Elon Musk submitted a comment to edge.org a day or so ago, on this article. It was later removed.

The pace of progress in artificial intelligence (I'm not referring to narrow AI) is incredibly fast. Unless you have direct exposure to groups like Deepmind, you have no idea how fast-it is growing at a pace close to exponential. The risk of something seriously dangerous happening is in the five year timeframe. 10 years at most. This is not a case of crying wolf about something I don't understand.

I am not alone in thinking we should be worried. The leading AI companies have taken great steps to ensure safety. The recognize the danger, but believe that they can shape and control the digital superintelligences and prevent bad ones from escaping into the Internet. That remains to be seen...

Now Elon has been making noises about AI safety lately in general, including for example mentioning Bostrom's Superintelligence on twitter. But this is the first time that I know of that he's come up with his own predictions of the timeframes involved, and I think his are rather quite soon compared to most. 

The risk of something seriously dangerous happening is in the five year timeframe. 10 years at most.

We can compare this to MIRI's post in May this year, When Will AI Be Created, which illustrates that it seems reasonable to think of AI as being further away, but also that there is a lot of uncertainty on the issue.

Of course, "something seriously dangerous" might not refer to full blown superintelligent uFAI - there's plenty of space for disasters of magnitude in between the range of the 2010 flash crash and clippy turning the universe into paperclips to occur.

In any case, it's true that Musk has more "direct exposure" to those on the frontier of AGI research than your average person, and it's also true that he has an audience, so I think there is some interest to be found in his comments here.

A preprint of the "The errors, insights and lessons of famous AI predictions – and what they mean for the future" is now available on the FHI's website.

Abstract:

Predicting the development of artificial intelligence (AI) is a difficult project – but a vital one, according to some analysts. AI predictions are already abound: but are they reliable? This paper starts by proposing a decomposition schema for classifying them. Then it constructs a variety of theoretical tools for analysing, judging and improving them. These tools are demonstrated by careful analysis of five famous AI predictions: the initial Dartmouth conference, Dreyfus's criticism of AI, Searle's Chinese room paper, Kurzweil's predictions in the Age of Spiritual Machines, and Omohundro's ‘AI drives’ paper. These case studies illustrate several important principles, such as the general overconfidence of experts, the superiority of models over expert judgement and the need for greater uncertainty in all types of predictions. The general reliability of expert judgement in AI timeline predictions is shown to be poor, a result that fits in with previous studies of expert competence.

The paper was written by me (Stuart Armstrong), Kaj Sotala and Seán S. Ó hÉigeartaigh, and is similar to the series of Less Wrong posts starting here and here.

The Journal of Experimental & Theoretical Artificial Intelligence has - finally! - published our paper "The errors, insights and lessons of famous AI predictions – and what they mean for the future":

Predicting the development of artificial intelligence (AI) is a difficult project – but a vital one, according to some analysts. AI predictions are already abound: but are they reliable? This paper starts by proposing a decomposition schema for classifying them. Then it constructs a variety of theoretical tools for analysing, judging and improving them. These tools are demonstrated by careful analysis of five famous AI predictions: the initial Dartmouth conference, Dreyfus's criticism of AI, Searle's Chinese room paper, Kurzweil's predictions in the Age of Spiritual Machines, and Omohundro's ‘AI drives’ paper. These case studies illustrate several important principles, such as the general overconfidence of experts, the superiority of models over expert judgement and the need for greater uncertainty in all types of predictions. The general reliability of expert judgement in AI timeline predictions is shown to be poor, a result that fits in with previous studies of expert competence.

The paper was written by me (Stuart Armstrong), Kaj Sotala and Seán S. Ó hÉigeartaigh, and is similar to the series of Less Wrong posts starting here and here.