The AI Revolution Hasn’t Happened Yet

Post by useryournoggin » Fri, Apr 20, 2018, 12:18 PM UTC

Talyen42 wrote: post #6

The biggest challenge yet unconquered is getting your "average" business ($1-100m revenue, zero AI knowledge) using ML to help with literally anything they do . I'd wager less than 1% of businesses outside of SV even have a clue where to begin, or what to use it for, my employer included. "Do we hire some AI guys?" We'll need to crack the 1%-using-it mark for me to consider the revolution "begun"...

Post by Retric » Fri, Apr 20, 2018, 12:59 PM UTC

rdlecler1 wrote: post #47

return1 wrote: post #33

Earlier quoted context omitted.

that's a trivial statement, unless he can suggest a different way to do things. Should people have held off building any bridges, until the 20th century arrived ? Building things and science have always evolved side-by-side in a feedback loop.

Actually it’s not a trivial statement. He’s saying that we don’t have enough theory guiding us and instead we just spin up a tensorflow library without having any clue what’s going on underneath the hood. And I don’t mean understanding linear algebra in neural networks, I mean we don’t have a good theory of computation for Artificial neural networks like we do for Boolean electronic circuits. There is a little bit of…

Post by danShumway » Fri, Apr 20, 2018, 1:42 PM UTC

nmca wrote: post #49

danShumway wrote: post #22

Earlier quoted context omitted.

Agreed. Throwing more computing power at the problem is a cheap way to make it look like we're making progress; our AI should be efficient enough to run on limited hardware. Not to say that ML/DL isn't useful, but I think that whatever the next revolution is in AI is likely to come from a completely different direction. I'm not a researcher either, so it's difficult for me to articulate exactly what problems I see, b…

It's worth noting that the training-time inference-time distinction matters here. Most of the fancy tech you've heard of recently (Semantic Seg, Pose, Localisation etc) can be pretty easily optimised for fast inference, and indeed it's not really so much of a research focus because it's so tractable (see MobileNets, v2, etc). Training, however, is still quite daunting.

Post by mietek » Fri, Apr 20, 2018, 2:12 PM UTC

throwaway84742 wrote: post #83

ergothus wrote: post #80

Earlier quoted context omitted.

I grew up hearing "we will eventually get computers that can play chess, but go is exponentially more complicated." People wee talking a century. I don't know that cars will happen soon(5-10 years), but I wouldn't want to bet one way or the other past that. We don't need perfection, we just need to equal humans, and humans are actually pretty bad, we are just used to it.

Humans are _amazing_ because they are able to correct the deficiencies of their perception with high level cognition augmented with memory of past experience. Machines can’t do cognition, and they can’t effectively use past experience either, to say nothing of doing a combination of those two things. Current “AI” is basically function approximation and nothing else. And humans do everything they do in a 20W power env…

Post by candiodari » Fri, Apr 20, 2018, 3:04 PM UTC

z3phyr wrote: post #67

Houshalter wrote: post #55

Earlier quoted context omitted.

What's special about 2018 computing power? Neural networks have been declared dead several times in the past. Because they couldn't do anything interesting with the measly computing power available at the time. Currently the biggest RNNs have a few thousand neurons. Which is absolutely puny compared to even animal brains. Of course this is defending a strawman. I don't know anyone that said it was just a computing po…

The real fallacy is calling neural networks the analogue of animal brains. We still have a lot of research to accomplish in this sphere.