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Is AI Riding a One-Trick Pony?

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Re: Is AI Riding a One-Trick Pony?

#71

Post by bra-ket »

danieltillett wrote: post #63
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

In defence of AI researchers 1 is very, very hard and to the best of our knowledge there is not one way the brain works. The brain is a complex, cobbled together set of systems all using different ways of problem solving.

most AI researchers have never opened a textbook on cognitive psychology or neurobiology , or any of these 'soft' sciences.

how do you plan to build artificial intelligence with no model of intelligence, without learning about important experiments in learning and memory , it's the complete ignorance that drives me crazy.

AI is not for specialists.

Re: Is AI Riding a One-Trick Pony?

#72

Post by hyperpallium »

kevin_thibedeau wrote: post #58
moxious wrote: post #46

Earlier quoted context omitted.

The unrestricted Turing test has been around since the 1950s as a test that hasn't changed. No one is moving the goalposts, I think it's rather the opposite. Every ten years computers learn a new trick or two and people rush to claim that this time, it's intelligent.

The Turing test is all a smoke and mirrors game. Q&A interactions say nothing about underlying self-directed initiative. Acting intelligent doesn't make it so just as a thespian doesn't become a real Hamlet by playing the role.

How do you know when you're talking to a real Hamlet?

BTW I hadn't heard your Hamlet counter before, and I like it. A similar one might be: just because someone sold you the Brooklyn Bridge doesn't mean you own it. The flaw is there are other ways of checking those; for intelligence, there are none. Behaviour is it (at least, so far... still awaiting a non-behavioural definition of intelligence).

Re: Is AI Riding a One-Trick Pony?

#73

Post by bhntr3 »

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.

Re: Is AI Riding a One-Trick Pony?

#74

Post by joe_the_user »

TimPC wrote: post #67
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…

Last I checked bridges came before Newtonian Mechanics and it seems strange to argue this wasn't a good thing. Admittedly paper writing wasn't the main mechanism of transmitting knowledge but it's fairly common for human engineering to come before the full theoretical foundations as opposed to after.

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 mechanics.

A stereotypical picture of an engineering approach without scientific knowledge would be a list of ways to do stuff combined with hints about how to vary the approach per-situation. It requires lots memorizing, trial-and-error and experts that often can't fully explain their reasoning. It's easy to believe bridge-building before Newton was like this though I'm not an expert. Present day AI sounds a lot like from what I've read (though I'm not an expert here either).

Edit: And yes, one could argue that the progress Newton ushered in merely replaced one list of models with a higher, more general list of models - yes, but that is how progress gone so far.

[1] https://en.wikipedia.org/wiki/Calculus_of_variations

Re: Is AI Riding a One-Trick Pony?

#75

Post by thephyber »

PaulHoule wrote: post #60

The media talks about nothing but "Deep Learning", however exciting things are happening with ensemble methods, SMT solvers, semantic and model-driven systems, etc. You just don't hear about it.

Ugh. Blaming "the media" is so last millennium. If you are posting on social networking websites or blogs, you are part of "the media". I don't expect "SMT solvers" to lead cable network news and you don't either.

You can always find some media outlet which covers your particular niche topic, but you will never be able to push all niche topics all the time in to the mainstream. In fact, it's human nature to avoid returning to places which cause you the pain of cognitive overload.

Re: Is AI Riding a One-Trick Pony?

#76

Post by sgt101 »

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…

"engineering before physics" is exactly wrong. No one did Engineering before a sophisticated understanding of Physics was achieved. They built bridges and towers, Engineering enables statements to be made about the performance of machines and buildings; it will survive a wind like x, you can do n cycles, do not load the wings in this way.

Re: Is AI Riding a One-Trick Pony?

#77

Post by sgt101 »

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

I think we should be more interested in how the mind works. Much of AI is a simulator of the mind.

Re: Is AI Riding a One-Trick Pony?

#78

Post by AlexCoventry »

aub3bhat wrote: post #69
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…

Humanity used fire for a long time before combustion was understood. Even today Anesthesia is not well understood at biological/physiological level that has not stopped its safe use and innovation through Clinical Trials. Maybe competitions and empiricism are the best approaches to building intelligent systems. Why get caught up in Physics/Math envy?

Hinton isn't saying "Let's stop using fire," but "Let's understand the principles behind fire so we can use them in more sophisticated, informed and powerful ways."

Re: Is AI Riding a One-Trick Pony?

#79

Post by danieltillett »

bra-ket wrote: post #71
danieltillett wrote: post #63

Earlier quoted context omitted.

In defence of AI researchers 1 is very, very hard and to the best of our knowledge there is not one way the brain works. The brain is a complex, cobbled together set of systems all using different ways of problem solving.

most AI researchers have never opened a textbook on cognitive psychology or neurobiology , or any of these 'soft' sciences. how do you plan to build artificial intelligence with no model of intelligence, without learning about important experiments in learning and memory , it's the complete ignorance that drives me crazy. AI is not for specialists.

Yes all of this is true. I do think studying how the brain works will provide very useful ideas of what might work in AI. At the very least it is a very interesting area to learn about.

Re: Is AI Riding a One-Trick Pony?

#80

Post by TimPC »

joe_the_user wrote: post #74
TimPC wrote: post #67

Earlier quoted context omitted.

Last I checked bridges came before Newtonian Mechanics and it seems strange to argue this wasn't a good thing. Admittedly paper writing wasn't the main mechanism of transmitting knowledge but it's fairly common for human engineering to come before the full theoretical foundations as opposed to after.

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…

Present day AI on the Deep Learning side is a lot like what you describe. We haven't really had the Newtonian foundations yet. The theoretical foundations are quite limited because they are hard to figure out. But the techniques with less established theory work far better on most applications in AI. Redirecting work into areas of AI that have more solid theoretical foundations but worse application performance is not the way forward. I'm all for figuring out hard theoretical foundations but I'm strongly opposed to redirecting research funding to techniques that result in worse applications. I'd also argue modelling the physics isn't always the right approach: vocal tract modelling for speech is an interesting approach that produces much worse speech than state of the art synthesis techniques. It will probably continue to do so for a long time. For vocal tract modelling to produce better synthesis you'd need the physical model to be less lossy in all it's parameterizations and modelling simplifications than any statistical fitting of data. And you'd still need some statistical model of the choices the human makes in producing speech and you'd want that statistical model to work better than the neural network that takes on a larger portion of the problem and replaces the physical model of sound production.
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