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Is AI Riding a One-Trick Pony?

technologyreview.com

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Re: Is AI Riding a One-Trick Pony?

#121

Post by bra-ket »

white-flame wrote: post #113
bra-ket wrote: post #57

Earlier quoted context omitted.

it's really simple 1) learn how the brain works 2) build a simulator most current AI research skips step 1

Medical research hasn't cracked step 1 either, at least not to a point of accurate simulation. Besides, if you could simulate a human brain, you will end up with something that needs to sleep, something with limited and unreliable memory, something that gets bored and distracted, something emotionally needy, etc. Then the extending of this chaotic, messy system is wildly unknown even if we could get a piece-for-piece…

Piece-for-piece replication might not be the right level of abstraction. Blue Brain project is one unfortunate example, on the other hand the current neural nets are stuck with neural model from 1943.

Re: Is AI Riding a One-Trick Pony?

#122

Post by emodendroket »

philipkglass wrote: post #53
emodendroket wrote: post #22

Earlier quoted context omitted.

I feel like people have a harder time accepting these kinds of flaws if they differ wildly from mistakes a human would make.

I agree: humans generally don't tolerate mistakes from machines if the mistakes are not similar to those that humans would make. And people generally don't recognize their own intellectual shortcomings in comparison to others (other human cultures, other non-human animals, machines) as long as those shortcomings are common within one's peer group. It's unremarkable to be unable to memorize long passages in a literate…

It's not totally illogical; it presents real problems. Let's say you wanted to offload content filtering to an AI and have it get rid of sexually explicit or graphically violent images. In this case an AI-based filter that could be fooled by adversarial input much more easily than a human.

Re: Is AI Riding a One-Trick Pony?

#123

Post by droidist2 »

bhntr3 wrote: post #73
philipkglass wrote: post #20

I'm encouraged that so much fruitful work has come out of this one trick. If you can use the same basic framework for image labeling, playing Go, and translating natural languages, I'd say it's a powerful tool with broad applications. I think that there's a kernel of insight to "A real intelligence doesn’t break when you slightly change the problem." But human perception and intelligence are pretty brittle. The metho…

Is life riding a one trick pony?! (Evolution) The title is just clickbait. Finding the simplest formula or equation for a process or phenomenon is the goal of a lot of scientists. Backprop may not be that simplest equation. But actually finding that "one weird trick" to intelligence will in no way be a bad thing when it happens.

All of life seems to be riding the carbon hype train.

Re: Is AI Riding a One-Trick Pony?

#124

Post by stephensonsco »

Yep, it is only one trick. Just like electrifying the world only had that one weird trick of alternating current transported miles with metal cables and the computing world that trick of transistors. Pretty good tricks though.

Understanding will come. Most people are still in the mapping phase. Hinton has moved passed that, and that's ok.

Re: Is AI Riding a One-Trick Pony?

#125

Post by bra-ket »

bra-ket wrote: post #57
gadjo95 wrote: post #50

The most relevant part of the article: David Duvenaud, an assistant professor in the same department as Hinton at the University of Toronto, says deep learning has been somewhat like engineering before physics. “Someone writes a paper and says, ‘I made this bridge and it stood up!’ Another guy has a paper: ‘I made this bridge and it fell down—but then I added pillars, and then it stayed up.’ Then pillars are a hot ne…

it's really simple 1) learn how the brain works 2) build a simulator most current AI research skips step 1

Prof. Hinton has an interesting talk about his new 'capsule' model based on psychology of shape perception: https://www.youtube.com/watch?v=rTawFwUvnLE

Re: Is AI Riding a One-Trick Pony?

#126

Post by SubiculumCode »

Toronto is the fourth-largest city in North America (after Mexico City, New York, and L.A.), and its most diverse: more than half the population was born outside Canada. You can see that walking around. The crowd in the tech corridor looks less San Francisco—young white guys in hoodies—and more international. There’s free health care and good public schools, the people are friendly, and the political order is relatively left-­leaning and stable; and this stuff draws people like Hinton, who says he left the U.S. because of the Iran-Contra affair. It’s one of the first things we talk about when I go to meet him, just before lunch.

The above paragraph taken from the article is an example of why these kinds of articles are frustrating. This is filler. I want meat.

Re: Is AI Riding a One-Trick Pony?

#127

Post by sdenton4 »

openasocket wrote: post #94
joe_the_user wrote: post #74

Earlier quoted context omitted.

It's not that bridges before Newton were bad, it's that Newton gave us the ability to design the strongest possible bridge of a given shape with the materials at hand - using not just calculus but calculus-of-variations, a subject nearly as old as Newtonian mechanics [1]. With this knowledge, what happens when one adds one or two columns to a bridge is now longer "news" the way it might have been before Newtonian mec…

I completely agree with your assessment, but the problem is a bit worse in my opinion. We already have a pretty firm grasp of how different ML systems learn and converge towards a solution in the average case. It's not that we need to understand our neural networks better, it's that we need to understand our problem domain better. We can't determine how well some ML architecture will perform at an object recognition…

It's work remembering that many times progress of held back by ideas that "aren't even wrong." The Perceptrons book wasn't wrong; it just attacked the wrong questions with an inadequate level of certainty in it's assumptions. It may be that we feel that we understand where machine learning is at now, but actually have a huge amount to learn because of inadequacies that we aren't even aware of.

Re: Is AI Riding a One-Trick Pony?

#128

Post by ridgeguy »

dragontamer wrote: post #87
bra-ket wrote: post #57

Earlier quoted context omitted.

it's really simple 1) learn how the brain works 2) build a simulator most current AI research skips step 1

> 1) learn how the brain works 2) build a simulator I disagree that step #1 is important. Consider the "Air-foil", which led to flight. In one sense, its an approximation of the wings of birds and other animals. But ultimately, the discovery that the "Air-foil" shape turns sideways blowing wind into an upward force now called "lift" is completely different from how most people understand bird wings. Bird Wings flap,…

Disclaimer: I have no expertise in AI.

That said, I agree that learning how the brain works seems unimportant and unnecessary. Evolution doesn't know how a brain works, but it's given us Einstein, Michelangelo, and conversations on HN.

It seems really important to learn how to build evolution into attempts at AI, given that evolution is the only known mechanism that leads to what we recognize as intelligence.

Re: Is AI Riding a One-Trick Pony?

#129

Post by ryaniscool »

I think a lot of comments here are missing a tacit point of the article. It took 30 years for this recent "breakthrough" to happen. Nobody has figured out how to use deep learning to develop the next breakthrough in AI beyond deep learning. Therefore, What happens after we run out of novel applications of deep-learning?
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