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Learning to Love the AI Bubble

sloanreview.mit.edu

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Re: Learning to Love the AI Bubble

#41

Post by tim333 »

epr wrote: post #37
tim333 wrote: post #29

Earlier quoted context omitted.

Some economists argued the dot com bubble was a net positive for the economy. While tanking dot coms lost money for their investors the positive externalities like funding broadband networks outweighed that. Along the lines that Webvan may have tanked but we ended up with Google and Wikipedia. I imagine likewise that most of the present AI startups will tank but we'll end up with useful AI of serious value.

> Some economists argued the dot com bubble was a net positive for the economy. I am no economist, but virtually any economist I have read would respond to that statement with: Compared to what?

I guess it would be compared to normal levels of investment and valuations of dot com businesses. Though I'll give you it's a little vague. I read something a while back but can't find it.

Re: Learning to Love the AI Bubble

#42

Post by codesushi42 »

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 may get you more mileage, but conceptually work very similarly to PCA for these use cases.

Image data? Use methods from signal processing, also decades old. Convolutions are nothing new, you're just now implementing them with neural networks, and adding a loss function based on what you're trying to predict.

Time series data? You could be better off just sticking to ARIMA. Depends on your use case, but using RNNs may not even work here.

Reinforcement learning is more exciting, and is solving new problems that weren't even being approached before. Same goes for GANs, and unsupervised learning in general stays exciting and fresh.

But most of the applications of AI are ho hum. Just use decades old methods, now implemented with neural networks. At least, sometimes. What has really changed is the amount of data available now, and the ability to process it. Not necessarily the approaches to analyzing it.

Re: Learning to Love the AI Bubble

#43

Post by keiru »

dalbasal wrote: post #33

The interesting thing (to me) about the dotcom bubble, pointed out in one of pg's essays, is how much it got right. It was right about the economic potential of the www. 20 years later and it is as big a deal as 1999 pundits predicted. It was right that the big winners would be very big, very fast. Google, FB Alibaba, amazon... Bigger than any tech company in 99. It was right about winning early and establishing domi…

And the reason why the dotcom timing was off was because the internet hadn't already permeated everyday life. That's when the network effect paid off and consolidation merged startups into giants.

Will the same happen to AI? Uses of AI and "AI" seem to take very well to today's world. Everybody wants a piece of the action be it in ad networks, big data, surveillance, cat ear filters or fake nude pics. People are much more technologically literate and the concept in any form will not land in barren land. It's sometimes even frustrating the way people expect a certain level of intelligence from basic applications (eg. try to implement a search function that is not error-flexible and you'll get angry comments).

Internet paved the way to having lots of redundant data we don't know what to do with. I think the world is all too ready to welcome advances in AI, and it's in fact ignored what it will do to financial systems or personal lives.

Re: Learning to Love the AI Bubble

#45

Post by voldacar »

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…

"AI" is inherently meaningless because it is a moving goalpost. At any point in time, AI essentially means "something normally done by humans that most people don't think computers could do proficiently". When, inevitably, someone programs a computer to do that thing, that thing quickly becomes no longer "AI" and the cycle continues.

Re: Learning to Love the AI Bubble

#46

Post by dalbasal »

keiru wrote: post #43
dalbasal wrote: post #33

The interesting thing (to me) about the dotcom bubble, pointed out in one of pg's essays, is how much it got right. It was right about the economic potential of the www. 20 years later and it is as big a deal as 1999 pundits predicted. It was right that the big winners would be very big, very fast. Google, FB Alibaba, amazon... Bigger than any tech company in 99. It was right about winning early and establishing domi…

And the reason why the dotcom timing was off was because the internet hadn't already permeated everyday life. That's when the network effect paid off and consolidation merged startups into giants. Will the same happen to AI? Uses of AI and "AI" seem to take very well to today's world. Everybody wants a piece of the action be it in ad networks, big data, surveillance, cat ear filters or fake nude pics. People are much…

At least in terms of zeitgeist, it feels like a turning point of some sort when the main conversations around "what does ai even mean?" started to go in a more concrete "can we get it to do this?" direction. It's increasingly becoming a part of of life, even mundane.^ A camera becomes obviously expected to understand who or what it's taking pictures of.

^great example, btw. auto complete and search captures it.

Re: Learning to Love the AI Bubble

#47

Post by dalbasal »

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…

There's certainly a lot of BS going on, especially if it helps sales or fundraising or whatnot... but there's also a lot actually happening in terms of ai/computerized-statistics, that regardless of what you call it, is permeating into life.

Re: Learning to Love the AI Bubble

#48

Post by cung »

chrisa wrote: post #15
nostalgk wrote: post #12

Earlier quoted context omitted.

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…

If you want to learn Deep Learning without all the math - I'm currently releasing a free video per week for something I'm calling the "Summer of AI": https://summerofai.com/ It walks you through all the basics of deep learning (with PyTorch) with a concept video, code video, and then suggested project for each week.

Thank you Chris! Seemed like a super effective course on a glance. Will be looking through this!

Re: Learning to Love the AI Bubble

#49

Post by nostalgk »

cung wrote: post #48
chrisa wrote: post #15

Earlier quoted context omitted.

If you want to learn Deep Learning without all the math - I'm currently releasing a free video per week for something I'm calling the "Summer of AI": https://summerofai.com/ It walks you through all the basics of deep learning (with PyTorch) with a concept video, code video, and then suggested project for each week.

Thank you Chris! Seemed like a super effective course on a glance. Will be looking through this!

I've gone through a few of the modules, and I can say it's been really effective so far.

Re: Learning to Love the AI Bubble

#50

Post by 6gvONxR4sf7o »

voldacar wrote: post #45
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…

"AI" is inherently meaningless because it is a moving goalpost. At any point in time, AI essentially means "something normally done by humans that most people don't think computers could do proficiently". When, inevitably, someone programs a computer to do that thing, that thing quickly becomes no longer "AI" and the cycle continues.

I disagree on the how the goalposts are moving. I'll outline the three camps I see.

Some people say AI and mean "a thinking sorta-conscious thing that thinks like we do." Like C3PO or R2D2 or HAL or ourselves. That's their definition. People complain that that's not a rigorous falsifiable concept, so they say, "Okay here's a test that I think can only be done by a thinking sorta-conscious thing that thinks like we do." Then someone clever figures out how to do it without something that fits their definition. They respond, "Okay fine I guess my test was bad." It's kinda moving the goalposts, but their fuzzy conception of what AI means is still unchanged. It just remains hard to make concrete, especially since we can't really define things like consciousness in the first place.

Another camp is marketing. It takes anything it can sell by calling it AI and does so. The goalposts lower. Linear regression is branded as AI now, like it or not. This is the opposite direction of what you're talking about.

A final camp is what you talked about. I think these people are really in the first camp I mentioned. They don't move the goalposts on their concept, they just move the goalposts on their test. But there are also a distinct group who consider it as "something normally done by humans that most people don't think computers could do proficiently" or even just "computerized decision making/information processing."

I'm in the first camp. "AI" is no less meaningless than "consciousness" but it's equally hard to define. Some people have begun using the label "AGI" for it. Same concept whatever your word choice. I think of C3PO but understand that words are defined by usage and maybe linear regression is AI now.

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