Summary of Artificial Intelligence For Science: the Easy and Hard Problems, by Ruairidh M. Battleday and Samuel J. Gershman
Artificial intelligence for science: The easy and hard problems
by Ruairidh M. Battleday, Samuel J. Gershman
First submitted to arxiv on: 24 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG); Neurons and Cognition (q-bio.NC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper discusses the limitations of current artificial intelligence (AI) approaches in driving scientific discoveries. While AI has excelled in solving optimization problems specified by domain experts, it falls short in generating novel research questions or “hard problems.” The authors argue that this is because AI lacks the ability to continually revise its understanding based on poorly defined constraints, a key aspect of human scientists’ cognitive processes. They propose studying the cognitive science of scientists to develop new computational agents that can automatically infer and update their scientific paradigms. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper talks about how artificial intelligence (AI) has been really good at helping us solve certain problems in science. But it’s not great at coming up with new ideas or questions to investigate, which is an important part of the scientific process. The authors think this is because AI isn’t good at changing its mind or adjusting what it’s doing based on unclear information, something that scientists are able to do naturally. They suggest studying how humans come up with new ideas and use that knowledge to create better AI systems. |
Keywords
» Artificial intelligence » Optimization
