Hacker News Board Hacker News, browsed like a classic message board
Live data from Hacker News

We can’t trust AI systems built on deep learning alone

technologyreview.com

Post reply on HN
Print view View original

21–30 of 133 posts

Re: We can’t trust AI systems built on deep learning alone

#21

Post by benogorek »

tachyonbeam wrote: post #16
m12k wrote: post #12

Earlier quoted context omitted.

As much I'm hoping there'll be a breakthrough in AGI, maybe the right approach is the one AlphaGo was using: DL not as the top level decision-making, but plugged into a traditional decision-making algorithm in specific places.

> As much I'm hoping there'll be a breakthrough in AGI I think it probably won't be one breakthrough, but several, over decades. Personally, I'm pretty happy that AGI is taking a long time to materialize. We likely won't see a "fast takeoff scenario" (the computer is learning at a geometric rate !!1). It will likely happen gradually over years (progressively more intelligent, more aware computer systems), and we may…

A business professor told me that cars were entirely incremental innovation all the way from the model T. Just little improvements, one at a time. I don't know if that's true, but I wonder if it will be an apt analogy for AGI - one feature at a time, and older attempts at it just look outdated.

Re: We can’t trust AI systems built on deep learning alone

#22

Post by gdubs »

So, I’ve been reading articles on this and I think I have a fuzzy idea of some of these solutions would entail. But what I’m hung up on is this: if Deep learning is about coming up with solutions to problems that are too hard for humans, how do we hope to understand the rationale behind whatever solutions the machine comes up with?

Re: We can’t trust AI systems built on deep learning alone

#23

Post by lonelappde »

benogorek wrote: post #21
tachyonbeam wrote: post #16

Earlier quoted context omitted.

> As much I'm hoping there'll be a breakthrough in AGI I think it probably won't be one breakthrough, but several, over decades. Personally, I'm pretty happy that AGI is taking a long time to materialize. We likely won't see a "fast takeoff scenario" (the computer is learning at a geometric rate !!1). It will likely happen gradually over years (progressively more intelligent, more aware computer systems), and we may…

A business professor told me that cars were entirely incremental innovation all the way from the model T. Just little improvements, one at a time. I don't know if that's true, but I wonder if it will be an apt analogy for AGI - one feature at a time, and older attempts at it just look outdated.

https://rework.withgoogle.com/blog/the-roofshot-manifesto/

Re: We can’t trust AI systems built on deep learning alone

#24

Post by jotakami »

benogorek wrote: post #21
tachyonbeam wrote: post #16

Earlier quoted context omitted.

> As much I'm hoping there'll be a breakthrough in AGI I think it probably won't be one breakthrough, but several, over decades. Personally, I'm pretty happy that AGI is taking a long time to materialize. We likely won't see a "fast takeoff scenario" (the computer is learning at a geometric rate !!1). It will likely happen gradually over years (progressively more intelligent, more aware computer systems), and we may…

A business professor told me that cars were entirely incremental innovation all the way from the model T. Just little improvements, one at a time. I don't know if that's true, but I wonder if it will be an apt analogy for AGI - one feature at a time, and older attempts at it just look outdated.

Perhaps, although the automobile has always had a clear explicit purpose: convert potential energy into rotational energy in a controlled manner. It’s hard to identify such a singular purpose for AGI.

Re: We can’t trust AI systems built on deep learning alone

#25

Post by ben_w »

gdubs wrote: post #22

So, I’ve been reading articles on this and I think I have a fuzzy idea of some of these solutions would entail. But what I’m hung up on is this: if Deep learning is about coming up with solutions to problems that are too hard for humans, how do we hope to understand the rationale behind whatever solutions the machine comes up with?

Sometimes it’s easier to understand a solution then it is to produce the solution in the first place. I don’t need to be Newton or Leibniz to understand calculus.

That said, in general I don’t expect that we could understand any particular solution produced by an AI, be it deep or otherwise, but I do expect it to be possible quite often.

Re: We can’t trust AI systems built on deep learning alone

#26

Post by lonelappde »

roenxi wrote: post #17
Barrin92 wrote: post #9

Earlier quoted context omitted.

there are some surprisingly weak arguments in the text. It's correct to not treat computational resources as constant, but ot treat them as unimportant or negligible is awful as well. Already computational resources are becoming prohibitive with only a few institutions producing state of the art models at high financial cost. If the goal is AGI this might get exponentially worse. Intelligence needs to take resource c…

> Already computational resources are becoming prohibitive with only a few institutions producing state of the art models at high financial cost. That is something of an illusion. Obviously there will be some sort of uneven distribution of computing power; some institutions will have more, some less. The institutions with more power will create models at the limit of what they can do, because that is the best use of…

That article says that doubling flops/$ used to take 1 year and now takes 3. It's open question whether that time gap will confirm to grow. The recent phase shift was due to approaching an asymptote in single core performance, and switching to optimizing multicore overhead. The low hanging fruit there will be consumed as well.

Re: We can’t trust AI systems built on deep learning alone

#27

Post by dreamcompiler »

gdubs wrote: post #22

So, I’ve been reading articles on this and I think I have a fuzzy idea of some of these solutions would entail. But what I’m hung up on is this: if Deep learning is about coming up with solutions to problems that are too hard for humans, how do we hope to understand the rationale behind whatever solutions the machine comes up with?

A lot of DL is about teaching computers to solve problems that are easy for humans (like driving and recognizing your grandmother) but for which humans have a tough time explaining how they do it.

The holy grail of neural nets has always been to build a simulation of the brain, figure out how it works, and apply that knowledge to how the human brain might work.

We're not there yet but progress has been made. Eventually we'll understand NNs well enough to explain not only themselves but also human brains. In any case we have no choice because we cannot deploy NNs in life critical situations until we understand how they work, because that's the only way to understand how they fail.

Re: We can’t trust AI systems built on deep learning alone

#28

Post by jotakami »

keithyjohnson wrote: post #7

Understanding a sentence is fundamentally different from recognizing an object. But people are trying to use deep learning to do both. I agree with most of the article but I think this^^ skips over the different types of networks used to solve perception and language problems. A CNN is very different from say, word2vec, which isn't a very deep network at all.

Perhaps the difference is in the nature of the information that is being probed and its larger context? Visual imagery often provides almost all of its own context, but the “meaning” of a sentence can be radically different depending upon its source. Humans produce words, so you almost need a working theory of mind to fully understand them. None of that context will ever make it into word2vec.

Re: We can’t trust AI systems built on deep learning alone

#29

Post by dreamcompiler »

wodenokoto wrote: post #18

In this context, what is classical AI? An SVM? A markov model? A large context free grammar with a dictionary?

Yes. Or rules, logic, or other symbolic system. AI has always been divided into two camps: Connectionist and not. Current "AI" is all connectionist. What we're now calling "Classical AI" is the non-connectionist kind that was prominent in the 60s-80s but fell out of favor in the AI Winter.
Post reply on HN