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Scientists Increasingly Can’t Explain How AI Works

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Re: Scientists Increasingly Can’t Explain How AI Works

#31

Post by williamcotton »

pixelfarmer wrote: post #22
dmichulke wrote: post #11

Earlier quoted context omitted.

What you're saying is (kinda) my response when people ask me about self-driving cars: How does the car deal with object X on the road? For X=piano, a stack of solar panels, a tank, an airplane, a pile of stones ... No one knows and since the size of the set of X is infinite, no one can appropriately train for it either. That's why we can't have self-driving cars without a general understanding of what objects are and…

Yes and no. Yes, because I agree with what you said. No, because it assumes self-driving has to rely purely on normal vision/cameras, which is just garbage². Think LiDAR and other such means to detect obstacles, but things like that are (currently) pretty expensive, so there is the attempt to just do it with (more or less) normal cameras, which leads to what you said (and I fully agree with that). Aside of the fact t…

LiDAR can't tell you mass of an object as it is headed towards your vehicle.

Imagine you're driving down the road and you see:

1.) A kid throw a rubber playground ball into the road and at your windshield.

2.) A kid heave a bowling ball into the road and at your windshield.

How would you react in each case? How do you distinguish between the two?

As a kid is heaving the bowling ball you can tell that it has a lot more mass by how the kid had to move his body to throw the ball. You learned that because you spent the first few years of your life experiencing the basic physics of reality. You've internalized the motion of the human body.

LiDAR can also have a dirty lens!

Re: Scientists Increasingly Can’t Explain How AI Works

#32

Post by noobermin »

joxel wrote: post #13

It's quite easy to explain. You take a bunch of tensors and multiply them by a bunch of tensors. Humans can't wrap their heads around multiple tensors being multiplied together and never will. It's not a problem with AI. It's a problem with humans. It's not AIs fault that we can understand F=ma but can't understand 50 tensors being stacked.

I can't believe you think the tediousness of a calculation means "humans can't understand it." You should look into multi loop calculations in quantum field theory.

Re: Scientists Increasingly Can’t Explain How AI Works

#33

Post by FartyMcFarter »

mdp2021 wrote: post #17
FartyMcFarter wrote: post #7

> The people who develop AI are increasingly having problems explaining how it works and determining why it has the outputs it has. I don't think this is anything new. This was already the case 20+ years ago with chess-playing computers. In the mid-90s, Deep Blue was evaluating 200 million chess positions per second. How do you explain the resulting moves? Obviously we know they were the result of a deep minimax-styl…

> anything new It is nothing new. It is called (at least) "the problem of transparency". The chess context is probably not the best, because many systems allow a lengthy complex explanation of the response (I cannot remember now the exact workings of Deep Blue - it has been a while last time I met the full info). It is a real problem in general, because we may not just want responses but we may want " to learn someth…

> And of course, "understanding" the engine is required to advance it.

I don't think so. People are clearly making huge advances in machine learning even though they can't explain the systems made 10 years ago, let alone more recent ones. There's a lot of trial and error / cargo-culting involved. Some researchers go as far as comparing machine learning research to Alchemy:

https://www.science.org/content/article/ai-researchers-alleg...

> many systems allow a lengthy complex explanation of the response

All computations have this property: you can execute the code by hand for as long as you have time to. But if you do this for long enough you become a mere observer of the computer's steps, which won't give you any intuition as to why the final output is the way it is (unless the algorithm being run is very simple).

Re: Scientists Increasingly Can’t Explain How AI Works

#34

Post by sebzim4500 »

photochemsyn wrote: post #18

The history of scientific development is one of finding patterns in data (think Kepler's studies of Tycho Brahe's accurate astronomical observations) - yes, AI excels at that - but then expressing those patterns in terms of simple mathematical equations: orbits are elliptical to a first approximation (not counting other graviational bodies than the sun and a planet), and the sun->planet vector sweeps out equal areas…

True but the vast majority of humans couldn't do that either.

Re: Scientists Increasingly Can’t Explain How AI Works

#36

Post by mdp2021 »

alexpetralia wrote: post #29
mdp2021 wrote: post #17

Earlier quoted context omitted.

> anything new It is nothing new. It is called (at least) "the problem of transparency". The chess context is probably not the best, because many systems allow a lengthy complex explanation of the response (I cannot remember now the exact workings of Deep Blue - it has been a while last time I met the full info). It is a real problem in general, because we may not just want responses but we may want " to learn someth…

I suppose this is why it is called "machine learning" and not "human learning"!

We deal with intelligence to either solve problems, or to benefit from intelligence to advance our own.

If you valued technical advance with a reduced concern towards civilization, ie. power over wisdom, ie. more "how" and less "why", you would get - well, for example, the present situation, that some consider "strongly suboptimal".

Some said that there have been two big branches of science fiction, that revolving around power (spaceships etc.) and that revolving around information, and that we have been just lucky to have reached the latter.

Re: Scientists Increasingly Can’t Explain How AI Works

#37

Post by mdp2021 »

FartyMcFarter wrote: post #33
mdp2021 wrote: post #17

Earlier quoted context omitted.

> anything new It is nothing new. It is called (at least) "the problem of transparency". The chess context is probably not the best, because many systems allow a lengthy complex explanation of the response (I cannot remember now the exact workings of Deep Blue - it has been a while last time I met the full info). It is a real problem in general, because we may not just want responses but we may want " to learn someth…

> And of course, "understanding" the engine is required to advance it. I don't think so. People are clearly making huge advances in machine learning even though they can't explain the systems made 10 years ago, let alone more recent ones. There's a lot of trial and error / cargo-culting involved. Some researchers go as far as comparing machine learning research to Alchemy: https://www.science.org/content/article/ai-r…

> "understanding" the engine is required to advance it

What I meant was more on the lines of "LeCun proposed Convolutional NN recognizing a promising paradigm", or "our prespective on Artificial Vision changed as we realized that the ANNs recognized textures instead of figures".

> All computations

And what I meant there was that in the chess systems of topic the calculations performed have been "as if symbolic", ie. reasoning on branches of consequences of the type "what could happen if I move this there" - even when adopting ANNs in some parts of the architecture -, as opposed to, say, "computing weights". That is the level "of machine code", not that "of electronics".

Re: Scientists Increasingly Can’t Explain How AI Works

#38

Post by sebzim4500 »

dmichulke wrote: post #11
croes wrote: post #4

To me the problem of AI is not how it works but how reliable it works. Do we have a way to test AI to prevent corner cases which could lead to catastrophic results?

What you're saying is (kinda) my response when people ask me about self-driving cars: How does the car deal with object X on the road? For X=piano, a stack of solar panels, a tank, an airplane, a pile of stones ... No one knows and since the size of the set of X is infinite, no one can appropriately train for it either. That's why we can't have self-driving cars without a general understanding of what objects are and…

I don't see why self-driving cars should have to be perfect though. I would argue that rationally I should start using self-driving cars as soon as they are better at driving than I am.

The fact there are edge cases that I handle better than the AI is not necessarily disqualifying, so long as the AI is so much better at the rest of the cases that on average the AI is safer. For example, the fact that I handle a piano that has been left in the middle of a freeway better than an AI might matter less than the fact that the AI responds 100ms faster when a car swerves into our path.

Re: Scientists Increasingly Can’t Explain How AI Works

#39

Post by p0pcult »

fallingknife wrote: post #10
Xcelerate wrote: post #5

There is a somewhat widespread implicit assumption that having sufficient intelligence to develop increasingly sophisticated machine learning implies that we also have sufficient intelligence to develop an “intuitive” understanding of how it works. I think that assumption is totally wrong, and trying to reconcile the two is probably a distraction and a waste of time. Evolution led to human intelligence just fine on i…

What people are really saying, but never will admit, is that they want AI to mirror their own biases, not to have none. That's the reason they are "cleaning" the input data.

I think with this response, you reveal more about yourself and your worldview, than any general truth about others.

Re: Scientists Increasingly Can’t Explain How AI Works

#40

Post by kriro »

I don't see a problem with not being able to explain how something works as long as it's not failing empirically. Scientists also cannot explain how humans work in detail and yet humans are "allowed" to do many things and make many decisions that cannot be fully explained.

The question for me is...if I have an AI system that outperforms humans empirically, why do I need to understand how it works to use it? In fact it is ethically problematic (at least for me) to not use a medical AI that outperforms doctors due to liability issues or claims of not being certain how it works. Oftentimes the only question is "what if something goes horribly wrong" but th hidden cost of letting humans do the task at 85% instead of some AI that could do it at 90% is rarely considered.

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