See this video at 39:30 for Yann LeCun giving some comments. He said:

• Human-level AI is not near
• He agrees with Musk that there will be important issues when it becomes near
• He thinks people should be talking about it but not acting because a) there is some risk b) the public thinks there is more risk than there is

Also here is an IEEE interview:

Spectrum: You’ve already expressed your disagreement with many of the ideas associated with the Singularity movement. I’m interested in your thoughts about its sociology. How do you account for its popularity in Silicon Valley?

LeCun: It’s difficult to say. I’m kind of puzzled by that phenomenon. As Neil Gershenfeld has noted, the first part of a sigmoid looks a lot like an exponential. It’s another way of saying that what currently looks like exponential progress is very likely to hit some limit—physical, economical, societal—then go through an inflection point, and then saturate. I’m an optimist, but I’m also a realist.

There are people that you’d expect to hype the Singularity, like Ray Kurzweil. He’s a futurist. He likes to have this positivist view of the future. He sells a lot of books this way. But he has not contributed anything to the science of AI, as far as I can tell. He’s sold products based on technology, some of which were somewhat innovative, but nothing conceptually new. And certainly he has never written papers that taught the world anything on how to make progress in AI.

Spectrum: What do you think he is going to accomplish in his job at Google?

LeCun: Not much has come out so far.

Spectrum: I often notice when I talk to researchers about the Singularity that while privately they are extremely dismissive of it, in public, they’re much more temperate in their remarks. Is that because so many powerful people in Silicon Valley believe it?

LeCun: AI researchers, down in the trenches, have to strike a delicate balance: be optimistic about what you can achieve, but don’t oversell what you can do. Point out how difficult your job is, but don’t make it sound hopeless. You need to be honest with your funders, sponsors, and employers, with your peers and colleagues, with the public, and with yourself. It is difficult when there is a lot of uncertainty about future progress, and when less honest or more self-deluded people make wild claims of future success. That’s why we don’t like hype: it is made by people who are either dishonest or self-deluded, and makes the life of serious and honest scientists considerably more difficult.

When you are in the kind of position as Larry Page and Sergey Brin and Elon Musk and Mark Zuckerberg, you have to prepare for where technology is going in the long run. And you have a huge amount of resources to make the future happen in a way that you think will be good. So inevitably you have to ask yourself those questions: what will technology be like 10, 20, 30 years from now. It leads you to think about questions like the progress of AI, the Singularity, and questions of ethics.

Spectrum: Right. But you yourself have a very clear notion of where computers are going to go, and I don’t think you believe we will be downloading our consciousness into them in 30 years.

LeCun: Not anytime soon.

Spectrum: Or ever.

LeCun: No, you can’t say never; technology is advancing very quickly, at an accelerating pace. But there are things that are worth worrying about today, and there are things that are so far out that we can write science fiction about it, but there’s no reason to worry about it just now.

AI that can't compete in the job market probably isn't a global catastrophic risk.

GiveWell is on the case, and has said it is looking at bio threats (as well as nukes, solar storms, interruptions of agriculture). See their blog post on global catastrophic risks potential focus areas.

The open letter is an indication that GiveWell should take AI risk more seriously, while the Musk donation is an indication that near-term room for more funding will be lower. That could go either way.

On the room for more funding question, it's worth noting that GiveWell and Good Ventures are now moving tens of millions of dollars per year, and have been talking about moving quite a bit more than Musk's donation to the areas the Open Philanthropy Project winds up prioritizing.

However, even if the amount of money does not exhaust the field, there may be limits on how fast it can be digested, and the efficient growth path, that would favor gradually increasing activity.

For some of the same reasons depressed people take drugs to elevate their mood.

Thanks Brian.

By "we" do you mean Gök Us Sibernetik Ar & Ge in Turkey? How many people work there?

"My intuition says it would be hard to mine a few million SNPs, pick the most strongly associated 9500, and have them account for less than .29 of the variance, even if there were no relationship at all."

With sample sizes of thousands or low tens of thousands you'd get almost nothing. Going from 130k to 250k subjects took it from 0.13 to 0.29 (where the total contribution of all common additive effects is around 0.5).

Most of the top 9500 are false positives (the top 697 are genome-wide significant and contribute most of the variance explained). Larger sample sizes let you overcome noise and correctly weight the alleles with actual effects. The approach looks set to explain everything you can get (and the bulk of heritability for height and IQ) without whole genome sequencing for rare variants just by scaling up another order of magnitude.

You can deal with epistasis using the techniques Hsu discusses and big datasets, and in any case additive variance terms account for most of the heritability even without doing that. There is much more about epistasis (and why it is of secondary importance for characterizing the variation) in the linked preprint.