It's been a long time worry that when AI gains a certain level of autonomy it will see no use for humans or even perceive them as a threat. A new study by Google's DeepMind lab may or may not ease those fears. There are two unmistakable sides to the debate concerning the future of artificial intelligence. The researchers at DeepMind have been working with two games to test whether neural networks are more likely to understand motivations to compete or cooperate. They hope that this research could lead to AI being better at working with other AI in situations that contain imperfect information.

How can artificial intelligence transform businesses? From real-world data, computers are learning to recognize patterns too complex, too massive, or too subtle for hand-crafted software or even humans. They and the Cleveland Cavaliers, the 2016 champs, use AI to put their teams at the top of their game. Retailers have been among the most active adopters of deep learning-powered intelligence. The consulting firm Gartner predicts that by 2020, 85 percent of customer interactions in retail will be managed by AI.

It happened with Chess and Go, and it finally happened with eSports. Elon Musk-backed Artificial Intelligence company "OpenAI" just used a bot to wallop the best DOTA2 players in the world. To be honest, it wasn't even close. Instead of trying to program the perfect bot, OpenAI just created a bot that learned through trial and error. Over the course of playing thousands of games against itself, the bot kept the behaviors that lead to victory and shed the ones that got it killed.

This article is featured in the new DZone Guide to Artificial Intelligence. Get your free copy for more insightful articles, industry statistics, and more! I have spoken to thought leaders at a number of large corporations that span across multiple industries such as medical, utilities, communications, transportation, retail, and entertainment. They were all thinking about what they can and should do with deep learning and artificial intelligence. They are all driven by what they've seen in well-publicized projects from well-regarded software leaders like Facebook, Alphabet, Amazon, IBM, Apple, and Microsoft.

Google's artificial intelligence group, DeepMind, has unveiled the latest incarnation of its Go-playing program, AlphaGo – an AI so powerful that it derived thousands of years of human knowledge of the game before inventing better moves of its own, all in the space of three days. Named AlphaGo Zero, the AI program has been hailed as a major advance because it mastered the ancient Chinese board game from scratch, and with no human help beyond being told the rules. In games against the 2015 version, which famously beat Lee Sedol, the South Korean grandmaster, in the following year, AlphaGo Zero won 100 to 0. The feat marks a milestone on the road to general-purpose AIs that can do more than thrash humans at board games. Because AlphaGo Zero learns on its own from a blank slate, its talents can now be turned to a host of real-world problems. At DeepMind, which is based in London, AlphaGo Zero is working out how proteins fold, a massive scientific challenge that could give drug discovery a sorely needed shot in the arm.

Apparently, there was an overwhelming response to my first blog so I get to write another one. Thanks again to both of you who read it. Given all the doom and gloom about AI in the media recently, in this episode I'd like to talk (write) about some of the positive things going on with a branch of AI known as "deep learning". Way back in ancient times when computers were just getting started in the '60s, while some scientists worked to perfect the lava lamp, others invented something called a neural network. Neural networks were loosely modeled after how they thought the brain worked back then, with an input and output and in between hidden layers of artificial neurons.

The article is based on a paper about the role of A.I. in Precision Medicine that was published in Expert Review of Precision Medicine and Drug Development. Classical medical practice puts large groups of people in their focus and tries to develop clinical solutions, drugs or treatment based on the needs of the statistical average person. Disruptive technologies change that perspective completely. The basis of that transformation is data. Physicians are able to collect a vast amount of medical information about the individual through cheap genome sequencing, big data analytics, health sensors, wearables or artificial intelligence.

Google has developed a computer program that teaches itself. The company's AI division, DeepMind, has unveiled AlphaGo Zero, an extremely advanced system that managed to accumulate thousands of years of human knowledge within days. DeepMind says it's the most powerful program it has created, because it isn't "constrained by the limits of human knowledge". AlphaGo Zero is the latest evolution of AlphaGo, the first computer program to ever defeat a world champion at the ancient Chinese game of Go. Unlike previous versions of AlphaGo, however, Zero was only provided with the rules of the game. It had to learn how to play all by itself, whereas the others were trained using data from thousands of games played by humans.

It is able to do this by using a novel form of reinforcement learning, in which AlphaGo Zero becomes its own teacher. The system starts off with a neural network that knows nothing about the game of Go. It then plays games against itself, by combining this neural network with a powerful search algorithm. As it plays, the neural network is tuned and updated to predict moves, as well as the eventual winner of the games. This updated neural network is then recombined with the search algorithm to create a new, stronger version of AlphaGo Zero, and the process begins again.

The "Hey Siri" feature allows users to invoke Siri hands-free. A very small speech recognizer runs all the time and listens for just those two words. The "Hey Siri" detector uses a Deep Neural Network (DNN) to convert the acoustic pattern of your voice at each instant into a probability distribution over speech sounds. It then uses a temporal integration process to compute a confidence score that the phrase you uttered was "Hey Siri". If the score is high enough, Siri wakes up. This article takes a look at the underlying technology.