Summary of Artificial Scientific Discovery, by Antonio Norelli
Artificial Scientific Discovery
by Antonio Norelli
First submitted to arxiv on: 18 Nov 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
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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 This thesis explores the fundamental concepts necessary for developing an artificial scientist capable of autonomously generating original research and contributing to human knowledge. The investigation begins with Olivaw, an AlphaGo Zero-like agent that discovers Othello knowledge from scratch but is unable to communicate it. This limitation leads to the development of the Explanatory Learning (EL) framework, which formalizes the problem faced by a scientist when trying to explain new phenomena to peers. The EL prescriptions allow for cracking Zendo, a board game simulating the scientific endeavor. A fundamental insight emerges: artificial scientists must develop their own interpretation of language used to explain findings. This perspective leads to modern multimodal models being seen as interpreters and devises a way to build interpretable CLIP-like models by coupling two unimodal models with little multimodal data and no further training. The thesis concludes by discussing what ChatGPT and its siblings are still missing to become artificial scientists, introducing Odeen, a benchmark that sees LLMs failing to interpret explanations while being fully solved by humans. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research explores the idea of creating an artificial scientist that can generate original research and contribute to human knowledge. The study starts with Olivaw, an agent that learns from scratch but cannot communicate its findings. This limitation leads to a new framework called Explanatory Learning (EL) that helps scientists explain their discoveries to others. By using EL, the researchers are able to solve a game that simulates the scientific process. They also find that artificial scientists need to develop their own way of understanding language to explain their findings. The study concludes by discussing what ChatGPT and its siblings still need to become artificial scientists. |
