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AI winter is well on its way

blog.piekniewski.info

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Re: AI winter is well on its way

#301
post #297
post #280

Earlier quoted context omitted.

I think chess may actually be the worst lab. Decisions made in chess are done so with perfect knowledge of the current state and future possibilities. Most decisions are made without perfect knowledge.

For chess, the future possibilities are so vast, you can't call them "perfect knowledge" with a straight face.

A human might not be able to, but a computer can. Isn't the explicit reason research shifted to using Go the fact that you can't just number crunch your way through it?

Re: AI winter is well on its way

#302

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…

Bleh, no it isn't.

I am 100% in agreement with the author on the thesis: deep learning is overhyped and people project too much.

But the content of the post is in itself not enough to advocate for this position. It is guilty of the same sins: projection and following social noises.

The point about increasing compute power however, I found rather strong. New advances came at a high compute cost. Although it could be said that research often advances like that: new methods are found and then made efficient and (more) economical.

A much stronger rebuttal of the hype would have been based on the technical limitations of deep learning.

Re: AI winter is well on its way

#303
post #57

Earlier quoted context omitted.

When I saw the Google demo of a CNN using video to split a single audio stream of two guys talking over each other, I became a believer.

Do you have a video of this?

Sorry, I went to bed!

The original google report was discussed here a few weeks ago:

https://news.ycombinator.com/item?id=16813766

Re: AI winter is well on its way

#304

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…

We very well might be in a deep-learning 'bubble' and the end of a cycle... but I don't think this time around it's really the end for a long-while, but more likely a pivot point.

The biggest minds everywhere are working on AI solutions, and there's also a lot in medical/science going on to map brains and if we can merge neuroscience with computer science we might have more luck with AI in the future...

So we could have a draught for a year or two, but there will be more research, and more breakthroughs. This won't be like the AI winters of the past where it lay dormant for 10+ years, I don't think.

Re: AI winter is well on its way

#305

Forget about self driving cars - the real killer application of deep learning is mass surveillance - there are big customer for that (advertising, policing, political technology - we better get used to the term) and its the only technique that can get the job done. I sometimes think that there really was no AI winter as we got other technologies that implemented the ideas: SQL Databases can be seen as an application…

I disagree. Even if there are a number of big customers for mass surveillance, self driving cars fundamentally changes the platform of our economy for everyone.

Re: AI winter is well on its way

#306
AI winter is not "on its way". There is AI hype and anti-AI hype, and then there is actual practice. This article is anti-AI hype, just as bad as its opposite. In practice there are tons of useful applications. We haven't even begun to apply ML and DL to all the problems laying around us, some of which are quite accessible and impactful.

The hype cycle will pass with time, when we learn to align our expectations with reality.

Re: AI winter is well on its way

#307

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…

Bleh, no it isn't. I am 100% in agreement with the author on the thesis: deep learning is overhyped and people project too much. But the content of the post is in itself not enough to advocate for this position. It is guilty of the same sins: projection and following social noises. The point about increasing compute power however, I found rather strong. New advances came at a high compute cost. Although it could be s…

> A much stronger rebuttal of the hype would have been based on the technical limitations of deep learning.

Who's to say we won't improve this though? Right now, nets add a bunch of numbers and apply arbitrarily-picked limiting functions and arbitrarily-picked structures. Is it impossible that we find a way to train that is orders of magnitude more effective?

Re: AI winter is well on its way

#308

Earlier quoted context omitted.

Bleh, no it isn't. I am 100% in agreement with the author on the thesis: deep learning is overhyped and people project too much. But the content of the post is in itself not enough to advocate for this position. It is guilty of the same sins: projection and following social noises. The point about increasing compute power however, I found rather strong. New advances came at a high compute cost. Although it could be s…

> A much stronger rebuttal of the hype would have been based on the technical limitations of deep learning. Who's to say we won't improve this though? Right now, nets add a bunch of numbers and apply arbitrarily-picked limiting functions and arbitrarily-picked structures. Is it impossible that we find a way to train that is orders of magnitude more effective?

To me, it's a bit like the question "Who's to say we wont find a way to travel faster than the speed of light?", by which I mean that in theory, many things are possible, but in practice, you need evidence to consider things likely.

Currently, people are projecting and saying that we are going to see huge AI advances soon. On which basis are these claims made? Showing fundamental limitations of deep learning is showing we have no idea how to get there. How to get there yet, indeed, just we have no idea how to do time travel yet.

Re: AI winter is well on its way

#309
post #258

Earlier quoted context omitted.

> understand images in Google Photos This is one of the areas I’m most enthusiastic about but … it’s still nowhere near the performance of untrained humans. Google has poured tons of resources into Photos and yet if I type “cat” into the search box I have to scroll past multiple pages of results to find the first picture which isn’t of my dog. That raises an interesting question: Google has no way to report failures.…

They collect virtually everything you do on your phone. They probably notice that you scroll a long way after typing cat and so perhaps surmise the quality of search results was low.

Doesn’t that seem like a noisy signal since you’d have to disambiguate cases where someone was looking for a specific time/place and scrolling until they find it?

I’ve assumed that the reason is the same as why none of the voice assistants has an error reporting UI or even acknowledgement of low confidence levels: the marketing image is “the future is now” and this would detract from it.

Re: AI winter is well on its way

#310
As a beginner in deep learning space, I am a bit baffled about the case "You need a lot of computational power". Good models learn fast, so if potential model looks promising on local machine, one can do training on gcloud for 100$ on high end machines. Where am I wrong in this line of thinking?
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