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Machine learning has become alchemy (2017) [video]

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Re: Machine learning has become alchemy (2017) [video]

#11

Post by m0zg »

We do understand how it works most of the time, but can't predict if certain changes will be beneficial or detrimental. After the fact it's pretty clear what e.g. CNNs do (find manifold transforms which, coupled with nonlinearities minimize error in the layer-wise output distributions when backpropagating loss of the minibatches of the training set). But you can't reliably say "if I add e.g. a bottleneck branch over here my accuracy will go up/down X%".

Fundamentally we have to contend with the fact that human ability to understand complex systems is fairly limited, and at some point it will become an impediment to further progress. Arguably in a number of fields we're past that point already.

Re: Machine learning has become alchemy (2017) [video]

#12

Post by whoisnnamdi »

Sure - perhaps alchemy in the sense that many practitioners are simply throwing things against the wall and seeing what sticks, but not in the sense that there isn't anything real behind all the math or engineering.

Many advancements in machine learning have significant backing in theoretical proofs that a given algorithm will result in unbiased estimates, or will converge to such and such value etc.

On some level, the high amount of experimentation necessary in machine learning is not so much a sign that the practice is faulty in any particular way, but rather, that the world is a complex place. This is especially true when attempting to predict anything that involves human behavior.

Long-story short - I'd cut ML some slack!

Re: Machine learning has become alchemy (2017) [video]

#13

Post by oldgradstudent »

m0zg wrote: post #11

We do understand how it works most of the time, but can't predict if certain changes will be beneficial or detrimental. After the fact it's pretty clear what e.g. CNNs do (find manifold transforms which, coupled with nonlinearities minimize error in the layer-wise output distributions when backpropagating loss of the minibatches of the training set). But you can't reliably say "if I add e.g. a bottleneck branch over…

That's all very cool.

But for safety critical systems you have to understand how these systems work to understand their limitations. You have to know when these techniques succeed, when they fail, and how badly they fail.

Re: Machine learning has become alchemy (2017) [video]

#14

Post by wish5031 »

xamuel wrote: post #8
rwilson4 wrote: post #2

Science is a combination of theory and experiment. Sometimes theory advances faster than experiment, sometimes vice versa. Right now in ML, experiment aka practice is advancing faster than theory. Theory will eventually catch up.

ML is a souped-up version of "draw a line through these points". We can get really efficient at drawing lines through points, but it's not like we'll suddenly realize some deeper fundamental theory about it!

But there actually is a huge amount of theory behind that problem. You can exactly derive the method that finds the best line. You can get error bounds on each of your coefficients and confidence intervals for them. You can alter the strength of your assumptions (e.g. about distribution of errors, homoskedacity, and so on) and see how it affects your model. You can add L1 or L2 regularization, both of which also have solid theoretical grounding. And so on.

All of these things help make your model more robust and give you greater confidence in it, which will be important if we want to put ML in, say, healthcare or defense. But you don’t get as much of this theory with more complex ML models, and certainly not with neural nets. Good luck trying to get a confidence interval for the optimal value of a weight in your net, much less interpreting it.

Re: Machine learning has become alchemy (2017) [video]

#15

Post by chrishare »

rwilson4 wrote: post #2

Science is a combination of theory and experiment. Sometimes theory advances faster than experiment, sometimes vice versa. Right now in ML, experiment aka practice is advancing faster than theory. Theory will eventually catch up.

I think a valid concern is that ML methods are being applied in critical, real-life scenarios without some practitioners being aware of flaws (bias, adversarial attacks, privacy issues) and without any theoretic safetynet that helps them reason about how these systems will behave. James Mickens discussed this recently in a keynote: https://www.usenix.org/conference/usenixsecurity18/presentat...

Maybe the only way to make steady progress here is to blaze ahead and rely on empirical evidence, whilst the theory is inevitably fleshed out. That's often the counter argument - that we do not know how the brain works, but rely on it nonetheless.

Re: Machine learning has become alchemy (2017) [video]

#16

Post by m0zg »

oldgradstudent wrote: post #13
m0zg wrote: post #11

We do understand how it works most of the time, but can't predict if certain changes will be beneficial or detrimental. After the fact it's pretty clear what e.g. CNNs do (find manifold transforms which, coupled with nonlinearities minimize error in the layer-wise output distributions when backpropagating loss of the minibatches of the training set). But you can't reliably say "if I add e.g. a bottleneck branch over…

That's all very cool. But for safety critical systems you have to understand how these systems work to understand their limitations. You have to know when these techniques succeed, when they fail, and how badly they fail.

Do you feel like you understand human limitations in these critical systems? Are humans suitable? Would AI be suitable if it performs statistically better than humans?

Re: Machine learning has become alchemy (2017) [video]

#17

Post by Retric »

whoisnnamdi wrote: post #12

Sure - perhaps alchemy in the sense that many practitioners are simply throwing things against the wall and seeing what sticks, but not in the sense that there isn't anything real behind all the math or engineering. Many advancements in machine learning have significant backing in theoretical proofs that a given algorithm will result in unbiased estimates, or will converge to such and such value etc. On some level, t…

Alchemists could do a lot, making gunpowder for example is non trivial. They simply worked from a bad model with little understanding of what was going on.

Consider, lead and gold are very similar substances and chemistry lets you transform many things into other things so it must have seemed very possible. Unfortunately, I suspect the current AI movement is in a very similar state even if they can do a lot of things that seems magical it’s built on a poor foundation. Resulting in people mostly just trying stuff and see what happens to work without the ability to rigorously predict what will work well on a novel problem.

Re: Machine learning has become alchemy (2017) [video]

#18

Post by oldgradstudent »

m0zg wrote: post #16
oldgradstudent wrote: post #13

Earlier quoted context omitted.

That's all very cool. But for safety critical systems you have to understand how these systems work to understand their limitations. You have to know when these techniques succeed, when they fail, and how badly they fail.

Do you feel like you understand human limitations in these critical systems? Are humans suitable? Would AI be suitable if it performs statistically better than humans?

> Would AI be suitable if it performs statistically better than humans?

In general yes, but it might depend on the pattern of failure - if your self-driving cars hunts me or my family personally, I might have problem with that.

But how can you determine that without releasing it to the wild and waiting for bodies? Worse, say you have a safe system, but you need to modify the network (to fix some bug). How can you determine that the new system is safe enough to put on the road?

Re: Machine learning has become alchemy (2017) [video]

#19

Post by xamuel »

whatshisface wrote: post #9
xamuel wrote: post #8

Earlier quoted context omitted.

ML is a souped-up version of "draw a line through these points". We can get really efficient at drawing lines through points, but it's not like we'll suddenly realize some deeper fundamental theory about it!

The reason you don't expect to see a deep fundamental theory of drawing a line through a few points is because you can always do it. ML doesn't always work, and sometimes it is harder to get working than other times. What's going on?

You can always draw a line through the points, but it isn't always a good approximation. If the points are inherently bunched around a line, then a line through them will approximate them well. If they're a big random cloud, then the line won't. It's the exact same way in ML, except the points are in n-dimensional space and "line" is replaced by "higher-dimensional curve or manifold". Sometimes (e.g. in image processing), the n-dimensional points are inherently bunched around a curve or manifold of the form you're using, and then ML works great. There's nothing deeper going on!

Re: Machine learning has become alchemy (2017) [video]

#20

Post by mistrial9 »

This video is great ! for me, not for the math though.. two things:

* multi-layer, automated "jiggering" with so many components that only a machine can contextualize them, might be great for finding patterns in some sets, but the industry HYPE, the DIRECTION+VELOCITY, and the human manipulation (including lies and pathologies) are gut-level PROBLEM.. and this guy says that! +100

* Alchemy itself rambles and spreads [1] Some variations of Alchemy included a ritualized, internal psychological and psychic experience by the human practitioner.. hard to stabilize, yet not always a bad thing, since you are reading this and are actually one of those ..

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

lastly, the selected slides encourage a student-minded viewer to look up some math and think a bit. Not a bad thing. Thanks for this video and thanks for the talk.

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