Big data and machine learning
blogs.law.harvard.edu
blogs.law.harvard.edu
And if it is sarcastic what exactly is he mocking ?
I guess that most HN readers would be unfamiliar the tools of the trade for big data and machine learning.
http://philip.greenspun.com/humor/ai.text
The AI field has been a prolific source of hokey new
terminology
...
AI is about the same age as the rest of computing.
...
If DOD spending on AI drops far enough, universities
like Stanford, MIT and CMU may even find the integrity
to rid themselves of scientifically embarassing, but
formerly profitable, AI programs. The quality of CS
faculties and budgets at universities across the
country will continue to be diluted by the presence of
large numbers of AI meatballs.
-- Gary Martins (former RAND manager)I'm not sure if this will always be true. I think that if we move toward a sort of neural network programming (1) we won't be able to "know why" in the sense that we can explain the functions, but will instead explain programs based on output from the layers and from the relationships between variables. For instance, deep dream is a way to understand how google's image classifier is "working" (2). It's super interesting that the classifier thinks that a human arm is part of a dumbbell, for instance.
1) Maybe something like this: http://web.mit.edu/~axch/www/art.pdf.
2) http://googleresearch.blogspot.ch/2015/06/inceptionism-going...
Google is well aware of the problems of machine learning: