Artificial intelligence researchers at Google DeepMind are celebrating after reaching a major breakthrough that's been pursued for more than 20 years: The team taught a computer program the ancient game of Go, which has long been considered the most challenging game for an an artificial intelligence to learn. Not only can the team's program play Go, it's actually very good at it. The computer program AlphaGo was developed by Google DeepMind specifically with the task of beating professional human players in the ancient game. The group challenged the three-time European Go Champion Fan Hui to a series of matches, and for the first time ever, the software was able to beat a professional player in all five of the games played on a full-sized board. The team announced the breakthrough in a Nature article published today.

As technology rapidly progresses, some proponents of artificial intelligence believe it will help solve complex social challenges and offer immortality via virtual humans. But AI's critics say we should proceed with caution, that its rewards may be overpromised, and that the pursuit of superintelligence and autonomous machines may result in unintended consequences. Is this the stuff of science fiction? Should we fear AI, or will these fears prevent the next technological revolution? For a different perspective, be sure to check out "We Shouldn't Fear the Promise of Artificial Intelligence."

Before we create a new intelligence in our image, we have to reconsider the fundamental lie that enables human civilization: that nature and culture are separate and distinct, rather than neighbors on the same continuum. At 1:17:00 in the video I refer to a Bruno Latour quote which I think is fundamental to this debate. The full quote is this: "Instead of two powers, one hidden and indisputable (nature), and the other disputable and despised (politics), we will have two different tasks in the same collective. The first task will be to answer the question: How many humans and nonhumans are to be taken into account? The second will be to answer the most difficult of all questions: Are you ready, and at the price of what sacrifice, to live the good life together? That this highest of political and moral questions could have been raised, for so many centuries, by so many bright minds, for humans only without the nonhumans that make them up, will soon appear, I have no doubt, as extravagant as when the Founding Fathers denied slaves and women the vote...There is a future, and it does differ from the past. But where once it was a matter of hundreds and thousands, now millions and billions have to be accommodated--billions of people, of course, but also billions of animals, stars, prions, cows, robots, chips, and bytes... That there was a decade when people could believe that history had drawn to a close simply because an ethnocentric--or better yet, epistemocentric--conception of progress had drawn a closing parenthesis will appear as the greatest and let us hope last outburst of an exotic cult of modernity that has never been short on arrogance."

My interest for DeepMind goes back to its acquisition by Google, in January 2014, for about half a billion dollars. Later in California, I had conversations with Artificial Intelligence and deep learning experts; they said Google had in fact captured about half of the world's best A.I. minds, snatching several years of Stanford A.I. classes, and paying top dollar for talent. Acquiring London startup Deep Mind was a key move in a strategy aimed at cornering the A.I. field. My interlocutors at Google and Stanford told me it could lead to major new iterations of the company, with A.I. percolating in every branch of Google (now Alphabet), from improving search to better YouTube recommendations, to more advanced projects such as predictive health care or automated transportation.

When it comes to communication, humans are hugely sensitive to each other's emotional states. Indeed, most people expect their emotional state to be taken into account by their correspondents. And when this happens, communication tends to be more effective. So if computers are ever to interact effectively with humans, they will need some way of repeating this trick and assessing the emotional state of their interlocutors. Understanding whether an individual has a positive or negative state of mind could make a huge difference to the quality of response that a computer might give.

OutsideIQ develops innovative artificial intelligence solutions that use big data to address complex risk-based questions and problems. ABC and FCPA policies require corporations to uphold proper compliance processes. OutsideIQ provides a fully auditable and sourced due diligence report, allowing corporations to operate without additional changes to their infrastructure. We are occupying a world where data is more than a commodity, it is becoming a currency, providing real value to companies who can efficiently extract data to gain a competitive advantage over their competitors and make better decisions. Over the years, big data technology has been in a revolution, developing new ways to find value in data.

We hear about AI taking over our jobs. We hear about AI listening in on our conversations. We hear about AI becoming a substitute for our romantic partners. Here's what the real AI experts Guru Banavar, (IBM), Toby Walsh, (The University of New South Wales), and Roman Yampolskiy (University of Louisville), say about the subject, and why a lot of what you think you know is probably wrong. Humans 2.0: How the robot revolution is going to change how we see, feel, and talk Robots aren't going to replace us, but by working hand in hand with us they will redefine what it means to be human.

Spam is a kind of messaging where the cost of sending is usually negligible and the receiver and the ISP pays the cost in terms of bandwidth usage. An example of a manual approach to detecting spam is using knowledge engineering. If the subject line of an email contains words'Buy viagra' its spam These rules can be configured by the user himself or by the email provider and if correctly thought out and executed this technique can be effectively be used to combat spam. This is a blog post about one such implementation. However, a manual rules based approach doesn't scale because of active human spammers circumventing any manual rules.

The overeager adoption of big data is likely to result in catastrophes of analysis comparable to a national epidemic of collapsing bridges. Hardware designers creating chips based on the human brain are engaged in a faith-based undertaking likely to prove a fool's errand. Despite recent claims to the contrary, we are no further along with computer vision than we were with physics when Isaac Newton sat under his apple tree. Those may sound like the Luddite ravings of a crackpot who breached security at an IEEE conference. In fact, the opinions belong to IEEE Fellow Michael I. Jordan, Pehong Chen Distinguished Professor at the University of California, Berkeley. Jordan is one of the world's most respected authorities on machine learning and an astute observer of the field. His CV would require its own massive database, and his standing in the field is such that he was chosen to write the introduction to the 2013 National Research Council report "Frontiers in Massive Data Analysis." San Francisco writer Lee Gomes interviewed him for IEEE Spectrum on 3 October 2014. IEEE Spectrum: I infer from your writing that you believe there's a lot of misinformation out there about deep learning, big data, computer vision, and the like. Michael Jordan: Well, on all academic topics there is a lot of misinformation. The media is trying to do its best to find topics that people are going to read about. Sometimes those go beyond where the achievements actually are.

Deep learning is all the rage these days. What exactly is deep learning? Well, it all boils down to neural networks. Neural networks have been around for decades, just that no one used to call them deep networks back then. Now we have all sorts of different flavors of neural networks - deep belief networks (DBNs), convolutional neural networks (CNNs), long short-term memory networks (LSTMs), and more.