Gene Cernan, commander of Apollo 17 and the last person to walk on the moon, has passed away. He was also the second American to walk in space during the Gemini 9 mission. A link has been sent to your friend's email address. Gene Cernan, commander of Apollo 17 and the last person to walk on the moon, has passed away. He was also the second American to walk in space during the Gemini 9 mission.

According to the World Health Organization, an estimated 285 million people are visually impaired, with 39 million living with blindness and the other 246 million having low vision. In a world of modern technological advancements, visual impairment has been the subject of much medical research. Perhaps the most notable among these are those that use artificial intelligence (AI), specifically through machine learning. Google's DeepMind has been working with the UK's National Health Service to do ophthalmology research.

Thanks to advanced new machine learning techniques, artificial intelligences are better at performing human tasks than ever. AIs can tell you what's in your photos, beat you at chess, design typefaces, dream up entirely new cities, and even tweet like Donald Trump--often better than the average person. They can't apply what they've learned from one problem to another--which is why even the best AIs are idiot savants: really smart in one arena, and dumb as sticks in all others. So how can AIs reach this elusive general intelligence? OpenAI--an artificial intelligence research nonprofit backed by Microsoft, Elon Musk, and Peter Thiel--thinks it involves AIs playing video games.

In this video, I show you a side project I've been working on. It's a program that uses "NeuroEvolution of Augmented Topologies" to solve OpenAI environments (simple games) with neural networks. By feeding observation data from game environment into my program, over time it's able to learn how to play itself. Since all of my code is open source on Github, anyone can use this to run their own simulations. Hacker House is supported by fans on Patreon.

In this video, we first go over the history of video game AI, then I introduce OpenAI's Universe, which lets you build a bot that can play thousands of different video games. It has environments for all sorts of games, from Space Invaders, to Grand Theft Auto, to Protein folding simulations. For your README, just include a 1-3 sentence description of your strategy and instructions on how to run the code.The demo code can be found in the README of the Universe repo. OpenAI asked me to make this video and I gladly said yes. They are awesome!! Please subscribe! That's what keeps me going.

With the purchase of DeepMind by Google for a rumoured 400 million pounds a chain of events was set off that began a debate in the glare of the media: just how far away was superintelligence, the AI singularity? Elon Musk was an investor in DeepMind, and a reader of Nick Boström's book "Superintelligence" and he became convinced that artificial intelligence was a threat to humanity "We are summoning the demon" he said. To the researchers behind the most recent developments in AI, the idea that our faltering steps towards artificial perceptual systems were anywhere close to a demon seemed ridiculous (speech recognition, object recognition). But the public perception remained and yet others with little knowledge of the technologies underpinng the advances added their voices to the fray. At the post conference banquet for NIPS 2014, a few of us were talking about the potential effect of these discussions on our research.

After suffering its first defeat in the Google DeepMind Challenge Match on Sunday, the Go-playing AI AlphaGo has beaten world-class player Lee Se-dol for a fourth time to win the five-game series 4-1 overall. The final game proved to be a close one, with both sides fighting hard and going deep into overtime. AlphaGo is an AI developed by Google-owned British company DeepMind, and had already wrapped up a historic victory on Saturday by becoming the first ever computer program to beat a top-level Go player. The win came after a "bad mistake" made early in the game, according to DeepMind founder Demis Hassabis, leaving AlphaGo "trying hard to claw it back." By winning the final game despite its blip in the fourth, AlphaGo has demonstrated beyond doubt its superiority over one of the world's best Go players, reaffirming a major milestone for artificial intelligence in the process. It was "the most mindblowing game experience we've had so far," said DeepMind founder Demis Hassabis at the post-match press conference, with an "incredibly close and tense finish." Lee said that he felt sorry the match was coming to an end, while expressing how difficult it has been from a psychological perspective.

Over the past year, Google's DeepMind AlphaGo AI has taken on (and defeated) worldwide Go masters in a series of high-profile matches. But in a sly move similar to a game-playing Turing test, DeepMind recently unleashed AlphaGo on some unsuspecting online Go players, thoroughly trouncing them in the process. The mysterious player, simply called "Master" or "Magister," started showing up on Tygem and FoxGo servers over the past few days and went on to play dozens of matches against some of the top Go players in the world. When Master won more than 50 straight, Go players on Reddit started to catch on, Business Insider reports. So a mysterious AI, 'Master', is trouncing top Go players online 50-0, likely superhuman, and no one knows who created it.

First off, DeepMind's learning agent has a better grasp of controlling pixels on the screen. Google notes it's "similar to how a baby might learn to control their hands by moving them and observing the movements." By doing this, it can figure out the best way to get high scores and play games more efficiently. Additionally, the agent can now figure out rewards from a game based on past performance. "By learning on rewarding histories much more frequently, the agent can discover visual features predictive of reward much faster," Google says.

Researchers from Google's DeepMind division say they have shown how their differentiable neural computer is able to process information using so-called'slow' thinking thought processes. Researchers are successfully teaching machines to process information in ways that emulate the subtleties and complexities of human thought processes.