He continued: "We wanted to see if we could build a system that could learn to play and beat the best Go players by just providing the games of professional players. We are thrilled to have achieved this milestone, which has been a lifelong dream of mine. Our hope is that in the future we can apply these techniques to other challenges -- from instant translation to smartphone assistants to advances in health care."

He continued: "We wanted to see if we could build a system that could learn to play and beat the best Go players by just providing the games of professional players. We are thrilled to have achieved this milestone, which has been a lifelong dream of mine. Our hope is that in the future we can apply these techniques to other challenges -- from instant translation to smartphone assistants to advances in health care."

Some things in life cannot be offset by a mere net gain in intelligence. The last few years have seen the widespread recognition that sophisticated AI is under development. Bill Gates, Stephen Hawking, and others warn of the rise of "superintelligent" machines: AIs that outthink the smartest humans in every domain, including common sense reasoning and social skills. Superintelligence could destroy us, they caution. In contrast, Ray Kurzweil, Google's chief engineer, depicts a technological utopia bringing about the end of disease, poverty and resource scarcity.

Ever since I read "Singularity is Near" I've been fascinated by Ray Kurzweil – his wirings, ideas, a predictions. He's not been afraid to go on the limb and make some brave and seemingly outlandish forecasts about the upcoming technological advances and their oversize impact on people and society. One of the main reasons why I always found his predictions credible is that they can, in a nutshell, be reduced to just a couple of seemingly simple observations: 1. Information-technological advances are happening exponentially, and 2. Information technology in particular is driving all the other technological and societal changes. The rest, to put it rather crudely, are the details. In "How to Create a Mind" Kurzweil zeroes in on just one scientific/technological project – creating a functioning replica of the human mind. He uses certain insights from information technology and neurology to propose his own idea of what human mind (and by extension human intelligence) are all about, and to propose how to go about emulating it "in silico."

Welcome to robot nursery school," Pieter Abbeel says as he opens the door to the Robot Learning Lab on the seventh floor of a sleek new building on the northern edge of the UC-Berkeley campus. The lab is chaotic: bikes leaning against the wall, a dozen or so grad students in disorganized cubicles, whiteboards covered with indecipherable equations. Abbeel, 38, is a thin, wiry guy, dressed in jeans and a stretched-out T-shirt. He moved to the U.S. from Belgium in 2000 to get a Ph.D. in computer science at Stanford and is now one of the world's foremost experts in understanding the challenge of teaching robots to think intelligently. But first, he has to teach them to "think" at all. "That's why we call this nursery school," he jokes. He introduces me to Brett, a six-foot-tall humanoid robot made by Willow Garage, a high-profile Silicon Valley robotics manufacturer that is now out of business. The lab acquired the robot several years ago to experiment with. Brett, which stands for ...

Markov chains, named after Andrey Markov, are mathematical systems that hop from one "state" (a situation or set of values) to another. For example, if you made a Markov chain model of a baby's behavior, you might include "playing," "eating", "sleeping," and "crying" as states, which together with other behaviors could form a'state space': a list of all possible states. In addition, on top of the state space, a Markov chain tells you the probabilitiy of hopping, or "transitioning," from one state to any other state---e.g., the chance that a baby currently playing will fall asleep in the next five minutes without crying first. With two states (A and B) in our state space, there are 4 possible transitions (not 2, because a state can transition back into itself). In this two state diagram, the probability of transitioning from any state to any other state is 0.5.

Note: This is Part 2 of a two-part series on AI. We have what may be an extremely difficult problem with an unknown time to solve it, on which quite possibly the entire future of humanity depends. Welcome to Part 2 of the "Wait how is this possibly what I'm reading I don't get why everyone isn't talking about this" series. Part 1 started innocently enough, as we discussed Artificial Narrow Intelligence, or ANI (AI that specializes in one narrow task like coming up with driving routes or playing chess), and how it's all around us in the world today. We then examined why it was such a huge challenge to get from ANI to Artificial General Intelligence, or AGI (AI that's at least as intellectually capable as a human, across the board), and we discussed why the exponential rate of technological advancement we've seen in the past suggests that AGI might not be as far away as it seems. This left us staring at the screen, confronting the intense concept of potentially-in-our-lifetime ...

Rise of AI 2016 was an event with 100 selected guests (scientists, investors, journalists, corporates, entrepreneurs and thinkers) at 25.02.2016 in Berlin. The topic of the evening was "The Singularity might be closer than you think". The host Fabian Westerheide is giving an introduction to the event and topic.

This reminds quite a bit of this excellent guide on drawing. All is takes is two simple steps.