
Customers who liked this Recommendation Engine may also like its Dequantization - beefman
https://www.scottaaronson.com/blog/?p=3880
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charmides
Scott: Your student is brilliant. Kudos to you for not insisting on sharing
authorship on the paper, like some academic advisors in your position would
do.

~~~
ScottAaronson
Thanks! In theoretical computer science as a whole, authorship is only for
those who made a direct intellectual contribution. It’s one of the things I
like best about the culture of our field.

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lainga
A fantastic result, and a nice story behind it. Per the abstract of Tang's
paper, the current best results are linear in _m_ and _n_.

~~~
titanomachy
Prof Aaronson said the best upper bound they have so far is O(log(n)^33)... to
give that more meaning, O(log(n)^33) grows faster than O(n) until about n =
10^15, so "not practical" is a bit of an understatement!

I still get why this is an interesting result, though. And if Scott thinks
that lower bounds are achievable he is probably right.

~~~
paradroid
10^15 is not that big. We could construct and compute on sparse matrices that
large today.

~~~
rspeer
I'm glad to know where the crossover point is, and yeah, hearing that it's
around 10^15 tells me it's just a few orders of magnitude of improvement away
from being a practical machine learning tool.

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anjc
How is it possible for an 18 year old to be so accomplished? Insane.

~~~
kennyg25
Apparently he is the son of a bioengineering professor and was able to work in
his father's nanotechnology lab while growing up. He also started taking
college courses at a local university when he was 12 (UT Arlington) before
going to UT Austin. [http://www.uta.edu/utamagazine/archive-
issues/2010-13/2012/1...](http://www.uta.edu/utamagazine/archive-
issues/2010-13/2012/12/cultivating-genius/index.html)

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doitLP
Brings to mind a recent quote that goes something like "Many of our brightest
scientists are trying to figure out how to get you to buy one more thing or
stay on a website a few seconds longer, instead of spending their time and
genius tackling pressing issues desperate for more attention."

~~~
s-shellfish
Meh, some problems are theoretically very interesting, but presently have
limited practical applications. Everyone has to find means to survive. For
some people those theoretical problems, that understanding, are one of very
few things that keep oneself whole.

That fine line between genius and insanity, it really does exist. I'm not
saying that because I think I'm a genius. Just, for some people, the only
things that 'ground' us are things that are abstract (I say this with great
awareness of the irony).

Trying to make the pieces fit with the world, that's sometimes hard. You can
throw the whole understanding you've built in the garbage, but then you lose
yourself, and, I dunno. I don't think it's desperation for attention. I think
it's a compulsion for things to make sense, because, a chaotic world. Some
things just have to make sense.

~~~
pishpash
You don't subscribe to the notion that there might be a market failure here?
There is marketmaking, then there is pro-consumption manipulation, how much is
each in adtech is debatable, but it is a waste of investment for advanced
degree holders to spend time manipulating people.

~~~
s-shellfish
Yea, I agree with that too, I can't say I fully like recommendation engines,
as someone who has always veered on the side of 'independent artist' in terms
of 'preference philosophy' I find them somewhat pointless, but whatever works.

I do like them when they actually yield a correct product that is coming from
informed consumers. I waste less money that way as a consumer. I'm talking
mainly about books and art supplies here, that's pretty much what I've used
them for. Makeup too, but that one, lots of money wasted because the beauty
industry is it's own insane beast of navigating trends.

I don't like them when they are manipulated by additional entities to dilute
the quality of recommendation, and I think honestly this was the reason for
their initial creation. But, open system, many recommendation engines work
great so long as the consumer base follows the rules. The more lucrative it
gets, the more layers of reasoning you need (to compete against bad actors
diluting the data quality). The more layers, the greater the need for
computational speed.

Worthy problem, worthless problem, I don't really know. I know the people
working on this stuff aren't necessarily the ones inventing the problems. It's
more that the original intent has been corrupted, and that makes some people
unhappy. So, people work on fixing the problems.

