering numerical and visual data, the Scientist crafts a report to explain the findings. Finally, it generates an automated peer review based on machine-learning standards to refine the project and guide future ideas.
Credit:
Sakana AI
Critics on Hacker News, an online forum known for its tech-savvy community, have raised concerns about The AI Scientist and question if current AI models can perform true scientific discovery. While the discussions there are informal and not a substitute for formal peer review, they provide insights that are useful in light of the magnitude of Sakana’s unverified claims.
“As a scientist in academic research, I can only see this as a bad thing,” wrote a Hacker News commenter named zipy124. “All papers are based on the reviewers trust in the authors that their data is what they say it is, and the code they submit does what it says it does. Allowing an AI agent to automate code, data or analysis, necessitates that a human must thoroughly check it for errors … this takes as long or longer than the initial creation itself, and only takes longer if you were not the one to write it.”
Critics also worry that widespread use of such systems could lead to a flood of low-quality submissions, overwhelming journal editors and reviewers—the scientific equivalent of AI slop. “This seems like it will merely encourage academic spam,” added zipy124. “Which already wastes valuable time for the volunteer (unpaid) reviewers, editors and chairs.”
And that brings up another point—the quality of AI Scientist’s output: “The papers that the model seems to have generated are garbage,” wrote a Hacker News commenter named JBarrow. “As an editor of a journal, I would likely desk-reject them. As a reviewer, I would reject them. They contain very limited novel knowledge and, as expected, extremely limited citation to associated works.”
Not a general intelligence
The reason why an AI language model-based system like the AI Scientist cannot currently create meaningful novel research on demand is because LLMs’ “reasoning” abilities are limited to what they have seen in their training data. LLMs can create novel permutations of existing ideas, but it currently takes a human to recognize them as being useful, which means an autonomous system like this (with no human in the loop to recognize and improve upon ideas or direct its efforts) doesn’t work with current AI technology.
