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Is AI Riding a One-Trick Pony?

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Re: Is AI Riding a One-Trick Pony?

#111

Post by aub3bhat »

AlexCoventry wrote: post #78
aub3bhat wrote: post #69

Earlier quoted context omitted.

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."

The ML community did take the Theory approach trying to prove bounds, SLT/SRM, PAC, etc. and that was an excercise in futility. While I don't deny that there is value to looking under the hood but for a long while the community abandoned any empirical results that didn't fit their paradigm. Between rigorously validating their methods and writing yet another 4 page long proof. A lot of researchers would prefer latter, effectively locking out empirical approaches from most dissemination venues and eventually funding.

Re: Is AI Riding a One-Trick Pony?

#112

Post by moxious »

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.

On the contrary it's a wonderful test because it establishes indistinguishability; namely if you pass it, the whole point is that a person can't tell the computer from the intelligent thing. Meaning that you can't really argue that the computer is different than the intelligent thing. Because how would you tell them apart?

Besides, the original claim was that the goalposts are moving. And even if you hate the Turing test, it's clear that the goalposts are not moving.

Re: Is AI Riding a One-Trick Pony?

#113

Post by white-flame »

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

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 replication to work. Such a thing would be of great benefit to medicine, but not really for AI to even start with until medicine is done reverse engineering it.

Re: Is AI Riding a One-Trick Pony?

#114

Post by Swizec »

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…

> It requires lots memorizing, trial-and-error and experts that often can't fully explain their reasoning.

You just described all of software engineering.

Re: Is AI Riding a One-Trick Pony?

#115

Post by semanticsyo »

joe_the_user wrote: post #109
openasocket wrote: post #108

Earlier quoted context omitted.

It will probably play out like a conversation. A data scientist trains an ML model, and in analyzing the results discovers some intrinsic property or invariant of the problem domain. The scientist can then encode that information into the model and retrain. And that goes on and on, each time providing more accurate results. As an aside, I think it's important that we find a way to examine and inspect how an ML model…

A data scientist trains an ML model, and in analyzing the results discovers some intrinsic property or invariant of the problem domain. The scientist can then encode that information into the model and retrain. And that goes on and on, each time providing more accurate results. Mmmaybe, It's tricky to articulate what pattern the data-scientist could see ... that an automated system couldn't see. Or otherwise, perhaps…

I imagine you need the data science to discern semantically relevant from irrelevant signals. How else do you “tell” your model what to look for? You could easily train for an irrelevant but fitting model.

Re: Is AI Riding a One-Trick Pony?

#116

Post by dmix »

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.

> Evolution

More like vibrating strings?

Re: Is AI Riding a One-Trick Pony?

#117

Post by dahart »

hyperpallium wrote: post #29

Real intelligence is whatever computers can't do yet. Some think this is because we have such an impoverished grasp of intelligence, that it's only when we see a computer actually do it that we realize it doesn't really represent intelligence (logical deduction and inference, rudimentary natural language understanding, expert systems, chess, speech recognition, image recognition). Machines and tools that perform bett…

I assume this is much tongue in cheek, but permit me to take it literally, just for fun. While there have been some public cases of the bar raising, there is no computer or AI yet than can tell you that you asked the wrong question. There's no real evidence that the bar was ever in the right place. We can't worry about the goal posts moving, when we have no idea where the goal posts are supposed to go -- impoverished may be an understatement. The article is correct that "Neural nets are just thoughtless fuzzy pattern recognizers". While everyone's excited that they started to work well on huge data sets, they are simple classifiers that can identify objects for which they've seen many examples. They are demonstrably deficient at extrapolating. Backprop and deep networks were a step forward, but it's just obvious there's a long way to go, regardless of politics.

Re: Is AI Riding a One-Trick Pony?

#118

Post by mlevental »

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…

>using not just calculus but calculus-of-variations, a subject nearly as old as Newtonian mechanics [1]

bridges aren't catenaries and Euler and Lagrange gave us the Euler-Lagrange equations, not Newton.

Re: Is AI Riding a One-Trick Pony?

#119

Post by indescions_2017 »

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…

OTOH, we are merely at circa Year Five into deep reinforcement learning research.

It started as a cluster of 16M CPUs having taught itself to recognize a cat 95% of the time after training on 1B google images.

And we are now at One-Shot Imitation Learning, "a general system that can turn any demonstrations into robust policies that can accomplish an overwhelming variety of tasks".

One Shot Imitation Learning

https://arxiv.org/abs/1703.07326

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