mappingbabeljc

Hiya, I work at OpenAI. I think the Grover paper is a good place to read about some of this:https://arxiv.org/abs/1905.12616 We're likely publishing more on detecting fine-tuned outputs in the future, also.

This does not have anything to do with OpenAI (though it's a cool initiative.) Source: I work at OpenAI.

Hi there, I work at OpenAI. This isn't related to OpenAI in any official sense, though there are some people involved who either have had links to us in the past, or currently have links to us.

Correct - we saw a similar phenomenon of rapid capability gain via self-play in our Dota 2 work: https://blog.openai.com/more-on-dota-2/

(Disclosure: I write Import AI). Link: http://jack-clark.net/import-ai

Hi there, the weights and model are here: https://github.com/openai/generating-reviews-discovering-sen...

I write a weekly AI newsletter called Import AI which is also cross-published to this WP blog. I try to cover a mixture of fundamental research papers and applied stuff. It also includes some OpenAI updates: https://jack-clark.net/

Hiya, (I'm Jack Clark) - this is a good point. The child probably gets about 50 to several hundred distinct 'frames' of the chicken. Still, a remarkably small number of examples.

You can create environments - it's coming! We'll be releasing many components over next few months.

There was an interesting academic research paper that showed you could train in GTA and transfer over to the KITTI dataset and do ok: https://arxiv.org/abs/1610.01983

We wanted people at NIPS in Barcelona to have something nice to read over their morning coffee and such. [I work at OpenAI - @jackclarksf on Twitter]

It just instituted a bunch of changes which will radically change the magazine and may make it break even. "A person familiar with the matter said the publication, which Bloomberg acquired in 2009, was on pace to lose between $20 million and $30 million this year." http://www.wsj.com/articles/bloomberg-changes-businessweek-l...

Even more interesting - an OpenAI paper on the roughly same subject/technique came out a little earlier (RL2 fast reinforcement learning for slow reinforcement learning https://arxiv.org/pdf/1611.02779.pdf). I think parallel inventions tends to indicate that multiple people have stumbled on a similar good idea at the same time. (disclaimer: I work at OpenAI. v pleased to see these two papers emerge so close together)

Hiya, I wrote the article. What might not be captured here is that this is above all a community of people all working on AI so it's not like anyone is really emotional and/or aggressive about this issue. It's more that it's always better for an ecosystem to have multiple dominant players who are all relatively equal. Torch & Theano people were saying it's non-optimal if TF becomes the majority framework, which is a fair point. Everyone I spoke to for this article said TF was engineered well, albeit a bit of a black box in certain aspects.

Hi there, I wrote the article. We also looked at data on things like number of forks and contributors on GitHub as well as massive rise in questions on StackOverflow (thanks to Delip Rao's great post here http://deliprao.com/archives/168 and invaluable ecosystem analysis from Francois Chollet). These other things were cut for purposes of length and because we felt a mainstream reader (which is more Bloomberg's audience) would be able to understand the 'stars' figure most easily of all metrics.

Obviously I agree with you that this measure isn't definitive, but we felt it was relatively easy to understand. And the rest of the reporting we did for this story bore out the idea that TF has gathered an unusual level of both enthusiasm and commitment in a short amount of time.

Thanks for reading!

they plan to publish more details in a research paper, AFAIK.

One area where Google does seem to avoid this secrecy is in AI: both Google Brain and DeepMind are consistently publishing papers & in many cases putting them up on Arxiv pre-publication.

I'm an optimist in this area - people like Wait But Why and XKCD (eg - https://xkcd.com/thing-explainer/) - show that most complex subjects can be explained in simple language that most people with an average education can understand. (An aside: I recently wrote an article about the use of D-Wave quantum computers on Wall Street and that experience gave me a sense of how tremendously difficult it can be to simple summarize an inherently technical topic. I estimate it took about five hours of study to be able to write a couple of accurate sentences I felt comfortable with.) Whether the general public has the inclination to take the time to understand this stuff is another question entirely!

Hi there, author here -- mostly trying to be accessible. And I think in the tech community it is well understood that robotics is hugely difficult, but it seems like general public pretty much equates progress in software AI with progress in robots, which is clearly not the case. We thought it might be helpful to highlight this to people who aren't hugely technical.

Hiya. Reporter here. On the press briefing call Hassabis said that the single node version won 494 out of 495 games against an array of closed- and open-source Go programs. The distributed version was used in the match against the human and will be used in March. Distributed alphago used in the October match was about 170GPUs and 1200CPUs, he said.

Hiya. Author here.

I'd class some of Apple's main peers as: Google, Microsoft, Facebook, Samsung, Baidu, and, to a lesser extent, Amazon. These are all large consumer technology companies that are trying to develop quite intimate relationships with consumers, whether through phones or services.

Among these companies, Apple's secrecy with regards to AI does make it an apparent outlier. All its peers are, to one extent or another, publishing much more aggressively than it in an apparent attempt to woo some of the most accomplished grad students in the academic community into considering going into industry. (Not mentioned in article, but relevant for this: Samsung has started publishing a few papers, and I'm seeing lots of collaboration between Samsung-affiliated researchers and SK academics pop up on Arxiv. Huawei is coming up as well, via its "Noah's Ark Lab" and some other R&D centers.)

Tl,dr; most companies are v private and publishing so openly is the outlier, but among Apple's peers/significant competitors, there is a tendency towards open publication and interaction with the academic community.

Hiya. For those interested, the RankBrain approach of converting words and phrases into vectors ties directly to Geoff Hinton's more ambitious ideas about AI. He speaks about it a bit from 32 mins in, in this video from the Royal Society in London earlier this year.

Geoff Hinton - "If we can convert a sentence into a vector that captures the meaning of the sentence, then google can do much better searches, they can search based on what is being said in a document. Also, if you can convert each sentence in a document into a vector, you can then take that sequence of vectors and try and model why you get this vector after you get these vectors, that's called reasoning, that's natural reasoning, and that was kind of the core of good old fashioned AI and something they could never do because natural reasoning is a complicated business, and logic isn't a very good model of it, here we can say, well, look, if we can read every English document on the web, and turn each sentence into a thought vector, we've got plenty of data for training a system that can reason like people do. Now, you might not want to reason like people do on the web, but at least we can see what they would think."

https://www.youtube.com/watch?v=IcOMKXAw5VA

Hiya. They wouldn't explicitly confirm that it is word2vec, but everything we discussed indicated it's likely doing something roughly equivalent to word2vec, and is also doing similar conversions for sequences which is likely connected to Sequence to Sequence learning (PDF: http://papers.nips.cc/paper/5346-sequence-to-sequence-learni...). It also links to Geoff Hinton's stuff on Thought Vectors which implicitly involves word2vec.

Thanks for the feedback. As a general news organization we struggle with definitions/scoping for stuff within AI as it's such a new area and we try to write for a broad, albeit informed, readership. I'll keep this in mind for future articles where we classify the two.

Hiya, author here - yes, you are correct. RankBrain is one of hundreds of distinct signals that go into the results page. It just happens to be one with a great deal of influence, which I think demonstrates the surprising generality/adaptability of this type of approach for natural language processing and interpretation.

Tried to get at this in the article (author here), but it's using vectors (think word2vec and seq2seq) to distill meaning and embed words and phrases into a single space that the computer can then use to reason about. From my understanding this is all done on the query end of things, so it's basically letting them do better natural language processing. It also ties into Hinton's work on "Thought Vectors".

Hiya. Author here. They wouldn't tell me. Asked a lot.

Hiya. One of the authors of the article here. We also reported this week that Azure alone likely did about $400m in revenue for Microsoft in June quarter, versus for Amazon on $1.8 billion. http://www.bloomberg.com/news/articles/2015-10-23/microsoft-...

Hiya - I wrote the article, it wasn't from PR. As far as I know the supplier stuff, revenue numbers, signup numbers, are new.

Disclaimer: I wrote the article. I feel the HN community may find some of the business numbers for DigitalOcean and Backblaze interesting.