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.
202 karma · joined June 12, 2015
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!
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.
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."