
The Economics of AI Today - guidefreitas
https://thegradient.pub/the-economics-of-ai-today/
======
Animats
That's kind of broad. Also, the robots shown have very little "AI".

Maybe machine learning has reached a peak. It's routine now to make
classifiers that are about 90% accurate, and really hard to get much beyond
that. What we really have are systems which extract lots of signals from an
input set and construct a statistical model that maps signals to results. This
works moderately well with enough data, but hits a limit at some point. It's
great for the class of problems where that's good enough. Like ad targeting
and search. Not so great where a wrong result is a serious problem. Like self-
driving cars and medical diagnosis.

I wonder what the next idea will be. I'd like to see progress on "common
sense", defined as being able to predict the consequences of real-world
actions as a guide to what to do next.

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TulliusCicero
> Also, the robots shown have very little "AI".

That's what happens when you change what "AI" means every time there's a
breakthrough.

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unishark
The bar has been getting raised overall though, as more computer tasks get
taken for granted. Unless your requirement is nothing short of scifi machine
consciousness.

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sgt101
This worth reading - a serious and interesting write up.

I'm concerned by the language around how the economy works here "that suggests
that the market expects..." The market doesn't expect a thing - the market is
made of a herd that flocks from shiny thing to shiny thing. It's not a
rational system and modelling it as such is surely completely debunked at this
point?

Also the section on regulation is narrow; the responses cited - putting
managers in, licensing a regulator to check outcomes - are weak and old
fashioned. Two fundamentals are not mentioned; first an investigative and
proselytizing enforcement agency as per areospace that can identify specific
routes to failure and then communicate them to the professional and business
actors. Secondly professions in the sense of proper engineers who are
personally liable and insured and _required_ for the use of the technology
(brew what you like in your room, but if you use it on people then prison
beckons). Third process and infrasturture that is required for use and that no
professional would contemplate life without (like design drawings and stress
analysis in engineering).

The section on the political economy of AI is interesting, it put me in mind
of the latest William Gibson book "Agency" in there there is a quote -
something along the lines of "at the boundary of unauthorized military
research and the most reckless kind of commercial use" describing the
origination of an AI. The framing and narrative of the book is much more
plagent and potent than the literature of economic analysis; the data and
logic of the economists so far are not as convincing as fiction and polemic.
If I was an economist I would be concerned by that.

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algo_trader
Has any one read both Agency & the Peripheral? Which one is the lighter read?

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sgt101
They are both pretty light and riproaring. Definitely novels of ideas not
characters, fantastic language and clever plots.

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ArtWomb
Well-sourced conference report. Actually saves me a lot of time. To summarize:
it's still the Wild West in AI. But there is broad recognition that governance
is essential

I'll just add one more report, as if the dozens already mentioned were not
enough. It's from Berkeley's Center For Long-Term Cybersecurity. And it
addresses the enormous challenge of securing AI systems from adversarial
attack. A glimpse into the vortex of how the "industrialization of AI" creates
a self-perpetuating, fractal-like cycle of eternal dependencies. Requiring us
to create ever stronger AI to protect and serve the AI on which our new
engineering platforms will be founded upon

[https://cltc.berkeley.edu/wp-
content/uploads/2019/02/CLTC_Cu...](https://cltc.berkeley.edu/wp-
content/uploads/2019/02/CLTC_Cussins_Toward_AI_Security.pdf)

It stands to reason then, the ultimate AI-mediated prediction problem is
predicting the impacts of AI itself ;)

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netcan
At least in my bubble, AI discussions have been going in predictable ways..
and getting grounded in the same places.

First, we try to build the (proverbial) foundation: What is AI? What is
intelligence? Is it general? _Lots_ of places to get stuck here. Can machines
understand meaning? Is general intelligence statistical. Can it be?

No real way of settling these, so we have poor foundations.

Then we ask: What can it do? When? What _will it_ do? Why? Can it automate
driving? Other stuff? How big a deal is this economically? Will all cars be
taxis?

At this point, foundations crack. We're trying to predict the economic side-
effects of a technology, its viability, timelines, regulation... and we're
building these predictions on very abstract foundations. Obviously, it's all
too squishy so we end up nowhere.

Anyway, the effects of technology are very hard to predict... especially
recent ones. Computerisation of offices _has not measurably increased
productivity_ since the 80s/90s[1], for example. A PC landed on every desk.
Many more people work at a desk than before. What predictions would we have
made in the 90s, when this was starting to look inevitable.

[1]David Graeber, Tyler Cowen & others highlight this point. It's hard to
define or measure, so most economists don't. But within wide margins of error,
it does not seem to

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ksec
>Computerisation of offices has not measurably increased productivity since
the 80s/90s[1],

As far as I can tell. It isn't AI, or whatever technology that is not getting
productivities increases in business. When was the last time you saw a CRM /
ERP replacement that had any productivities increase? It seems there is a very
clear divider in between all Technology Companies vs Other non-tech company.
It is that No one knows how to best integrate the two to maximise potentials.
Only the one which have their feet on both side ( Amazon ) seems to understand
it.

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netcan
I think it runs deeper than " _can 't figure out how to X._"

We're very good at figuring out productivities when dealing with a factory or
somesuch. A transport authority or facebook "business headquarters" doesn't
interplay with technology in the same ways.

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ksec
>I think it runs deeper than "can't figure out how to X."

Yes. I wish there is an in depth article to explain some of these observation
we see in real life.

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trycrmr
Had hoped an article like this would shed some light on the environmental
impacts of the additional compute required to build and maintain a feature
backed by AI. My understanding is it takes significantly more compute + data,
and therefore electricity, to build and maintain a feature baked by AI. As AI
becomes increasingly accessible, particularly through offerings from cloud
providers, the power consumption would increase faster than when folks were
exclusively writing scripts/uploading binaries to process transactions. My
hope is it's negligible. Haven't had the time to crunch numbers to figure this
out as it's out of my daily lane.

Maybe I skimmed the article too fast and missed it while enjoying my coffee
and bagel sandwich. Let me know if that happened.

~~~
trycrmr
*backed

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mistrial9
related to ide.mit.edu ?

