AI Timelines – Page 5

Brain performance in FLOPS

The computing power needed to replicate the human brain’s relevant activities has been estimated by various authors, with answers ranging from 1012 to 1028 FLOPS. Details Notes We have not investigated the brain’s performance in FLOPS in

Index of articles about hardware

Hardware in terms of computing capacity (FLOPS and MIPS) Brain performance in FLOPS 2019 recent trends in GPU price per FLOPS Electrical efficiency of computing 2018 price of performance by Tensor Processing Units 2017 trend in

Cost of human-level information storage

It costs roughly $300-$3000 to buy enough storage space to store all information contained by a human brain. Support The human brain probably stores around 10-100TB of data. Data storage costs around $30/TB. Thus it costs roughly $300-$3000 to buy

Costs of information storage

Posted 23 July 2015 Cheap secondary memory appears to cost around $0.03/GB in 2015. In the long run the price has declined by an order of magnitude roughly every 4.6 years. However the rate has declined so much that prices haven’t substantially dropped since 2011 (in 2015).

Information storage in the brain

The brain probably stores around 10-100TB of data.

Conversation with Steve Potter

Posted 13 July 2015 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

Predictions of Human-Level AI Timelines

Note: This page is out of date. See an up-to-date version of this page on our wiki. Updated 5 June 2015 We know of around 1,300 public predictions of when human-level AI will arrive, of

Accuracy of AI Predictions

Updated 4 June 2015 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

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

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