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Machine learning has become alchemy (2017) [video]

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

#51

Post by mentalhealth »

module0000 wrote: post #42
turingspiritfly wrote: post #31

Earlier quoted context omitted.

Those massive gains have yet to considered reliable enough to be considered trusthworthy. Would you consider them trusthworthy in court, where lives are at stake? Gains are nice but we are still so far from the essence of AI systems and considering how much resources we are pouring into learning, at this point all of them appear as nothing more than massive fat expensive toys

>> Those massive gains have yet to considered reliable enough to be considered trusthworthy. We're using them at Generic Health Insurance Megacorp in production - lots of enterprises are. If you are in the IT industry, it might be useful to spend some lab time with ML. Possibly you have a misconception of ML and/or confuse it with AI.

Going all in on the "unintelligible black box" model of health insurance, I see.

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

#53

Post by m0zg »

gshrikant wrote: post #39
m0zg wrote: post #22

Earlier quoted context omitted.

But any technology can be deadly if you deploy it widely enough. _WhatsApp_ has resulted in "bodies" and it doesn't have any AI in it at all. First airplanes were basically flying coffins. Cars until early 90s had very little chance of survival in collision above 40mph. Many drugs have serious, sometimes deadly side effects. Quarter of a million people die in hospitals in US alone every year due to medical errors. 10…

Rightfully so. Part of the problem is the way that we speak about the technology - calling it 'artificial intelligence' when the underlying technology does not resemble true intelligence at all. This raises expectations and lets people use AI as a dumping ground for infeasible ideas.

But my point is, even the "real" intelligence isn't so hot in a lot of cases, and AI could surpass it in terms of outcomes on narrow tasks.

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

#55

Post by TrackerFF »

The ML scene was more rigorous 10-15-20 years ago, because it was mostly confined to the world of academia and industrial R&D, and we had much less wide-reaching problems to work towards.

As tech evolves, more data is being generated, which in turn creates more problems that increases the demand for solutions.

To put it short: Solving "real world" problems is more/better rewarded than figuring out the underlying technology, so it's no wonder that we see a larger portion of practitioners that may lack the academic background - and that is understandable.

You could be a ML Ph.D (in Academia) for 5-10 year, earning gov. worker salary while trying to reach the next step - or hack together some ML-based product, until desired accuracy, and cash in 50-100 times the pay.

Now, with that said, I understand that Ali targeted his speech at the NIPS folk - but the industry / academia crossover in ML is massive, and it still stands that much of industry problems are of commercial interest - so the motivation for many is still the same.

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

#56

Post by AlexCoventry »

joe_the_user wrote: post #3

It is simple to say deep learning is based on "alchemy" or "engineering" or whatever it is that isn't strong theory. And it's reasonable to say deep learning has a lot of mathematical and statistical intuitions but doesn't have a strong theory - maybe just doesn't yet have a strong theory or maybe can never get one. So this is by now a standard argument. The standard answers I think have been: 1) Well, we are discove…

I don't think it got as much traction, last time.

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

#57

Post by nurettin »

QML wrote: post #41
Barrin92 wrote: post #21

The problem with ML in my opinion is not that we're missing some sort of fundamental theory, but that there simply is none. ML is essentially fancy pattern matching roughly resembling the human visual system, which is why it happens to be good at tasks related to perception. It's not some master algorithm, it's not going to produce sci-fi AI, and it probably isn't even suited to solve most problems in the realm of in…

Wait, what? Are you confusing ML with AI? The last two courses I took on ML were all theory. For example, when we use linear regression, we make assumptions about the noise — i.i.d. and Gaussian.

Would you describe AI as: automatic categorization and decision making?

If so, then Machine Learning is a part of modeling AI. Regardless of how they are taught in terms of University lectures.

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

#58

Post by wetpaws »

perl4ever wrote: post #36
laretluval wrote: post #30

Earlier quoted context omitted.

There’s going to be people plugging their ears and shouting “but it’s just nonlinear function approximation!” all the way into the singularity.

Human intelligence involves choosing which context to use at any given point. So far, so called AI appears to take an external source of context for granted, which is why it seems to me fundamentally different from "real" intelligence, just like your eyes are different from your entire nervous system.

Every time there is some progress in AI people would yell "but this is not a real AI 'cause humans can..." ( play go/understand pictures/language/semantics/etc, goalpost is always in motion)

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

#59

Post by tnecniv »

QML wrote: post #41
Barrin92 wrote: post #21

The problem with ML in my opinion is not that we're missing some sort of fundamental theory, but that there simply is none. ML is essentially fancy pattern matching roughly resembling the human visual system, which is why it happens to be good at tasks related to perception. It's not some master algorithm, it's not going to produce sci-fi AI, and it probably isn't even suited to solve most problems in the realm of in…

Wait, what? Are you confusing ML with AI? The last two courses I took on ML were all theory. For example, when we use linear regression, we make assumptions about the noise — i.i.d. and Gaussian.

I believe OP is speculating about state-of-the-art ML. The classical techniques, like linear regression, are well studied. However, at this point, there is a race to explain theoretically _why_ deep learning is so successful at generalization when, by classical standards, it shouldn't be. OP is speculating that this quest is in vain.

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

#60

Post by bumby »

solotronics wrote: post #37
turingspiritfly wrote: post #31

Earlier quoted context omitted.

Those massive gains have yet to considered reliable enough to be considered trusthworthy. Would you consider them trusthworthy in court, where lives are at stake? Gains are nice but we are still so far from the essence of AI systems and considering how much resources we are pouring into learning, at this point all of them appear as nothing more than massive fat expensive toys

I have the perspective of an informed layman as a programmer who hasn't messed with ML yet. Wouldn't the "safest" solution be a system with multiple algorithms and a consensus mechanism?

I believe some models do precisely that. Random forest ML as an example tallies "votes" on the outcome. I'm not sure how robustly multiple algorithms have been applied to this voting technique, but it would be an interesting read if anyone has information on it.
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