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An Interview with an Anonymous Data Scientist (2016)

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

#101

Post by blueplastic »

nocoder wrote: post #19

I work at a tech company and one of the things I have recently noticed is how ML and AI terms are being increasingly used by the business people. The guys who have no technical understanding, these are accountants or marketing guys saying we should ask tech team to design ML to solve these problems. Its as if ML is a thing to through at every kind of imaginable problem and it will be magically solved. I believe a lot…

I'm pretty sure you can throw IBM Watson's AI at any of these business problems and you can solve it very quickly.

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

#102

Post by soared »

perturbation wrote: post #94
cosmic_ape wrote: post #64

Earlier quoted context omitted.

I think their hype got even you a little bit. That is revealed by the word "even" in the phrase: 'people calling CNNs or even non-deep-learning models "AI"'...

What I mean by this is - I don't see how anyone could reasonably call a Random Forest "AI" with a straight face, whereas someone could (wrongly, but understandably) call a CNN / RNN / etc. AI if only because it has the word "neural" in it. There's two groups: - People who are overly enthusiastic about neural nets - People who are cynically calling every ML algorithm "AI", up to and including linear regression and I'm…

To anyone non-technical, a decision is AI. 99% of the world is non-technical so its probably only going to continue to be this way.

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

#103

Post by chillee »

azag0 wrote: post #71
aoki wrote: post #46

Earlier quoted context omitted.

i suspect that what he's referring to is that he's heuristically minimizing a somewhat arbitrary (loss) function in a million-ish dimensions using the simple variants of gradient descent that work under these conditions. it sounds far too WIBNI to produce good results reliably (in practice, let alone in theory). the landscape has so many stationary points at which to get stuck; why would you ever get good results? th…

I think this recent paper [1] sheds quite a bit of light on this. [1] https://arxiv.org/abs/1703.00810v3

Really don't think that's the best paper to say "sheds quite a bit of light on this". That paper has been somewhat controversial since it came out.

I think https://arxiv.org/abs/1609.04836 is seminal in showing unsharp minima = generalization, the parent's paper is good for showing that gradient descent over non-convex surfaces works fine, https://arxiv.org/abs/1611.03530 is landmark for kicking off this whole generalization business (mainly shows that traditional models of generalization, namely VC dimension and ideas of "capacity" don't make sense for neural nets).

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

#104

Post by lemondrops »

perturbation wrote: post #62

I've been seeing nothing but negative, dismissive comments about data science on HN lately, which is really disappointing. There's definitely a lot of hype right now about DL, but almost all of my job does not deal with Big Data or Deep Learning, 'just' machine learning + stats + calc + scripting + data cleaning + deploying models. I think most people don't have big data (Amazon has an x1 with 4 TB of RAM, after all!…

It's silly you're getting downvoted for this well-articulated and insightful comment.

I think much of the negativity towards DS from the programming community is because the Data Scientist is what the programmer used to be ~15 years ago. It's that nerdy thing for a select group of very smart people, whereas being a software developer/engineer/architect/whatever has become just another common job (at least outside of Silicon Valley).

Also, from my experience as the lone developer taking the first steps to implement machine learning techniques in my company - lots of developers also think DS/ML is a cool thing with value, but they simply, absolutely don't understand it (and don't want to put in the effort to learn). These techniques are not hard and not magic, but they require a completely different way to think about problems than "traditional" programming does. I've seen developers up and down the hierarchical ladder struggle with wrapping their heads around these concepts, and it's way easier to dismiss it all as "hype" instead of accepting the fact that these techniques will be a huge part of what software development will look like in the future.

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

#105

Post by kwisatzh »

Radim wrote: post #72
EdwardDiego wrote: post #22

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 gi…

Getting better obviously, but the feet-on-the-ground experience for MLlib is still far from pleasant: hard to configure, hard to manage, hard to scale, hard to debug. By way of anecdote, Spark's MLlib used to contain an implementation of word2vec that failed when used on more than 2 billion words (some arcane integer overflow). So much for scale! As for performance, in 2016, the break-even point where a Spark cluster…

Can you be more specific in terms of issues with ML Lib? I'm thinking of using it with Spark cause of big data requirements, but have heard MLLib in particular is highly unreliable.

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

#106

Post by geezerjay »

fnl wrote: post #67
gwern wrote: post #8

Earlier quoted context omitted.

Speaking as a 'loon', his AI history is wrong in several places: 1. the Fifth Generation Project ( https://en.wikipedia.org/wiki/Fifth_generation_computer ) was 19 8 0s officially ending in 1992, not 'late 1990s' (during the Dot-com bubble?!); 2. the Lisp bubble didn't pop because of a failed DoD piloting project, it popped because of the first AI Winter + commodity SPARC/x86 pressure + recession ( https://en.wikiped…

Here's another factual error: Data science is from the 1960s, and was used first in a paper published by Peter Naur in 1974: https://en.wikipedia.org/wiki/Data_science

Data science is actually statistics, which goes quite a bit further than the 1960s. In fact, today's data scientists love to quote Box and Fischer.

Data science and data mining are victories of marketing over common sense.

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

#107

Post by fnl »

geezerjay wrote: post #106
fnl wrote: post #67

Earlier quoted context omitted.

Here's another factual error: Data science is from the 1960s, and was used first in a paper published by Peter Naur in 1974: https://en.wikipedia.org/wiki/Data_science

Data science is actually statistics, which goes quite a bit further than the 1960s. In fact, today's data scientists love to quote Box and Fischer. Data science and data mining are victories of marketing over common sense.

Sorry, I meant that in the sense of the origin of the term. But yes, DS is mostly just another word for statistics. About as pointless as the term AI has become.

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

#108

Post by _raoulcousins »

srean wrote: post #87
brucephillips wrote: post #27

Earlier quoted context omitted.

> 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 j…

I don't understand why you don't want a global optimum. Is this obvious? Does the following paragraph explain it, because I don't see the connection.
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