Machine learning is the automation of discovery, and it is responsible for making our smartphones work, helping Netflix suggest movies for us to watch, and getting presidents elected. But there is a push to use machine learning to do even more--to cure cancer and AIDS and possibly solve every problem humanity has. Pedro Domingos is at the very forefront of the search for the Master Algorithm, a universal learner capable of deriving all knowledge--past, present and future--from data. In this book, he lifts the veil on the usually secretive machine learning industry and details the quest for the Master Algorithm, along with the revolutionary implications such a discovery will have on our society. Pedro Domingos is a Professor of Computer Science and Engineering at the University of Washington, and he is the co-founder of the International Machine Learning Society.

Unexpected convergent consequences…this is what happens when eight different exponential technologies all explode onto the scene at once. This post (the second of seven) is a look at artificial intelligence. Future posts will look at other tech areas. An expert might be reasonably good at predicting the growth of a single exponential technology (e.g., the Internet of Things), but try to predict the future when A.I., robotics, VR, synthetic biology and computation are all doubling, morphing and recombining. You have a very exciting (read: unpredictable) future. This year at my Abundance 360 Summit I decided to explore this concept in sessions I called "Convergence Catalyzers." For each technology, I brought in an industry expert to identify their Top 5 Recent Breakthroughs (2012-2015) and their Top 5 Anticipated Breakthroughs (2016-2018). Then, we explored the patterns that emerged.

Inbox by Gmail has a few useful features but probably the most interesting is Smart Reply, which was expanded to the desktop this week. As impressive as the A.I.-powered suggestions for replies to your emails are, they're just not very useful yet. Sure, Smart Replies tend to offer relevant options, but they're not very useful in most real-world cases. Open an invitation to party and you might get'Sounds good!,' 'Can't wait,' and Sorry, I can't be there!' as the suggestions. By the time I've added'Hi Bob,' and a few other bits of personalization, I might as well have written the whole thing myself.

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."

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.

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.

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.