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Learning to Love the AI Bubble

sloanreview.mit.edu

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Re: Learning to Love the AI Bubble

#51

Post by sjg007 »

codesushi42 wrote: post #42
6gvONxR4sf7o wrote: post #28

I think this bubble's a weird one in that it's a very different size depending on your point of view. Everything is getting rebranded as AI. Taking averages, grouped by something? That's AI now. Using algorithms to do different things for different people? That's AI now. At least it will be in your press coverage. One thing is AI to the press and public, another thing is AI to investors, yet another thing for nontech…

Well said, AI in practice is just stats rebranded. Neural networks are shiny and new, but they are just an implementation for solutions from stats that have been around for decades. Regression? MSE loss. Now with a neural network trained on MSE loss. Classification? Logistic regression with cross entropy loss. Anomaly detection? Feature extraction? Plenty of people still use PCA, which is nothing new. Autoencoders ma…

Neural networks add computational depth. So I would disagree with the statement that AI is just "stats rebranded". That's about as useful an analogy as saying that statistics in practice is just applied linear algebra.

Re: Learning to Love the AI Bubble

#52

Post by sjg007 »

nostalgk wrote: post #12
lkrubner wrote: post #9

Right now there are 5 comments on this post, and all 5 focus on the issue of how to invest in AI. That suggests something about how Hacker News has changed over the years. There is a larger focus on “what stock can I buy” and somewhat less focus on working with the actual tech.

If I wanted to get started learning about machine learning/AI, where is the best place to do that? I'm a functional programmer who's learned mostly everything about software engineering on the job, and I feel like I don't have the background I need to get started on it; I struggle immensely with math, but have had no problem with my career in software yet thus. I am going to be traveling for a machine learning conven…

Kaggle.com has some competitions and learning modules.

Re: Learning to Love the AI Bubble

#53

Post by codesushi42 »

sjg007 wrote: post #51
codesushi42 wrote: post #42

Earlier quoted context omitted.

Well said, AI in practice is just stats rebranded. Neural networks are shiny and new, but they are just an implementation for solutions from stats that have been around for decades. Regression? MSE loss. Now with a neural network trained on MSE loss. Classification? Logistic regression with cross entropy loss. Anomaly detection? Feature extraction? Plenty of people still use PCA, which is nothing new. Autoencoders ma…

Neural networks add computational depth. So I would disagree with the statement that AI is just "stats rebranded". That's about as useful an analogy as saying that statistics in practice is just applied linear algebra.

Define computational depth. Non linearity? Parallelizable? Computational depth sounds like hyperbole.

You're still approaching stats problems with the same methodologies. Your just using NNs as your optimizer.

Re: Learning to Love the AI Bubble

#54

Post by sjg007 »

codesushi42 wrote: post #53
sjg007 wrote: post #51

Earlier quoted context omitted.

Neural networks add computational depth. So I would disagree with the statement that AI is just "stats rebranded". That's about as useful an analogy as saying that statistics in practice is just applied linear algebra.

Define computational depth. Non linearity? Parallelizable? Computational depth sounds like hyperbole. You're still approaching stats problems with the same methodologies. Your just using NNs as your optimizer.

If you are interested, I would suggest reading up on the https://en.wikipedia.org/wiki/Universal_approximation_theore...

Re: Learning to Love the AI Bubble

#55

Post by 6gvONxR4sf7o »

sjg007 wrote: post #54
codesushi42 wrote: post #53

Earlier quoted context omitted.

Define computational depth. Non linearity? Parallelizable? Computational depth sounds like hyperbole. You're still approaching stats problems with the same methodologies. Your just using NNs as your optimizer.

If you are interested, I would suggest reading up on the https://en.wikipedia.org/wiki/Universal_approximation_theore...

There are theorems like that for polynomials and fourier series and all sorts of other function classes too. They are just as practically relevant (or irrelevant).

Re: Learning to Love the AI Bubble

#56

Post by 6gvONxR4sf7o »

codesushi42 wrote: post #42
6gvONxR4sf7o wrote: post #28

I think this bubble's a weird one in that it's a very different size depending on your point of view. Everything is getting rebranded as AI. Taking averages, grouped by something? That's AI now. Using algorithms to do different things for different people? That's AI now. At least it will be in your press coverage. One thing is AI to the press and public, another thing is AI to investors, yet another thing for nontech…

Well said, AI in practice is just stats rebranded. Neural networks are shiny and new, but they are just an implementation for solutions from stats that have been around for decades. Regression? MSE loss. Now with a neural network trained on MSE loss. Classification? Logistic regression with cross entropy loss. Anomaly detection? Feature extraction? Plenty of people still use PCA, which is nothing new. Autoencoders ma…

Stats is getting rebranded as AI, but that's not the extent of it. You approximately solve a traveling salesman problem and it's AI too now. The label is growing to encompass all kinds of algorithmic decision making. You don't even need data, which is the sine qua non of stats.

Re: Learning to Love the AI Bubble

#57

Post by sjg007 »

6gvONxR4sf7o wrote: post #55
sjg007 wrote: post #54

Earlier quoted context omitted.

If you are interested, I would suggest reading up on the https://en.wikipedia.org/wiki/Universal_approximation_theore...

There are theorems like that for polynomials and fourier series and all sorts of other function classes too. They are just as practically relevant (or irrelevant).

Sure... I mean it is matrices all the way down but the claim that AI (e.g. deep learning) is just applied statistics is disingenuous.

Re: Learning to Love the AI Bubble

#58

Post by carlmr »

scottlocklin wrote: post #8
carlmr wrote: post #5

Earlier quoted context omitted.

If the bubble is only VC money as you suggest, then I don't see it having very strong negative effects outside of Silicon Valley. I'm more worried about global economic crises than VCs losing their money.

I've said this many times when I watch VCs invest in some damn fool thing: there should be a market for selling such investments short. Maybe I should ask a VC for backing for this idea.

There's probably some betting shop for this.
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