Summary of Barriers to Complexity-theoretic Proofs That Achieving Agi Using Machine Learning Is Intractable, by Michael Guerzhoy
Barriers to Complexity-Theoretic Proofs that Achieving AGI Using Machine Learning is Intractable
by Michael Guerzhoy
First submitted to arxiv on: 10 Nov 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computational Complexity (cs.CC)
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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 recent paper by van Rooij et al. (2024) claims to prove that achieving human-like intelligence through learning from data is impossible due to complexity-theoretic limitations. However, our investigation reveals that this proof relies on an unjustified assumption about the distribution of input-output pairs to the system. We analyze this assumption and identify two fundamental barriers to repairing the proof: the need for a precise definition of “human-like” intelligence and the need to account for the inductive biases inherent in machine learning systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A recent study says it’s impossible to create artificial intelligence that’s as smart as humans. But, we found out that their method is based on an assumption that doesn’t make sense. We looked closer at this assumption and realized there are two main problems: first, we need a clear definition of what “human-like” intelligence means; second, we have to consider how machine learning systems are programmed with certain biases that affect the outcome. |
Keywords
» Artificial intelligence » Machine learning
