Tentative GPT4′s summary. This is part of an experiment. Up/Downvote “Overall” if the summary is useful/harmful. Up/Downvote “Agreement” if the summary is correct/wrong. If so, please let me know why you think this is harmful. (OpenAI doesn’t use customers’ data anymore for training, and this API account previously opted out of data retention)
TLDR: Convergent evolution, where organisms with different origins develop similar features, can provide insights into deep selection pressures that may extend to advanced AI systems, potentially informing AI alignment work and predicting future AI system properties.
Arguments: The article provides several examples of convergent evolution, including the body shapes of sharks and dolphins, multicellularity, agency, intelligence, and sentience. The article discusses that these convergent properties might provide valuable insights into selection pressures relevant to AI alignment research.
Takeaways: 1. Cases of convergent evolution might point to deep selection pressures, which may help predict advanced AI systems’ properties. 2. Convergent evolution may challenge existing assumptions about AI alignments, which often rely on convergence. 3. Learning from convergent evolution can help AI alignment work by understanding the properties that may extend to advanced AI systems.
Strengths: 1. The article presents strong examples of convergent evolution that can potentially extend to AI systems. 2. Convergent evolution as a concept provides a powerful framework for searching for deep selection pressures relevant to AI alignment. 3. The article explores the relevance of convergent evolution to AI alignment work and suggests fruitful areas of future research.
Weaknesses: 1. The article acknowledges that biology is significantly different from AI, which might limit the direct applicability of convergent evolution insights to AI alignment. 2. Due to the complex interactions of selection pressures and contingencies, it may be challenging to predict which properties will extend to advanced AI systems.
Interactions: The exploration of convergent evolution interacts with AI safety topics like instrumental convergence, natural abstraction hypothesis, and selection theorems. Understanding these interactions can help refine alignment work and predictions about AI systems.
Factual mistakes: The summary accurately represents the content of the article and does not contain factual mistakes or hallucinations.
Missing arguments: The main missing argument in the earlier sections is the importance of explicitly discussing convergence and contingency in AI alignment. This discussion can help refine our understanding of the properties that may extend to advanced AI systems and the selection pressures that shape their development.
Tentative GPT4′s summary. This is part of an experiment.
Up/Downvote “Overall” if the summary is useful/harmful.
Up/Downvote “Agreement” if the summary is correct/wrong.
If so, please let me know why you think this is harmful.
(OpenAI doesn’t use customers’ data anymore for training, and this API account previously opted out of data retention)
TLDR: Convergent evolution, where organisms with different origins develop similar features, can provide insights into deep selection pressures that may extend to advanced AI systems, potentially informing AI alignment work and predicting future AI system properties.
Arguments: The article provides several examples of convergent evolution, including the body shapes of sharks and dolphins, multicellularity, agency, intelligence, and sentience. The article discusses that these convergent properties might provide valuable insights into selection pressures relevant to AI alignment research.
Takeaways:
1. Cases of convergent evolution might point to deep selection pressures, which may help predict advanced AI systems’ properties.
2. Convergent evolution may challenge existing assumptions about AI alignments, which often rely on convergence.
3. Learning from convergent evolution can help AI alignment work by understanding the properties that may extend to advanced AI systems.
Strengths:
1. The article presents strong examples of convergent evolution that can potentially extend to AI systems.
2. Convergent evolution as a concept provides a powerful framework for searching for deep selection pressures relevant to AI alignment.
3. The article explores the relevance of convergent evolution to AI alignment work and suggests fruitful areas of future research.
Weaknesses:
1. The article acknowledges that biology is significantly different from AI, which might limit the direct applicability of convergent evolution insights to AI alignment.
2. Due to the complex interactions of selection pressures and contingencies, it may be challenging to predict which properties will extend to advanced AI systems.
Interactions: The exploration of convergent evolution interacts with AI safety topics like instrumental convergence, natural abstraction hypothesis, and selection theorems. Understanding these interactions can help refine alignment work and predictions about AI systems.
Factual mistakes: The summary accurately represents the content of the article and does not contain factual mistakes or hallucinations.
Missing arguments: The main missing argument in the earlier sections is the importance of explicitly discussing convergence and contingency in AI alignment. This discussion can help refine our understanding of the properties that may extend to advanced AI systems and the selection pressures that shape their development.