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.

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 "Berkeley robot for the elimination of tedious tasks," is a friendly-looking creature with a big, flat head and widely spaced cameras for eyes, a chunky torso, two arms with grippers for hands and wheels for feet. At the moment, Brett is off-duty and stands in the center of the lab with the mysterious, quiet grace of an unplugged robot. On the floor nearby is a box of toys that Abbeel and the students teach Brett to play with: a wooden hammer, a plastic toy airplane, some giant Lego blocks. Brett is only one of many robots in the lab. In another cubicle, a nameless 18-inch-tall robot hangs from a sling on the back of a chair. Down in the basement is an industrial robot that plays in the equivalent of a robot sandbox for hours every day, just to see what it can teach itself.

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.

We present a novel approach for real-time facial reenactment of a monocular target video sequence (e.g., Youtube video). The source sequence is also a monocular video stream, captured live with a commodity webcam. Our goal is to animate the facial expressions of the target video by a source actor and re-render the manipulated output video in a photo-realistic fashion. At run time, we track facial expressions of both source and target video using a dense photometric consistency measure. Reenactment is then achieved by fast and efficient deformation transfer between source and target.

What Google has been able to achieve with neural networks is providing us with the building blocks for machine intelligence, laying the groundwork for the next decade of how technology will enhance the way people interact with the world,

"Data Science" is the most exciting research and professional fields these days. It is creating a lot of buzz, both within the academy as well as in the business world. Detractors like to point out that most of the topics and techniques used by people who call themselves Data Scientists have been around for decades if not longer. However, has often been the case that a combination of topics and methodologies becomes important and concrete enough that a truly new subfield emerges. Predictive Modeling is a particularly exciting subfield of Data Science.

On December 2nd, 1942, a team of scientists led by Enrico Fermi came back from lunch and watched as humanity created the first self-sustaining nuclear reaction inside a pile of bricks and wood underneath a football field at the University of Chicago. Known to history as Chicago Pile-1, it was celebrated in silence with a single bottle of Chianti, for those who were there understood exactly what it meant for humankind, without any need for words. Now, something new has occurred that, again, quietly changed the world forever. Like a whispered word in a foreign language, it was quiet in that you may have heard it, but its full meaning may not have been comprehended. However, it's vital we understand this new language, and what it's increasingly telling us, for the ramifications are set to alter everything we take for granted about the way our globalized economy functions, and the ways in which we as humans exist within it. The language is a new class of machine learning known as deep learning, and the "whispered word" was a computer's use of it to seemingly out of nowhere defeat three-time European Go champion Fan Hui, not once but five times in a row without defeat.