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One article from this weeks Nature outlook articles about cognitive science. You can even play the cognitive game :). The full set of articles is quite interesting!

Most self-respecting artists wouldn't agree to paint a portrait of a toilet in the middle of a field. Fortunately, advancements in artificial intelligence have given computers the ability to imagine just about any scenario, no matter how bizarre, and illustrate it. Take a look at this image.

Those are some creepy faces among them.. This is also just completely random isn't it?

Well, biased to the data it was trained on. Computing a net is pretty deterministic. But not everything is perfectly correlated yet. Still nice progress.

Just out - I wish I could understand this :(

ignorance is a bliss :)

I know you like these... [Edit] And by the way, this website contains also more practical stuff, like this

of course, "they" being Polish Wikipedia contributors who geo-tag like mad...

Have you had a look on Japan? It looks like they just geo-tagged all their train stations.

Genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning. For paper see: https://arxiv.org/pdf/1712.06567.pdf

Interesting pointers in this one! I would like to explore neuroevolution as well, although it seems extremely resource-demanding?

Not necessarily, I think it can be made to be much faster hybridizing it with backprop and Taylor maps. Its one ideas in the closet we still have not explored (Differential Intelligence: accelerating neuroevolution).

Dataset automatically constructed from public images uploaded by users on FB/Instagram with hashtags used as labels! They refer to this as 'weakly supervised learning'. Then neural net fine-tuned for ImageNet and achieved record 85.4% accuracy.

Took 2000 GPUs over 2 weeks to train :)

If it can be done for obesity, I guess noise is also an option right? :)

love it

Random branches are collected, scanned to 3D, and connected with servos. Then a neural network is trained to control this "robot".

Fascinating insight into the question of how networks learn.

"What if running the 26.2 miles of a marathon only felt like running 24.9 miles, or if you could improve your average running pace from 9:14 minutes/mile to 8:49 minutes/mile without weeks of training?"

I thought the point of running a marathon was to make the effort :)