AI Timelines – Page 4 – AI Impacts

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Media discussion of 2016 ESPAI

The 2016 Expert Survey on Progress in AI was discussed in at least 20 media outlets, popular blogs, and industry-specific sites that we know of. Most of them were summaries of the survey findings. Commonly

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Guide to pages on AI timeline predictions

This page is an informal outline of the other pages on this site about AI timeline predictions made by others. Headings link to higher level pages, intended to summarize the evidence from pages below them. This list was complete on 7 April 2017 (here is

Progress in general purpose factoring

The largest number factored to date grew by about 4.5 decimal digits per year over the past roughly half-century. Between 1988, when we first have good records, and 2009, when the largest number to date was factored, progress was

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Trends in algorithmic progress

Algorithmic progress has been estimated to contribute fifty to one hundred percent as much as hardware progress to overall performance progress, with low confidence. Algorithmic improvements appear to be relatively incremental. Details We have not recently examined this topic carefully

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Funding of AI Research

Provisional data suggests: Equity deals made with startups in AI were worth about $5bn in 2016, and this value has been growing by around 50% per year in recent years. The number of equity deals in AI startups globally

2016 Expert Survey on Progress in AI

Published June 2016; last substantial update before Oct 2017 The 2016 Expert Survey on Progress in AI is a survey of machine learning researchers that Katja Grace and John Salvatier of AI Impacts ran in

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Concrete AI tasks for forecasting

This page contains a list of relatively well specified AI tasks designed for forecasting. Currently all entries were used in the 2016 Expert Survey on Progress in AI. List Translate a text written in a newly discovered language

Conversation with Tom Griffiths

Participants Professor Tom Griffiths, Director of the Computational Cognitive Science Lab and the Institute of Cognitive and Brain Sciences at the University of California, Berkeley. Finan Adamson, AI Impacts. Note: These notes were

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Global computing capacity

[This page is out of date and its contents may have been inaccurate in 2015, in light of new information that we are yet to integrate. See Computing capacity of all GPUs and TPUs for a related

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Costs of human-level hardware

Computing hardware which is equivalent to the brain – in terms of FLOPS probably costs between $1 x 105 and $3 x 1016, or $2/hour-$700bn/hour. in terms of TEPS probably costs $200M – $7B, or or $4,700 – $170,000/hour

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