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.

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?

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."

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.

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.

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.

The number of new malware variations that pop up each day runs somewhere between 390,000 (according to AV-TEST Institute) and one million (according to Symantec Corporation). These are new strains of malware that have not been seen in the wild before. Even if we consider just the low end figure, the situation is still dire. Google Now is about to get a lot better in the future, aiming to serve Android users even when they're offline. With smartphones increasingly gaining ground, digital assistants have become widely popular and heavyweight companies are competing to deliver the best software in this category.

Machine learning is an obvious complement to a cloud service that also handles big data. Often the major reason to collect massive amounts of observables is to predict other values of interest to the business. For example, one of the reasons to collect massive numbers of anonymized credit card transactions is to predict whether a new transaction is valid or fraudulent with some likelihood. It's no surprise then that Microsoft, with a large AI research department, would add machine learning facilities to its Azure cloud. Perhaps because the technology originated with the researchers, the commercial offering has all of the complex models and algorithms that a statistics and data weenie could want.

Graphics processors power more than the likes of Call of Duty: Black Ops III; they also provide the number-crunching for modern machine learning systems. But GPUs are largely proprietary hardware devices, led in the market by Nvidia, which is notorious for its poor reputation as an open source player. Leave it to Nvidia's competitor AMD, long beleaguered by slumping sales and shrinking market share, to develop a plan with the partial goal of advancing the state of GPU-accelerated high-performance computing. Thus, while AMD hopes to make GPU programming less of a black box with GPUOpen, the company is trying to rescue its own business as well. After all, AMD's reputation with open source users is also shaky, thanks to unfulfilled promises.