This reminds me of ... What’s the difference between a data scientist and a statistician? A data scientist lives in San Francisco.
The data scientist optimizes ad clicks
An Interview with an Anonymous Data Scientist (2016)
61–70 of 108 posts
Re: An Interview with an Anonymous Data Scientist (2016)
#62I'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!) but there's no shame in that. I'll use a big machine for grid search or other embarrassingly parallelizable stuff, but I can confirm that Spark is usually a bad tool for actual ML unless you use one of their out-of-the-box algos. Even then, tuning the cluster on EMR with YARN is a pain, especially for pyspark. There's a gap, I think, between the inflated expectations of "I'm going to get general AI in 5 years and CHANGE THE WORLD" and "this K-means clustering will be a good way to explore our reviews", but somewhere in the middle there is actual value.
(I also hate that "AI" is becoming the new hype-train; I don't consider anything of what I do to be "AI", but you have people calling CNNs or even non-deep-learning models "AI"). This is only going to result in inflated expectations- DS practitioners have to communicate the value without hype, and also find a way to weed out charlatans.
Re: An Interview with an Anonymous Data Scientist (2016)
#63Jeff Hamerbacher, the guy who coined the term Data Science, also said "The best minds of my generation are thinking about how to make people click ads. That sucks.”
Re: An Interview with an Anonymous Data Scientist (2016)
#64I'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!…
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"'...
Re: An Interview with an Anonymous Data Scientist (2016)
#65Earlier quoted context omitted.
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.
[deleted]
Re: An Interview with an Anonymous Data Scientist (2016)
#66Jeff Hamerbacher, the guy who coined the term Data Science, also said "The best minds of my generation are thinking about how to make people click ads. That sucks.”
Um, no, that's yet another falsehood in that interview; The term DS is much older, and stems from Peter Naur, anecdotally coined in the 1960s and with a provable [edit: removed wrong ref] paper in 1974 using that term: https://en.wikipedia.org/wiki/Data_science
Re: An Interview with an Anonymous Data Scientist (2016)
#67Good 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…
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
Re: An Interview with an Anonymous Data Scientist (2016)
#68It 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?
Even if I wanted it to double, I don't think that would make it more likely to actually happen.
I think the likelihood of machine power available being double or quadruple what it is now is pretty good.
Re: An Interview with an Anonymous Data Scientist (2016)
#69As important as it is to debunk the hype surrounding AI, it is also important to note that the recent advances in neural nets hinted that we're onto something regarding the functioning of the brain, and in my opinion, it would be equally foolish to dismiss the _possibility_ of a breakthrough that would get us much closer to general AI (for instance if someone came up with some kind of short-term / long-term memory mechanism that works well)
I personally think that the main reason why general AI may be very far away is because there is little incentive today for working on it. Specialized AI seemss good enough to drive cars. Specialized AI should be good enough to put objects in boxes, cut vegetables and flip burgers and so on, and the economical impact of building that is much greater than the economical impact of making a robot that barely passes the turing test and that's otherwise fairly dumb or ethically unbounded.
Re: An Interview with an Anonymous Data Scientist (2016)
#70I'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!…
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.
But, all those things people did in the '90's or even earlier. It was called "data warehousing" or "decision support" back then. The fundamental techniques - linear regression, logistic regression, k-mean clustering - go back even earlier, to the OR community post-WW2. Banks have been doing credit scoring with these techniques for a loooong time. The manufacturing industry has been using these techniques for even longer. Engineering for even longer than that.
So you can see why people are quite cynical about the way old, established techniques are being presented as the hot new thing - and you can see why people who have been doing this stuff for 20+ years might be annoyed at 20-somethings who claim to have invented this new thing. What's wrong with someone calling themselves a "statistician" or an "applied mathematician"?
But this is by no means purely a DS thing, seems noone is a programmer anymore either, they're all "senior certified enterprise solution architects" or some grandiose thing.