A team of researchers at Google's Deep Mind London project, has taught animated players how to play a realistic version of soccer on a computer screen. In their paper published in the journal Science Robotics, the group describes teaching the animated players to play as solo players and also in teams. For several years, robot engineers have been working diligently to create robots capable of playing soccer. Such work has resulted in competition between various groups to see who can devise the best robot players. And that has led to the creation of RoboCup, which has several leagues, both in the real world and simulated.

Dutch and Spanish computer scientists have discovered how systems that use artificial intelligence (AI) learn in practice. In many systems that are based on so-called'deep learning', it was not clear how that learning process actually took place. The researchers have now managed to figure out how an image recognition system learns about its environment. Then they simplified that learning system by forcing it to focus on less important information as well. AI systems for image recognition are of great importance for autonomous driving cars, for a start.

Our ecosystem is changing fast, and your ability as a leader to clearly distinguish between the powers of emerging technologies is important for the success of your business. Poor investments into new ventures can threaten your competitiveness and waste valuable resources. If you invest into an AI venture, but you treat it as a software venture, then you are doing it wrong. Despite the huge business potential of AI technologies, many AI ventures are poorly executed and miss significant business opportunities. There are many reasons for this poor execution, e.g., ill-prepared culture and strategy, insufficient access to talent, and poor data and infrastructure preparedness.

Artificial Intelligence is a broad term that encompasses many techniques, all of which enable computers to display some level of intelligence similar to us humans. The most popular use of Artificial Intelligence is robots that are similar to super-humans at many different tasks. They can fight, fly, and have deeply insightful conversations about virtually any topic. There are many examples of robots in movies, both good and bad, like the Vision, Wall-E, Terminator, Ultron, etc. Though this is the holy grail of AI research, our current technology is very far from achieving that AI level, which we call General AI.

In a paper published on the preprint server Arxiv.org, They claim the system can extrapolate to interactions it hasn't seen before, like those involving multiple shirts and pants, enabling it to make long-term predictions. Causal understanding is the basis of counterfactual reasoning, or the imagining of possible alternatives to events that have already happened. For example, in an image containing a pair of balls connected to each other by a spring, counterfactual reasoning would entail predicting the ways the spring affects the balls' interactions. The perception model is trained to extract certain keypoints (areas of interest) from videos, from which the interference module identifies the variables that govern interactions between pairs of keypoints.

Artificial intelligence (AI) is rapidly entering health care and serving major roles, from automating drudgery and routine tasks in medical practice to managing patients and medical resources. As developers create AI systems to take on these tasks, several risks and challenges emerge, including the risk of injuries to patients from AI system errors, the risk to patient privacy of data acquisition and AI inference, and more. Potential solutions are complex but involve investment in infrastructure for high-quality, representative data; collaborative oversight by both the Food and Drug Administration and other health-care actors; and changes to medical education that will prepare providers for shifting roles in an evolving system. The flashiest use of medical AI is to do things that human providers--even excellent ones--cannot yet do. For instance, Google Health has developed a program that can predict the onset of acute kidney injury up to two days before the injury occurs; compare that to current medical practice, where the injury often isn't noticed until after it happens.2

Artificial intelligence machines are good at what they do, but how smart are they really? A supercomputer toppling a grandmaster at chess is old hat, with IBM's Deep Blue beating Garry Kasparov at the end of the last millennium. DeepMind's artificial intelligence system AlphaGo beat the world champion in 2016 at the even more complex Go -- and then AlphaGo Zero, which learned the game by teaching itself rather than by playing against humans, wiped the floor with AlphaGo. In recent years, AI systems, whether used in games, medical research or self-driving cars, have shown an extraordinary ability to learn and learn fast -- AlphaGo Zero defeated the version of AlphaGo that had beaten the world champ just three days after it started learning the game. See Hava Siegelmann discuss AI's role in national security at the Dec. 14 CXO Tech Forum.

One taxi driver said he had noticed a 20 percent rise in passenger traffic since using the AI prediction system. In downtown Tokyo, just like in most major cities, taxi drivers make an educated guess as to where they might find their next paying passenger. Years of experience have honed their prediction skills to pinpoint customers depending on location, the time of day and weather. But even with this knowledge, tracking down passengers is still a hit and miss science with many drivers saying they can cruise around for up to two hours without finding a fare. Japan's biggest mobile phone operator, NTT Docomo, wants to change all that.