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An Interview with an Anonymous Data Scientist (2016)

logicmag.io

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Re: An Interview with an Anonymous Data Scientist (2016)

#81

Post by yters »

marshray wrote: post #17
comstock wrote: post #7

Any bets on when the current deep learning bubble is going to burst? It’s shocking to me how much technical people buy into this, how “this time it’s different” and AI isn’t “over-promising and substantially under-delivering” this time. Really odd to watch it come round again, when the reality is we’re more likely to see some near incremental progresses, partly fueled by more compute and algorithmic advances. Partly…

I think we're just used to computers advancing noticeably on a regular basis: "Is this year's iPhone better enough to justify an upgrade?" Also, we judge the difficulty of things by our own experience. It took us ~1 billion years to get to the point where we could communicate abstract ideas and play chess. These were once believed to be the challenging problems in AI. It turned out that chess is easy we're just relat…

Chess is easy when you have the hardware to effectively brute force it. Once someone develops an algorithm that requires an order of moves comparable to a human, and significantly outperforms a human, then AI will be interesting.

Re: An Interview with an Anonymous Data Scientist (2016)

#84

Post by dalbasal »

Terr_ wrote: post #4

Good interview, there are a bunch of bits I feel like I ought to be Quoting For Truth but then I'd end up with a pretty bloated reply. > I want to emphasize that historically, from the very first moment somebody thought of computers, there has been a notion of: “Oh, can the computer talk to me, can it learn to love?” And somebody, some yahoo, will be like, “Oh absolutely!” And then a bunch of people will put money in…

For some devils advocacy...

I remember hearing from some old salty in the oil business that geologists are the wrong people to ask about peak oil. They always understimated future discoveries. The ones that tended to get it right were finaciers and investors.

The idea is that geologists have their noses down in the details of practical, useful knowledge that they have or can get. Financiers don't really know anything, just that wells have been found in the past. They just model things like exploration money, the rate and quality of new finds, oil prices, production costs...

There could be somethng similar here. The real technology people see mostly problems. All the stuff that would need to be solved, that they have no idea how to solve. The fact that we don't even know what intelligence is. The frauds making audacious claims.

Outsiders see drones, self driving cars, spam filters, google search, chess, face recognition, translation, chatbots^. They see that voice recognition now works. I reckon medical diagnosis might do something soon. In any case, it seems that pone way or another, these add up to something. ...just as a hunch.

Obviously I don't know the answer and this whole comment is based on an anecdote that may not even be true. Still, I don't discount the possibility that the unwashed masses are right.

^just kidding

Re: An Interview with an Anonymous Data Scientist (2016)

#85

Post by muraiki »

sundarurfriend wrote: post #75

It's an interesting read, though not very enlightening in terms of new information. It's same old pre-existing arguments put in a more informal, more directly honest package. As another person who's seen robots fall over again and again and has a scope for the difficulty of the problem, I'd say there's also the risk of the day to day failures making us lose sight of the forest for the trees, with availability bias wo…

Yeah, I was shocked when I came across that. The data scientist appeared to be really in tune with ethical problems, and then speaks like that. It's very disappointing.

Re: An Interview with an Anonymous Data Scientist (2016)

#86

Post by chestervonwinch »

fnl wrote: post #66
otalp wrote: post #63

Jeff Hamerbacher, the guy who coined the term Data Science, also said "The best minds of my generation are thinking about how to make people click ads. That sucks.”

Um, no, that's yet another falsehood in that interview; The term DS is much older, and stems from Peter Naur, anecdotally coined in the 1960s and with a provable [edit: removed wrong ref] paper in 1974 using that term: https://en.wikipedia.org/wiki/Data_science

Interestingly, Tukey's (of fast Fourier fame) paper, "The future of Data Analysis" [1], was published circa 1961.

[1]: https://projecteuclid.org/download/pdf_1/euclid.aoms/1177704...

Re: An Interview with an Anonymous Data Scientist (2016)

#87

Post by srean »

brucephillips wrote: post #27
sriku wrote: post #26

> You become so acutely aware of the limitations of what you’re doing that the interest just gets beaten out of you. You would never go and say, “Oh yeah, I know the secret to building human-level AI.” A colleague of mine called these "educated incapacities" - where we become acutely aware of impossibilities and lose sight of possibilities. Andrej Karpathy, in one of his interviews iirc, said something like "if you a…

> If you ask folks in nonlinear optimization, they'll tell you that DL is not possible. I sincerely doubt anyone who knows more than one sentence about deep learning would say that, since deep learning doesn't claim to optimize.

You are right. Unfortunately, many (doubly unfortunately, even in academia, well, many who switched careers in optimization to ML) think that machine learning is just optimization.

Regarding deep NNs, one should be careful with what one wishes for, because sometimes they come true. Landing up with the global optimum of that thing would likely be the last thing one wants.

The key to deep NNs is to do such a pathetic job of optimizing the loss that the generalization is good. A problem is that there several different ways of doing a job poorly, not all of them would generalize well. When I have my engineer hat on, I would rather not have lots of indeterminism on my watch if I can afford it. Too dang hard to maintain correctness of.

On the other hand if one has a "with high probability" style result where the probabilities are high enough to be practically relevant, then we have something more workable.

Re: An Interview with an Anonymous Data Scientist (2016)

#88

Post by srean »

yters wrote: post #82

DL is hyped as a big thing, but why are multiple layers on a NN a breakthrough? The only breakthrough is hardware, but I don't see that hyped.

Shh, will you. Some truths are not to be aired in public.

We know that no manager got fired for choosing Java.

There is a researcher's version of that. No researcher got fired for making a neural network more 'convoluted'. It helps if there exists one dataset where it does 0.3% better. Doesn't matter if that data set is(has been since the late 90s) standard fare as a homework problem in machine learning course.

That said we do understand these things a bit better than before. Some concrete math is indeed coming out.

Re: An Interview with an Anonymous Data Scientist (2016)

#89

Post by ramtatatam »

I'm not native English speaker and I find this sentence from the article weird:

> Because the frightening thing is that even if you remove those specific variables, if the signal is there, you're going to find correlates with it all the time, and you either need to have a regulator that says, “You can use these variables, you can't use these variables,” or, > I don't know, we need to change the law. As a data scientist I would prefer if that did not come out in the data. I think it's a question of how we deal with it. But I feel sensitive toward the machines, because we're telling them to optimize, and that's what they’re coming up with."

So is he saying that he is worried optimisation throws results that are not what he would like to see?

Re: An Interview with an Anonymous Data Scientist (2016)

#90

Post by chestervonwinch »

sjg007 wrote: post #33
eanzenberg wrote: post #13

Eh, pretty disappointing interview. It doesn’t tske a team to utilize gpu computing, it takes one person and I’ve done it. Also, you can’t complain about there being no strong-ai companies and then list accomplishments of strong-ai companies. I personally don’t like the phrase data scientist but I get it and I get why it’s science as opposed to engineering. I personally like the split between machine learning, BI, an…

I think the contrast is between statisticians and physicists PhDs compiling GPU support... even some CS PhDs have a hard time with that... this is less important as time goes on since the engineers figure it out and make it readily available.

When I installed Theano, it was just `pip install theano`, and editing a couple of lines in a config file. Are other GPU libs (tensorflow, caffe, etc.) really that much more difficult?
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