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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)

#51

Post by sp527 »

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…

That is a very impressive anecdote in how powerfully it expresses the dichotomy in thinking styles between two different types of people. Babbage the engineer presumed the most literal interpretation of the query and came to the most logical conclusion. But arguably the politicians, having a keener understanding and appreciation of human fallibility, had the more profound and shockingly prognosticative insight.

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

#52

Post by newfoundglory »

sp527 wrote: post #51
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…

That is a very impressive anecdote in how powerfully it expresses the dichotomy in thinking styles between two different types of people. Babbage the engineer presumed the most literal interpretation of the query and came to the most logical conclusion. But arguably the politicians, having a keener understanding and appreciation of human fallibility, had the more profound and shockingly prognosticative insight.

What is this supposed to mean?

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

#53

Post by amrrs »

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…

Wondering what Tensorflow has to do with that out-performance since it must be all about the model/algorithm that you implemented in that - like you could've had a TF code running the same conventional approach you mentioned above - which wouldn't have done any magic. Isn't it the algorithm like a convnet doing the magic rather than TF itself responsible for it?

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

#54

Post by sp527 »

newfoundglory wrote: post #52
sp527 wrote: post #51

Earlier quoted context omitted.

That is a very impressive anecdote in how powerfully it expresses the dichotomy in thinking styles between two different types of people. Babbage the engineer presumed the most literal interpretation of the query and came to the most logical conclusion. But arguably the politicians, having a keener understanding and appreciation of human fallibility, had the more profound and shockingly prognosticative insight.

What is this supposed to mean?

I believe what the politicians really wanted to ask was: “how do we account for the tendency for human operators to make mistakes?” If you stop to think about it, what seems at first like a dumb question in fact presages difficulties that will have to be addressed by everything from unit testing to spell checkers to entire classes of non-deterministic algorithms.

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

#55

Post by cosmic_ape »

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…

As other comments mention, if Tensorflow is seen for what it is - a framework for computation, rather than just "a deep learning thingy", it may be pretty useful.

It is probably quite far from a standard usage, but Tensorflow may be used to write some custom graphical models inference, for example. To be practical these algorithms can not be implemented in, say, pure python.

The point is Tensorflow gets you pretty close to assembly level computation. The alternative is to write in, say, cython - which is much more time consuming to write, and does not give you parallelization for free. Another alternative I guess would be torch, but that is the same as tensorflow the way I see it.

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

#56

Post by mslate »

amrrs wrote: post #53
mslate wrote: post #35

Earlier quoted context omitted.

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…

Wondering what Tensorflow has to do with that out-performance since it must be all about the model/algorithm that you implemented in that - like you could've had a TF code running the same conventional approach you mentioned above - which wouldn't have done any magic. Isn't it the algorithm like a convnet doing the magic rather than TF itself responsible for it?

Yes, TF is merely a framework implementing convolutional neural nets, not a novel implementation of them.

We chose TF over other convolutional neural net libraries because it was 1. Python and 2. heavily sponsored by Google.

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

#57

Post by IanCal »

sp527 wrote: post #54
newfoundglory wrote: post #52

Earlier quoted context omitted.

What is this supposed to mean?

I believe what the politicians really wanted to ask was: “how do we account for the tendency for human operators to make mistakes?” If you stop to think about it, what seems at first like a dumb question in fact presages difficulties that will have to be addressed by everything from unit testing to spell checkers to entire classes of non-deterministic algorithms.

I've always wondered if those questions came after a statement like "this eliminates the possibility of errors", and were less of a question than a statement.

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

#58

Post by gaius »

CalChris wrote: post #3

This reminds me of ... What’s the difference between a data scientist and a statistician? A data scientist lives in San Francisco.

A data scientist lives in San Francisco

That's completely over-simplifying matters. Data scientists also drink soy lattes and ride children's push scooters.

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

#59

Post by sanxiyn »

joe_the_user wrote: post #49
comstock wrote: post #32

Earlier quoted context omitted.

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

The big results as far as I understand: - Image recognition - Winning the games computers hadn't won already - Incremental progress on translation. Plus translation that doesn't need as many domain experts - Self-driving cars (with related automation applications) Of image there, image recognition stands out as the big leap and the rest are relatively incremental. One of the things with the other applications is that…

You forgot speech recognition.

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

#60

Post by SiempreViernes »

carlsborg wrote: post #18

It was 2016 and he said "I’ve noticed on AWS prices was that a few months ago, the spot prices on their GPU compute instances were $26 an hour for a four-GP machine, and $6.50 an hour for a one-GP machine. That’s the first time I’ve seen a computer that has human wages.." Minimum wage (or thereabouts $7.20) now gets you a whopping p2.8xlarge (8 GPU, 32 vcpus, 488GB RAM), and the single GPU machine p2.xlarge is now $0…

Depends, do you think the lowest legal wage should go up or down?
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