I had a recent meeting with a person who was introduced to Machine Learning for the first time. It was interesting to know how someone totally new to the field would interpret what Machine Learning would be. He could instantly connect the term learning with what most Data Scientists would call Reinforcement Learning. A machine could observe phenomena and refine itself by itself is what he thought. It is weird that the one branch of Artificial Intelligence a layman could best connect to is the one least studied.

Today OpenAI, a non-profit artificial intelligence research company, launched OpenAI Gym, a toolkit for developing and comparing reinforcement learning algorithms. It supports teaching agents everything from walking to playing games like Pong or Go. OpenAI researcher John Schulman shared some details about his organization, and how OpenAI Gym will make it easier for AI researchers to design, iterate and improve their next generation applications. John studied physics at Caltech, and went to UC Berkeley for graduate school. There, after a brief stint in neuroscience, he studied machine learning and robotics under Pieter Abbeel, eventually honing in on reinforcement learning as his primary topic of interest.

Movidius, machine intelligence partner to DJI, FLIR, Google and others, is introducing the first ever powerful deep learning processing accelerator that fits into a tiny USB Stick. It connects to existing systems and increases the performance of neural networking tasks by 20-30X. It performs at over 150GFLOPS while consuming under 1.2W. Called the Fathom Neural Compute Stick, It's basically the world's first supercomputer on a USB device. Developers, researchers, hobbyists (think raspberry pie) and anyone developing deep learning applications will benefit from Fathom.

Rajat Monga, TensorFlow Technical Lead & Manager, Google TensorFlow is an open source software library for numerical computation using data flow graphs. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. TensorFlow was developed by researchers and engineers working on the Google Brain Team for the purposes of conducting machine learning and deep neural networks research. He is particularly interested in enabling smarter devices. Prior to Google, as the Chief Architect and Director of Engineering at Attributor, Rajat hired the founding engineering team, and led the labs and operations to design and build Attributor's core matching engine.

Hui Wang has seen the nature of online fraud change a lot in the 11 years she's been at PayPal. In fact, a continuous evolution of methods is kind of the nature of cybercrime. As the good guys catch onto one approach, the bad guys try to avoid detection by using another. Today, said Wang, PayPal's senior director of global risk sciences, "The fraudsters we're interacting with are… very unique and very innovative. In deep learning, though, Wang and her team might have found a way to help level the playing field between PayPal and criminals who want exploit the online payment platform.

I love motivational examples in teaching complex ideas! I use this simple little video to teach Deep Learning to my students. When we consider Deep Learning, we think of ideas like teaching a computer to recognize images of cats using Deep Learning OR training a computer to play pacman using Deep Learning. You let the Deep Learning system iterate with many examples and in each case, you tell the computer using a classifier if its interpretation was correct or not (aka is it a cat or not, Pacman scores, etc.) Now watch the video - video link is lipstick robot. I see the click at the end of the step as a classifier.

The big news on March 12 of this year was of the Go-playing AI-system AlphaGo securing victory against 18-time world champion Lee Se-dol by winning the third straight game of a five-game match in Seoul, Korea. After Deep Blue's victory against chess world champion Gary Kasparov in 1997, the game of Go was the next grand challenge for game-playing artificial intelligence. Go has defied the brute-force methods in game-tree search that worked so successfully in chess. In 2012, Communications published a Research Highlight article by Sylvain Gelly et al. on computer Go, which reported that "Programs based on Monte-Carlo tree search now play at human-master levels and are beginning to challenge top professional players." AlphaGo combines tree-search techniques with search-space reduction techniques that use deep learning. Its victory is a stunning achievement and another milestone in the inexorable march of AI research.

Please make Pull Requests for good resources, or create Issues for any feedback! Seq2Seq solves the traditional fixed-size input problem thatEffective Approaches to Attention-based Neural Machine Translation prevents traditional DNNs from mastering sequence based tasks such as translation and question answering. It has been shown to have state of the art performances in English-French and English-German translations and in responding to short questions. Seq2Seq was first introduced in late 2014 by 2 papers (Sequence to Sequence Learning with Neural Networks and Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation) from Google Brain and Yoshua Bengio's group. The two papers took a similar approach in machine translation, in which Seq2Seq was developed upon.

This review of the fifth and last game of the Google DeepMind challenging match between deep learning AlphaGo and top Go-prof Lee Sedol (9p) is a highlighting game commentary and analysis including short explanations and discussions of the most important moves and positions, many diagrams, images of the match, and very brief commentaries by top Go-profs and Lee Sedol himself. Lee Sedol avoids that AlphaGo will foreclose the bottom right corner and extends his corner (Dia. AlphaGo calculates that it's three stones have enough aji to make a fight meaningful. The program apparently is unfamiliar with a common tesuji (also known as the'tombstone squeeze'; where you offer two stones, subsequently throw in another one, in order to rob the opponent efficiently from the inner liberties). At this point in the game, Demis Hassabis tweeted: "AlphaGo made a bad mistake early in the game (it didn't know a known tesuji) but now it is trying hard to claw it back... nail-biting".

It's a long established tradition for startup founders to fudge their numbers, exaggerate projections, and cherrypick data in meetings with investors. But Venrock health investor Bob Kocher says this approach won't fly with him. "I hear spin every day. I believe I'm lied to more often in Silicon Valley than at the White House," says Kocher, who formerly worked as a special assistant to President Obama to help shape the Affordable Care Act. "I'm looking for entrepreneurs who will level with me."