> The chess version beat a leading conventional chess program 28 wins, 0 losses, and 72 draws. In a not equal fight, and the results are still not published. I'm not claiming that AlphaZero wouldn't win, but that test was pure garbage.
The results were published - https://arxiv.org/abs/1712.01815 I agree AlphaZero had fancier hardware and so it wasn't really a fair fight.
No, these are not full results. There are just 10 example games published. Where is the rest?
The BBC recently requested information about the use of facial recognition from UK police forces. Those that use facial recognition reported false positive rates of >95%. That led some to abandon the systems, others just use it as one form of pre-screening. Mass surveillance with facial recognition is nowhere near levels where it can be used unsupervised. And that's even before people actively try to deceive it. For…
Aha, yeah I saw this in the news - pretty classic use of people horribly misunderstanding statistics and/or misrepresenting the facts. Let's say one person out of 60 million has sauron's ring. I use my DeepMagicNet to classify everyone, and get 100 positive results. Only one is the ringbearer, so I have a 99% error rate. Best abandon ship.
I do think of myself that I can read statistics. They didn't mention it as a false positive rate, that's what I interpreted. I don't have the article here but they said that the system gave out ~250 alerts, of which ~230 turned out to be incorrect. It didn't specify at all how big the database of potential suspects was. The number of scanned faces was ~50-100k each time (stadium). Nevertheless, the 230/250 is the issue here, simply because it destroys trust in the system. If the operator already knows that the chance of this being a false alarm is ~5%, will they really follow up all alerts?
The BBC recently requested information about the use of facial recognition from UK police forces. Those that use facial recognition reported false positive rates of >95%. That led some to abandon the systems, others just use it as one form of pre-screening. Mass surveillance with facial recognition is nowhere near levels where it can be used unsupervised. And that's even before people actively try to deceive it. For…
95% false positive rate is extremely good for surveillance, as the cost of false positive is low (wasted efforts by the police). That means for every 20 people the police investigate, one is a target.
They didn't mention arrests but I think the database used was much larger. Probably all persons of interest. The Met (London Police) has been using experts in face recognition for years and their hit rate is much higher. Those people can also identify a high number of suspects on random CCTV while the software was used in stadiums where people tend to remain at one place for most of the time.
So 95% false positive can be better than nothing but it's nowhere near what trained policemen can do.
YouTube captioning in English works surprisingly well, the improvement over the last few years is huge. It still chokes on proper nouns but in general it mostly works.
I think it's a bit like self-driving cars in the sense that it's good enough to be impressive but not good enough to be actually usable everywhere. Of course self-driving is worse because people seldom die of bad captions. Google's captioning works well when people speak clearly and in English. Google translate works well when you translate well written straightforward text into English. It's impressive but it's got…
What's important to remember, I think, is that we should not compare YouTube auto captions to human made captions, because auto captions were not created as a substitute for human made captions - if it wasn't for auto captioning, all these videos wouldn't get any captions at all. They may never be perfect, but they're not designed to be, they're creating new value on their own. And IMO they crossed the threshold of being usable, at least for English.
> But eventually the general public wises up to the unfulfillment of expectations, and drives their attention elsewhere. Here we have the AI winter. And more importantly, business and government leaders wise up and turn off the money tap.
This is why it's wise for researchers and business leaders to temper expectations. Better a constant modest flow of money into the field than a boom-bust cycle with huge upfront investment followed by very bearish actions.
I think the problem is, that's absolutely against the specific interests of university departments, individual researchers, and newspapers - even if it's in the interest of the field as a whole.
This is a deep, significant post (pardon pun etc). The author is clearly informed and takes a strong, historical view of the situation. Looking at what the really smart people who brought us this innovation have said and done lately is a good start imo (just one datum of course, but there are others in this interesting survey). Deepmind hasn't shown anything breathtaking since their Alpha Go zero. Another thing to co…
>Deepmind hasn't shown anything breathtaking since their Alpha Go zero They went on to make AlphaZero, a generalised version that could learn chess, shogi or any similar game. The chess version beat a leading conventional chess program 28 wins, 0 losses, and 72 draws. That seemed impressive to me. Also they used loads of compute during the training but not so much during play.(5000 TPUs, 4TPUs). Also it got better th…
oh wow, it can play chess. can it efficiently stack shelves in warehouses yet?
> AI winters are a result of a massive disparity between the expectations of the general public and the reality of where the technology currently sits. I think they also happen when the best ideas in the field run into the brick wall of insufficiently developed computer technology. I remember writing code for a perceptron in the '90s on an 8 bit system, 64 k RAM - it's laughable. But right now compute power and data…
I wonder, though, what will happen with the demise of Moore's law... can we simply go with increased parallelism? How much can that scale?
That part will be harder than we can imagine.
Most of the software world will have to move on stuff like Haskell or functional language. As of now bulk(almost all) of our people are trained to program in C based languages.
It won't be easy. There will be a renewal for high demand software jobs.
Can't speak for the other items, but these >translate over 100 languages in Google Translate; >caption over a billion videos in 10 languages on YouTube; barely even work. Yeah, it's a difficult problem but it's not even close to being solved.
YouTube captioning in English works surprisingly well, the improvement over the last few years is huge. It still chokes on proper nouns but in general it mostly works.
A. Cooper had a nice example of this kind: a dancing bear. Sure, the fact that bear dances is very amusing, but let it not distract us from the fact that it dances very very badly.
> But eventually the general public wises up to the unfulfillment of expectations, and drives their attention elsewhere. Here we have the AI winter. And more importantly, business and government leaders wise up and turn off the money tap.
This is why it's wise for researchers and business leaders to temper expectations. Better a constant modest flow of money into the field than a boom-bust cycle with huge upfront investment followed by very bearish actions.
That requires super rational agents in the game theory sense...