This reminds me of ... What’s the difference between a data scientist and a statistician? A data scientist lives in San Francisco.
More cynically, the difference is 100k/year :P.
In rent? :D
An Interview with an Anonymous Data Scientist (2016)
41–50 of 108 posts
Re: An Interview with an Anonymous Data Scientist (2016)
#42Earlier quoted context omitted.
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?
The article doesn’t make that statement. The article doesn’t provide data to support any statements (it’s a pop science piece).
The original blog:
https://research.googleblog.com/2016/09/a-neural-network-for...
Is better suggests deep learning resulted in maybe 10% improvement. Isn’t as good as human in all cases.
Re: An Interview with an Anonymous Data Scientist (2016)
#43[deleted]
Re: An Interview with an Anonymous Data Scientist (2016)
#44> I feel like the Hollywood version of invention is: Thomas Edison goes into a lab, and comes out with a light bulb. And what you’re describing is that there are breakthroughs that happen, either at a conceptual level or a technological level, that people don’t have the capacity to take full advantage of yet, but which are later layered onto new advances.
Brilliant.
Re: An Interview with an Anonymous Data Scientist (2016)
#45Earlier quoted context omitted.
If super human translation doesn't impress you, what will?
The article doesn’t make that statement. The article doesn’t provide data to support any statements (it’s a pop science piece). The original blog: https://research.googleblog.com/2016/09/a-neural-network-for... Is better suggests deep learning resulted in maybe 10% improvement. Isn’t as good as human in all cases.
Ah I misread "as good" as "better". It's still an epsilon difference, though. And the article lists other applications that have had "step wise" improvements, which is the opposite of incremental of course.
Also, you didn't answer the question.
Re: An Interview with an Anonymous Data Scientist (2016)
#46> 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.
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?
there's a small cottage industry of papers (like [0]) that try to explain this.
Re: An Interview with an Anonymous Data Scientist (2016)
#47This reminds me of ... What’s the difference between a data scientist and a statistician? A data scientist lives in San Francisco.
Believe it or not, they are flooding Southeast Asia, too.
Re: An Interview with an Anonymous Data Scientist (2016)
#48Earlier 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…
What TF model did you use?
This was "ages" ago, pre 1.0 so ~2 years ago. TBH, I can't recall which model we used. We ran it in production for several months on a proprietary training dataset of 30k emails, re-training it once a week.
I regret not following through more on that project, but hey, you've only got so much political capital to burn when people ask you "and how does it make us money?"
Re: An Interview with an Anonymous Data Scientist (2016)
#49Earlier 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).
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 they provide a recipe format that's more systematic than previous approaches. A lot of vision approaches pre-deep-learning were very hit-or-miss. Deep learning has a lot of black art involved in effective training and a lot of time investment but my impression it is more reliable than what came before.
Any other examples welcome
Re: An Interview with an Anonymous Data Scientist (2016)
#50> 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…
I think there is also a pretty pervasive over-estimation of how capable humans are.
As I see more of the failure modes of deep learning, a lot of successes and mistakes made by humans start to become more understandable. Machines don't need to be perfect or avoid failures; like humans, they need to work most of the time and then be used in systems that are tolerant of their potential faults and mistakes.