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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)

#31

Post by rcpt »

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…

Right. I found that part of the article particularly irritating - there are tons of examples of researchers making substantial contributions outside of their primary field, cf https://mathoverflow.net/q/173268/6360

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

#32

Post by comstock »

brucephillips wrote: post #28
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.

AlphaGo is interesting. But what big new problems have been solved? (rather than incrementally improved).

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

#33

Post by sjg007 »

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.

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

#34

Post by brucephillips »

comstock wrote: post #32
brucephillips wrote: post #28

Earlier quoted context omitted.

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.

AlphaGo is interesting. But what big new problems have been solved? (rather than incrementally improved).

https://m.cacm.acm.org/magazines/2017/6/217734-deep-learning...

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

#35

Post by mslate »

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…

We used it for a sales email classification problem--it significantly out-performed our conventional approaches (i.e. logistic regression + bag-of-words), but we were not PhDs and none of our job titles were "data scientist" so I guess that makes us charlatans ;)

That service offering among the rest of the business was marginal so it never became an offering that our sales team pitched our customers very aggressively, so in this particular case TensorFlow did not push the needle so-to-speak.

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

#36

Post by brucephillips »

mslate wrote: post #35
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…

We used it for a sales email classification problem--it significantly out-performed our conventional approaches (i.e. logistic regression + bag-of-words), but we were not PhDs and none of our job titles were "data scientist" so I guess that makes us charlatans ;) That service offering among the rest of the business was marginal so it never became an offering that our sales team pitched our customers very aggressively…

What TF model did you use?

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

#37

Post by comstock »

brucephillips wrote: post #34
comstock wrote: post #32

Earlier quoted context omitted.

AlphaGo is interesting. But what big new problems have been solved? (rather than incrementally improved).

https://m.cacm.acm.org/magazines/2017/6/217734-deep-learning...

Looks like great incremental progess. Have you seen the state of JapaneseEnglish translation? It’s almost completely useless.

I really don’t see this as a huge win for deep learning, anything else?

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

#38

Post by brucephillips »

comstock wrote: post #37
brucephillips wrote: post #34

Earlier quoted context omitted.

https://m.cacm.acm.org/magazines/2017/6/217734-deep-learning...

Looks like great incremental progess. Have you seen the state of Japanese English translation? It’s almost completely useless. I really don’t see this as a huge win for deep learning, anything else?

Whether progress is incremental is an ill defined question. I don't consider "super human translation" to be incremental. The key point here is that deep learning has produced significant results. I'm not sure why you care to argue semantics.

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

#39

Post by comstock »

brucephillips wrote: post #38
comstock wrote: post #37

Earlier quoted context omitted.

Looks like great incremental progess. Have you seen the state of Japanese English translation? It’s almost completely useless. I really don’t see this as a huge win for deep learning, anything else?

Whether progress is incremental is an ill defined question. I don't consider "super human translation" to be incremental. The key point here is that deep learning has produced significant results. I'm not sure why you care to argue semantics.

Well, I’m interested in understanding how valuable deep learning is and if lives up to the hype.

Better translation of European languages (which wasn’t a totally unsolved problem anyway) doesn’t seem to be something that really lives up to the hype.

Particularly as the article cited doesn’t seem to back up its statements very well.

So... anything else?

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

#40

Post by brucephillips »

comstock wrote: post #39
brucephillips wrote: post #38

Earlier quoted context omitted.

Whether progress is incremental is an ill defined question. I don't consider "super human translation" to be incremental. The key point here is that deep learning has produced significant results. I'm not sure why you care to argue semantics.

Well, I’m interested in understanding how valuable deep learning is and if lives up to the hype. Better translation of European languages (which wasn’t a totally unsolved problem anyway) doesn’t seem to be something that really lives up to the hype. Particularly as the article cited doesn’t seem to back up its statements very well. So... anything else?

If super human translation doesn't impress you, what will?
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