
Learning to Love the AI Bubble - KhoomeiK
https://sloanreview.mit.edu/article/learning-to-love-the-ai-bubble/
======
dalbasal
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 dominant
position... letting network effects and the scaling power of software and the
web go to work.

Unfortunately for binary outcomes.. getting 4/5 things right is still a
wipeout.

The bubble was slightly off about timing. More big winners were
founded/determined in the 5 years after the bubble than during it. It slightly
overestimated early mover advantage.. closely related to the timing mistake.

If that bubble is the model for this one... interesting times ahead.

~~~
keiru
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.

~~~
dalbasal
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.

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6gvONxR4sf7o
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 nontechnical workers, and not even a single cohesive thing
to people building it all. Wherever you personally draw your lines between AI
and not AI, the boundaries do keep expanding. Does that mean the bubble is
growing? There are undoubtably more people doing machine learning, and there
are more people doing statistics, and more people solving optimization
problems, and each other thing that we call AI, but the "AI" label is growing
faster than all that. It's a weird bubble. If it pops, does that mean there
will be fewer jobs for people like me, or does it just mean people will just
stop calling it AI? Or is this just a word's meaning changing and not a large
bubble?

This story comes to mind:
[http://web.archive.org/web/20190626012618/https://gen.medium...](http://web.archive.org/web/20190626012618/https://gen.medium.com/the-
bs-industrial-complex-of-phony-a-i-44bf1c0c60f8)

~~~
codesushi42
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.

~~~
sjg007
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.

~~~
codesushi42
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.

~~~
sjg007
If you are interested, I would suggest reading up on the
[https://en.wikipedia.org/wiki/Universal_approximation_theore...](https://en.wikipedia.org/wiki/Universal_approximation_theorem)

~~~
6gvONxR4sf7o
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).

~~~
sjg007
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.

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lkrubner
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.

~~~
nostalgk
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 convention in two weeks as
well, but I'd love for a good place to find some background on this so I can
maybe be successful in competing there.

~~~
chrisa
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/](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.

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

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

------
epr
> Not all bubbles have negative consequences for the economy

Unless we are using a different definition of "bubble" than it would seem (to
me) most people intend when they use that word, this is a factually incorrect
statement.

If widespread misallocation of investment capital does not "hurt" the economy,
then what does? If capital is invested with a positive outcome for the wrong
reasons, then that certainly would not fit the definition.

First applicable definition from a google search:

Bubble: used to refer to a significant, usually rapid, increase in asset
prices that is soon followed by a collapse in prices and typically arises from
speculation or enthusiasm rather than intrinsic increases in value.

~~~
tim333
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.

~~~
epr
> 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?

~~~
tim333
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.

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tim333
I'm a bit skeptical there is a bubble as defined in the article as "when the
market value of assets decouple from their intrinsic value and expectations of
rising valuations generate investor demand." As a speculator where are these
soaring AI stocks for me to punt on?

By the way you can read the article with a trial account from O'Rilley but
there's not much too it beyond what's in the summary
[https://learning.oreilly.com/library/view/learning-to-
love/5...](https://learning.oreilly.com/library/view/learning-to-
love/53863MIT60439/)

~~~
sgt101
Isn't the lack of stocks to punt on a dysfunction of the investment system?
The VC market is doing the speculation, and you are locked out - not because
they don't want your money, but because they don't want you to have a chance
of getting theirs!

~~~
carlmr
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.

~~~
scottlocklin
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.

~~~
carlmr
There's probably some betting shop for this.

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febeling
„This time it’s different“ sentiment is necessary ingredient of any bubble.

~~~
gubbrora
Sometimes it really is different. And sometimes it isn't.

~~~
tim333
This is the first 'bubble' where I've felt it really may be different. Along
the lines of this stuff
[https://twitter.com/paulg/status/1130133801403858954](https://twitter.com/paulg/status/1130133801403858954)

------
nabla9
AI bubble can have negative effects:

Misallocation of limited human resources. Good people go from doing long term
fundamental research into developing applications in startups.

Overinvestment and misallocation of capital resources during the bubble can
lead to long period of underinvestment once the bubble bursts.

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keiru
I want to create an AI startup that automatically bids for tulips in the
crypto market.

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pikapikamtf
i feel bad for everyone chasing the AI education bubble ...

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calebm
There are ups and downs as the tide rises. I'm not sure I would call the
crests "bubbles".

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alexheikel
That´s why: F*uck AI (halisback.com) ;)

