
Ask HN: Are we in an AI / ML bubble? - orbOfOrthanc
As in title. Is there a systemic over-valuation of companies in these spaces?
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gesman
I doing high-end amateur photography and there are plenty of tools that are
able to enhance (sharpen, noise reduction, color correction) imagery using AI
that was not possible before.

I took AI/DL course before and professor demonstrated from scratch how to
build an algo and train model capable of amazing upsampling of imagery.

Hence - bubble or not - there are lots of useful, practical benefits to this
today.

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probinso
AI is too big a term, Its like asking if there is a math bubble. AI is a lot
of different disciplines.

There is likely a mis-prioritization of applications in the market.

You should look at bubbles in the context of applications, and perhaps
dependent on strategy separately. Context ~ Health, AutoBI, NLP...; Strategy ~
unsupervised, explainable, deep, symbolic...

Another way to look at this is identifying Misery stocks. If the economy takes
a hard dip, what will continue to be funded. Those are misery stocks. Some AI
will survive misery at varying levels, others will not.

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WnZ39p0Dgydaz1
There are very few "AI companies". True AI companies are research labs like
DeepMind and those AI consultancies. Both of these make money from PR and
hype. Other than that, some video/image analysis and processing companies,
e.g. face recognition solutions, come to mind as exceptions. The other kind of
company that depends on the recent AI developments is infra. They are selling
the "shovels" for others to buy into the hype.

Other companies that brand themselves as AI do so for PR purposes. Most of
them don't use AI, use robust techniques that have been around for many
decades, or use it only to justify their pitch. Their product would be just
the same with rule-based systems or without any kind of ML.

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anon234345566
This, precisely.

I would add, there are some DL/ML business components which are in heavy use
in some industries which given the companies size are giant productions
deployments of "IA" (not expert systems or other automation technologies), so
you could say we are actually living somehow in the principle of a golden age
of "IA" (DL/ML techniques), but DL/ML techniques - at least what is known
publicly to be the state of the art - has some practical limits (i.e. power
consumption to traing useful models), but workarounds for those limits are
being heavyly studied or solutions are being tested (as we speak indeed).

What's here for sure, it's a golden age of data: you can extract (meta)data
from almost everything running on a CPU/GPU, the "likes" in everything are the
users training models, not exactly the models you imagine (because you can
associate/correlate some scenarios - "a birthday party at the office" \- with
others you would think are a lot different - "a christmas party at the bar" \-
but they'are not so different actually, and the features found by the training
are more or less - it should be a % - interchangeable.

So yeah, every "like" out there is training something behind the scene.

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rvz
Yes, because everyone is now 'An AI company' to woo investors, yet they don't
contribute to AI research.

If this hype-cycle doesn't sound ridiculous, I've also heard that my local
grocery store is using deep-learning + kubernetes to scale their supply chain
systems. They told me that they're writing a Medium blog-post about this which
is coming soon /s

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codingslave
No its just getting started, the world is still largely almost all
unstructured data that needs to be structured

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goodhacker
Yes, but this is to be expected. I think we are nearing the 'trough of
disillutionment' in the Gartner hype cycle.

[https://en.m.wikipedia.org/wiki/Hype_cycle](https://en.m.wikipedia.org/wiki/Hype_cycle)

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askafriend
You need to have/generate meaningful data at significant scale to really be a
company where AI can make a big difference.

Few companies build products that generate or interface with data at that
level.

