An Interview with an Anonymous Data Scientist (2016)

Hah, this is a great interview! [You can't really trust someone who calls themselves a data scientist, they are just taking that exciting and financially rewarding name], loosely paraphrasing. Too bad it is anonymous. It totally fits my unfair preconceptions of this field. I know, I'm a "computer scientist" with a phd, its not a real science if you have to put science in the name, that's what they tell me.

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 into it, and then they'll be disappointed.

Reminds me of a pre-transistor computing quote from Charles Babbage, about some overeager British politicians:

> On two occasions I have been asked, — "Pray, Mr. Babbage, if you put into the machine wrong figures, will the right answers come out?" In one case a member of the Upper, and in the other a member of the Lower, House put this question. I am not able rightly to apprehend the kind of confusion of ideas that could provoke such a question.

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 1980s 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.wikipedia.org/wiki/Lisp_machine) (and I don't recall DARPA instituting any policy like 'no AI', just stopping subsidizing Symbolics and later Connection Machine); 3. the Club of Rome report couldn't've killed its modeling language because it only really acquired its present ill repute by the 1990s, the implementation language Modelica (https://en.wikipedia.org/wiki/Modelica) didn't die (last release: April 2017) and is still in industrial use which is more than almost all languages from the 1960s-1970s can say, and even the World3 model (https://en.wikipedia.org/wiki/World3) analyzed in the report continued development for decades; 4. the Oxford paper (https://www.fhi.ox.ac.uk/wp-content/uploads/The-Future-of-Em...) doesn't make precise forecasts for when any automation may happen (merely saying "associated occupations are potentially automatable over some unspecified number of years, perhaps a decade or two"); 5. the GPU server comparison is really weird as computers have almost always cost more than humans and only relatively recently do any computers' hourly costs fall below minimum wage; and 6. the Dartmouth description is wrong, the conference merely proposed (http://www-formal.stanford.edu/jmc/history/dartmouth/dartmou...) that meaningful progress could be made by 10 researchers, not grad students ("We propose that a 2 month, 10 man study of artificial intelligence be carried out during the summer of 1956 at Dartmouth College...We think that a significant advance can be made in one or more of these problems if a carefully selected group of scientists work on it together for a summer.")

Also, come on dude, Keras isn't hard to use - it's not even comparable to Tensorflow. But at least he didn't tell the tank story.

And there's more where he's plain wrong, like Aluminium.

Despite all that a great antidote to the overhype that I see most days.