2017 – Page 2 – AI Impacts

What do ML researchers think you are wrong about?

By Katja Grace, 25 September 2017 So, maybe you are concerned about AI risk. And maybe you are concerned that many people making AI are not concerned enough about it. Or not concerned about the

Automation of music production

Most machine learning researchers expect machines will be able to create top quality music by 2036. Details Evidence from survey data In the 2016 ESPAI, participants were asked two relevant questions: [Top forty] Compose a

Stuart Russell’s description of AI risk

Stuart Russell has argued that advanced AI poses a risk, because it will have the ability to make high quality decisions, yet may not share human values perfectly. Details Stuart Russell describes a risk from

AI hopes and fears in numbers

By Katja Grace, 28 June 2017 People often wonder what AI researchers think about AI risk. A good collection of quotes can tell us that worry about AI is no longer a fringe view: many big names are concerned. But

2016 ESPAI questions printout

This is a list of questions from the 2016 Expert Survey on Progress in AI. Details This page is a printout of questions from the 2016 Expert Survey on Progress in AI provided by the

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

Some survey results!

By Katja Grace, 8 June 2017 We put the main results of our survey of machine learning researchers on AI timelines online recently—see here for the paper. Apologies for the delay—we are trying to avoid spoiling the newsworthiness of the

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

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