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AI Timelines

Conversation with Steve Potter

Participants Professor Steve Potter – Associate Professor, Laboratory of NeuroEngineering, Coulter Department of Biomedical Engineering, Georgia Institute of Technology Katja Grace – Machine Intelligence Research Institute (MIRI) Note: These notes were compiled by MIRI and

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AI Timelines

Predictions of Human-Level AI Timelines

We know of around 1,300 public predictions of when human-level AI will arrive, of varying levels of quality. These include predictions from individual statements and larger surveys. Median predictions tend to be between 2030 and 2055

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Accuracy of AI Predictions

Accuracy of AI Predictions

It is unclear how informative we should expect expert predictions about AI timelines to be. Individual predictions are undoubtedly often off by many decades, since they disagree with each other. However their aggregate may still be quite informative. The main potential reason we

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Accuracy of AI Predictions

Publication biases toward shorter predictions

We expect predictions that human-level AI will come sooner to be recorded publicly more often, for a few reasons. Public statements are probably more optimistic than surveys because of such effects. The difference appears to be less than

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Accuracy of AI Predictions

Selection bias from optimistic experts

Experts on AI probably systematically underestimate time to human-level AI, due to a selection bias. The same is more strongly true of AGI experts. The scale of such biases appears to be decades. Most public AI predictions

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AI Timelines

Group Differences in AI Predictions

AGI researchers appear to expect human-level AI substantially sooner than other AI researchers. The difference ranges from about five years to at least about sixty years as we move from highest percentiles of optimism to the lowest. Futurists appear to be around

AI Timelines

Brain performance in TEPS

Traversed Edges Per Second (TEPS) is a benchmark for measuring a computer’s ability to communicate information internally. Given several assumptions, we can also estimate the human brain’s communication performance in terms of TEPS, and use this