http://www.theverge.com/2015/11/25/9798448/neural-network-describe-live-video-neuraltalk

This is soooo coool

Now NN can generate new faces. Just like in Matrix movie

https://m.youtube.com/watch?feature=youtu.be&v=GVe6kfJnRAw

Yan LeCunn's neural network taught itself basic arifmetic operations.

https://www.youtube.com/watch?v=4qMeVgy6CqA

Detecting Tumor Cells in Blood Samples

https://engineering.pinterest.com/blog/introducing-new-way-visually-search-pinterest

Notes of Pinterest engeneers on visual search

Direct arxiv paper link:

http://arxiv.org/abs/1505.07647

http://blogs.nvidia.com/blog/2015/11/25/compute-the-cure-3/

Deep Learning for cancer research

Finally, there is Reinforcement Learning for MOBA (games like DoTA / Heroes of the Storm / League Of Langues). It was published several months ago in Korea , which demonstrates squad of agents fight each other in league of legend like mini game. (6 layers with LSTM/MaxOut).

The mini game has two champions which have unique skill sets and attributes, which can buff/debuff targets including themselves.

So, there is a hot start for those, who want to start digging in this direction

https://onedrive.live.com/view.aspx?resid=166F2AF156F7AB19!1089&ithint=file%2cpptx&app=PowerPoint&authkey=!AA1FLzme4BNhWUE

https://www.youtube.com/watch?v=e1eTJvS_Inw

Microsoft believes that in 5 years Siri will understand human speach.

With all accents, idioms and sarcasms.

These advances may lead to new interfaces for cars, watches and even PCs. The internet of things will become something more than a concept.

http://www.businessinsider.com/microsoft-chief-scientist-xuedong-huang-on-the-future-of-speech-recognition-2015-12

Today ended a Megaface competiotion on recognition of faces.

The competition was about precision — teams had to find most similar faces.

There is a russian startup beating google — that's gonna hit every newspaper, so feel free to share this message to your friends.

Yes, you can beat a great company, even if you are in a small team.

It's not always about the size of a dog in a fight, it's about the size of a fight in a dog.

results:
http://megaface.cs.washington.edu/results/
paper:
http://arxiv.org/abs/1512.00596

We show that an end-to-end deep learning approach can be used to recognize either English or Mandarin Chinese speech--two vastly different languages. Because it replaces entire pipelines of hand-engineered components with neural networks, end-to-end learning allows us to handle a diverse variety of speech including noisy environments, accents and different languages. Key to our approach is our application of HPC techniques, resulting in a 7x speedup over our previous system. Because of this efficiency, experiments that previously took weeks now run in days. This enables us to iterate more quickly to identify superior architectures and algorithms. As a result, in several cases, our system is competitive with the trannoscription of human workers when benchmarked on standard datasets. Finally, using a technique called Batch Dispatch with GPUs in the data center, we show that our system can be inexpensively deployed in an online setting, delivering low latency when serving users at scale.

New paper by Andrew Ng

Long awaited recognition of the upcoming AI hype. Interview with leading Deep Learning developers in the NY times

http://www.nytimes.com/2015/12/11/science/an-advance-in-artificial-intelligence-rivals-human-vision-abilities.html?_r=1

Guys from google did that every data scientists thought of since ImageNet release — they made a tool, which can classify all the images in your collection and tag them with the objects present on the images, so you can search through you memories just with typing keywords.

http://recode.net/2015/12/09/ex-googlers-take-on-google-photos-with-machine-smarts/

OpenAI is a non-profit artificial intelligence research company, which recieved 1 bil dollars for AI research.

https://openai.com/blog/introducing-openai/

This is the recording from July 23rd SF Machine Learning Meetup at Workday Inc. San Francisco office.

Featured speaker - Ilya Sutskever
Ilya Sutskever received his PhD in 2012 from the University of Toronto working with Geoffrey Hinton. He was also a post-doc with Andrew Ng at Stanford University. After completing his PhD, he cofounded DNNResearch with Geoffrey Hinton and Alex Krizhevsky which was acquired by Google. He is interested in all aspects of neural networks and their applications.

https://clip.mn/video/yt-aUTHdgh1OjI

There is even a deep learning channel on the youtube

https://www.youtube.com/watch?v=P2HPcj8lRJE

Yoshua Bengio:
A must-read for those interested in dialogue research, with an overview of available corpora for learning from them:

http://arxiv.org/abs/1512.05742

We have an annoucement to make.

Russian Deep Learning community is quite excited and enthusiastic about the recent Kaggle challenge put forward by Allen Institute for Artificial Intelligence (https://www.kaggle.com/c/the-allen-ai-science-challenge). Backed by a large interest group here in Moscow, we want to build off of this initiative by organising a Winter school paired with an AI-hackathon - http://qa.deephack.me . Collaborative work of many teams forms a powerful educational environment that can stimulate people to learn and work better, and may in the end lead to discoveries that would have been overlooked otherwise.

Based on our prior experience we expect a successful event! The last event like that we have organized—a week-long hackathon to improve DeepMind code to play Atari games (see http://deephack.me ) — did well. It was an academic, free for participants but competitive event that combined hacking with a crash course of educational lectures by +Yoshua Bengio, Andrey Dergachev , Alexey Dosovitski, Vitali Dunin-Barkovskyi , +Terran Lane, +Anatoly Levenchuk, +Sridhar Mahadevan , Maxim Milakov, +Sergey Plis, +Irina Rish, +Ruslan Salakhutdinov, +Jürgen Schmidhuber, +Thomas Unterthiner, Dmitri Vetrov, Alexander Zhavoronkov. The winning team was awarded with a trip to NIPS and their paper based on their work got accepted to a NIPS workshop. In fact, many other participants were inspired enough to come to NIPS on ther own.

We invite everybody who are interested in participation as a hacker or a speaker :)

More details (and registration form) can be found at http://qa.deephack.me