[gwern]

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.

[fnl]

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

[geezerjay]

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.

[fnl]

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.

[MikeGale]

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.

[gwern]

I did notice that one, but aluminum is kind of a complex topic (https://en.wikipedia.org/wiki/Aluminium#Synthesis_of_metal): the early cost was both the chemical processing and the low ore content, and one could charitably read him as referring to discovering bauxite and the electrolysis method, and then he's certainly right about the cost of electricity coming down drastically and making aluminum even cheaper. So not clearly wrong IMO, given that it's an extemporaneous interview.