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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)

#21

Post by comstock »

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…

I find it somewhat understandable from non-tech people. I’m more surprised at now much people within the tech world but the hype.

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

#22

Post by EdwardDiego »

Can anyone comment on his point about Spark's ML libs? I note that was from last year (about 2015 code), not sure what level of beta they were at, but yeah, I use it for batch processing, but have never used the ML aspects, so just curious.

> And even up to last year, there’s just massive bugs in the machine learning libraries that come bundled with Spark. It’s so bizarre, because you go to Caltrain, and there’s a giant banner showing a cool-looking data scientist peering at computers in some cool ways, advertising Spark, which is a platform that in my day job I know is just barely usable at best, or at worst, actively misleading.

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

#23

Post by fellellor »

wolfgke wrote: post #11
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…

> On two occasions I have been asked, — "Pray, Mr. Babbage, if you put into the machine wrong figures, will the right answers come out?" In one case a member of the Upper, and in the other a member of the Lower, House put this question. I am not able rightly to apprehend the kind of confusion of ideas that could provoke such a question. Luckily math has developed methods such as error-detecting/error-correcting codes…

From what I know, error correcting codes wrap around information (in a manner of speaking) so as to provide a measure of consistency, which then enables error correction properties. If the information itself is riddled with errors then the error correcting code can't do anything here.

People using Babbage's machine would have entered raw information into that thing. No error correcting code would correct the human induced flaws in that. So the question was absurd at the time.

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

#25

Post by hyperbovine »

nicolewhite wrote: post #15

I enjoyed his comments on Tensorflow. > It’s really bad to use. There’s so much hype around it, but the number of people who are actually using it to build real things that make a difference is probably very low. I wonder how many data scientists out there are actually developing Tensorflow models for a mission-critical project at work. I'm not. I have used Tensorflow successfully within my personal projects, but I'v…

I'm currently using TF for a scientific algorithm that's completely unrelated to deep learning. The speedup over our previous solution is probably on the order of 1000x. There's nothing magical about Tensorflow, we were just too lazy/busy to dive deep on the legacy code, GPUify it, etc. Tensorflow let me do that in a couple of days. So, that's a win. OTOH I completely agree that the API and docs are completely inscrutable at times. Presumably Google is happy with it.

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

#26

Post by sriku »

> 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 ask folks in nonlinear optimization, they'll tell you that DL is not possible".

It is useful to keep that innocence alive despite being educated, especially if the cost to trying something out doesn't involve radical health risks. That plus a balance with scholarship.

Knowledge, courage and the means to execute are all needed.

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

#27

Post by brucephillips »

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.

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

#28

Post by brucephillips »

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…

The big tech companies are demonstrably using deep learning to solve previously unsolvable problems. It's a significant advance.

What's yet to be seen is if startups can profit from this advance, since it depends on massive data and compute.

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

#29

Post by mljoe »

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.

It happens when practitioners generalize theorems to scenarios that look similar but don't apply. The common pattern is misapplying an infinite set theorem to finite set case. If you don't know about the theorem in question to begin with, there is no way for you to misrepresent it.

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

#30

Post by brucephillips »

> the data sets have gotten large enough where you can start to consider variable interactions in a way that’s becoming increasingly predictive. And there are a number of problems where the actual individual variables themselves don’t have a lot of meaning, or they are kind of ambiguous, or they are only very weak signals. There’s information in the correlation structure of the variables that can be revealed, but only through really huge amounts of data

This isn't really true, since this can be said of any ML model. ML is nothing new. Deep learning is new. It works because we have so much data that we can start to extract complex, nonlinear patterns.

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