Summary of Intelligence at the Edge Of Chaos, by Shiyang Zhang et al.
Intelligence at the Edge of Chaos
by Shiyang Zhang, Aakash Patel, Syed A Rizvi, Nianchen Liu, Sizhuang He, Amin Karbasi, Emanuele Zappala, David van Dijk
First submitted to arxiv on: 3 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Neural and Evolutionary Computing (cs.NE)
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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 abstract proposes investigating how complex rule-based systems influence the capabilities of artificial models trained to predict these rules. The study focuses on elementary cellular automata (ECA), one-dimensional systems generating behaviors ranging from trivial to highly complex. Large Language Models (LLMs) are trained on different ECAs, and their performance is evaluated on downstream tasks like reasoning and chess move prediction. The findings show that rules with higher complexity lead to more intelligent models, while uniform, periodic, or chaotic systems result in poorer performance. The study suggests that intelligence arises from predicting complexity and that exposure to complexity may be sufficient for creating intelligence. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Artificial systems can learn to do things on their own by looking at simple rules. These rules can create really complex behaviors. Scientists wanted to see how well computer models could understand these rules and use them to make good decisions. They used a type of model called a Large Language Model (LLM) and trained it to predict what would happen if they changed the rules. The results showed that when the rules were more complicated, the LLM got better at making decisions. But if the rules were too simple or too chaotic, the LLM didn’t do well. This makes sense because intelligence might come from understanding how things work and using that to make good choices. |
Keywords
» Artificial intelligence » Large language model
