I was listening to The Economist “Babbage” podcast yesterday, and was really struck my something Timoni West said. She mentioned Arthur C. Clarke’s Third Law - “Any sufficiently advanced technology is indistinguishable from magic”. She then said; “The reverse is also true - any sufficiently rigorous technology doesn’t feel like technology any more”.
Today I was at a Computation meets Data Science Conference, organised by Wolfram Research and the CQF. There were some interesting talks. The ones I enjoyed the most used Mathematica to analyse data in real time in interesting ways. It looks like Mathematica has good support for building neural networks now. I was impressed at how quickly Jon Macloone from Wolfram was able to get some quite useful neural network models up and running. Jon made the point that for some problems you are able to get results really quickly with neural nets, and others it's really hard to get good results, and it's not obvious which problems are which.
I've been playing more with Neural-Style Transfer. It's fun to play with, but with the code I'm using, I'm struggling to get results I can use. I was trying to merge the style of a Platon photo with a photo taken of me (when I was growing a beard). It was weird what information the neural net decided to take from the style photo.
I just started experimenting with Image-Style Transfer. I've been excited about it for a long time, but reading this code on Nvidia's latest paper prompted me to start playing with it in earnest. Of course, in the Coursera Deep Learning courses we studied this as well. As I don't have an Nvidia card installed on my notebook, I started off with this Torch Implementation.
There is a great graph-filled post over at Slate Star Codex called Technological Unemployment - much more than you wanted to know. After analysing a lot of data from the US economy, the author arrives at some tentative conclusions: The main point seems to be that the evidence for large-scale technological unemployment is mixed. There is evidence of technological underemployment however. There are signs that people are now struggling to adjust. The final paragraph is:
"This is a very depressing conclusion. If technology didn’t cause problems, that would be great. If technology made lots of people unemployed, that would be hard to miss, and the government might eventually be willing to subsidize something like a universal basic income. But we won’t get that. We’ll just get people being pushed into worse and worse jobs, in a way that does not inspire widespread sympathy or collective action. The prospect of educational, social, or political intervention remains murky."
I read this post on QUIC over at LWN. QUIC is a protocol that multiplexes network connection streams on top of UDP (to get through routers). I had no idea that it was actively used in production with YouTube! Apparently the YouTube mobile app uses QUIC for streaming videos. According to Jana Iyengar (from Google) around 35% of outbound traffic is happening using QUIC.
Today I discovered the sheer awesomeness that is Emacs with EIN. This lets my Emacs environment to to Jupyter Notebooks. Through it, I have the power of Emacs Python completion and editing while writing iPython functions. It works really well! I can display matplotlib graphs inline in my Emacs buffer. There is even symbolic computation via the sympy package! Bliss!