1,836 karma · joined June 21, 2011
Helped build and sold a company -- Vidrovr
Working on something new...
https://github.com/danielmorozoff https://danmorozoff.substack.com
In some ways it's an introductory text, but written in a open and accessible way.
Performing scientific experiments is not about winning first place, it's about creating abstractions/ models in order to achieve deeper insight. As such failing to find that your model is wrong is not like loosing a race. Ex: a bunch of fantastic physics was discovered and described with Newtonian mechanics , though inherently that model is flawed, it took a while to realize it.
Here is the paper for those who do not have academic access: http://sci-hub.cc/10.1007/s00127-016-1179-6
Nature: http://www.nature.com.ezproxy.cul.columbia.edu/nm/journal/v2...
Has any work been done in trying to model AI as a deep learning problem, getting testers to play against one another and running feature extractors on their behaviors (a la deep mind) and training NN models on this data?
R is a popular language for doing any form of stats/ learning theory work for research/ academia. Productionwise not as popular.
Not worth nothing...
An interesting example would be the treatment of migraines, and or focal epilepsy using TMS. Migraine treatment with TMS, is believed to set the brain in a controlled firing rhythm, whereas migraines are believed to be caused by uncoordinated neural firing similar to epilepsy but in a milder form.
Recently the FDA has approved such a treatment: http://www.fda.gov/NewsEvents/Newsroom/PressAnnouncements/uc...
It's ironic to think that given everything we think we know about how the brain functions , such a gross and simple procedure as wiring a battery to your head or inducing a magnetic field would have such strong and even beneficial effects.
https://scholar.google.com/scholar?q=transcranial+magnetic+s...
http://arxiv.org/abs/1607.02533
Their method more maximizing the cross entropy yields very peculiar results if you look at the images as the hyper-parameter iterations go up. It seems as if what we are seeing is some convolution with some peculiar form of noise. I wonder what the structure across these adversarial images looks like and whether it could provide some insight into certain patterns in cov net learning?
Truly fascinating why we can't seem to detect dark matter on earth.
If you are interested/live in NYC there is a library with a ton of research materials on currencies/ history from all over the world at the American Numismatic Society's library: http://numismatics.org/Library/Library
Some interesting issues were money in Jewish ghettos / concentration camps during wwii