Gaze is an extremely powerful and important signal during human-human communication and interaction, conveying intentions and informing about other's decisions. What happens when a robot and a human interact looking at each other? Researchers at IIT-Istituto Italiano di Tecnologia (Italian Institute of Technology) investigated whether a humanoid robot's gaze influences the way people reason in a social decision-making context. What they found is that a mutual gaze with a robot affects human neural activity, influencing decision-making processes, in particular delaying them. Thus, a robot gaze brings humans to perceive it as a social signal.

One of the types of learning that we hear about in Machine Learning is Reinforcement Learning, where an agent learns a goal in an environment, known or unknown, through reward and punishment. Unlike learning methods such as Supervised and Unsupervised learning, Reinforcement learning does not require data at all. In my class CS4100, the course briefly touched on the practices of this learning method so I wanted to explore a bit further. A lot of the applications of Reinforcement learning are in games or complex and computationally expensive real-world problems, so it is hard to find something to "meaningfully" apply reinforcement learning to. Nonetheless, this is still a super interesting topic where an agent can build a policy that maximizes the reward function without knowing its environment.

As artificial intelligence continues its rapid progress, equaling or surpassing human performance on benchmarks in an increasing range of tasks, researchers in the field are directing more effort to the interaction between humans and AI in domains where both are active. Chess stands as a model system for studying how people can collaborate with AI, or learn from AI, just as chess has served as a leading indicator of many central questions in AI throughout the field's history. AI-powered chess engines have consistently bested human players since 2005, and the chess world has undergone further shifts since then, such as the introduction of the heuristics-based Stockfish engine in 2008 and the deep reinforcement learning-based AlphaZero engine in 2017. The impact of this evolution has been monumental: chess is now seeing record numbers of people playing the game even as AI itself continues to get better at playing. These shifts have created a unique testbed for studying the interactions between humans and AI: formidable AI chess-playing ability combined with a large, growing human interest in the game has resulted in a wide variety of playing styles and player skill levels.

This is more of an opinion piece. This is a topic that pops up time and again – we should modify the Brood War API to do things in a certain way, so bots will behave more like humans! Bots are having unfair advantages! Let me provide an example: the handling of invisible units. A bot can see an invisible unit if it moves, or attacks.

That should be very good for the companies that are the arms merchants in AI technology, particularly chip companies like Micron Technology (ticker: MU) and Xilinx (XLNX). A new form of computing is emerging, and it demands new chips. The change is every bit as profound as the rise of micro-computing in the 1970s that made Intel a king of microprocessors. It makes Micron and Xilinx more important, but it will probably also lead to future chip stars that aren't public now or may not even have been founded yet. Barron's first explored the new AI in an October 2015 cover story, "Watch Out Intel, Here Comes Facebook."

"What we are seeing today is that there typically exists a bit of a delay when companies start connecting assets and collecting information to be able to rely on machine learning algorithms and their accuracy," says Salminen. "The training of these algorithms requires large amounts of data and thus time. It takes time for any individual company to move through the cycle of starting with very basic use cases and moving onto more complex algorithms and dependencies, and eventually introducing machine learning." He recognizes companies that require warehouses – or those whose supply chains do – currently expect sophisticated IoT solutions from a production and manufacturing point of view. Salminen encourages companies who have examined the cost of IoT solutions for manufacturing or supply chain management over recent years to do so again. "Things are changing at such a pace that it is now very cost efficient even for smaller companies to deploy off-the-shelf IoT solutions for their supply chains as the price of hardware, connectivity and software has dramatically reduced over the last 5 years," he reasons.

A complete revolution in the marketing industry is around the corner. Dubbed by some as the "fourth industrial revolution," AI bots will dominate this new marketing landscape in every conceivable way. Equipped with advanced machine learning technology, they will come up with holistic, data driven digital campaigns. From effective copywriting to lead scoring and churn prediction, bots will do it all. So huge are the marketing potential, that tech giants are betting big on this new technology.

When researchers inside Google's DeepMind artificial intelligence lab first built AlphaGo--the machine that plays the ancient game of Go better than any human--they needed human help. The machine learned to play this exceedingly complex game by analyzing about 300 million moves by professional Go players. Then, once AlphaGo could mimic human play, it reached an even higher level by playing game after game against itself, closely tracking the results of each move. In the end, the machine was good enough to beat the Korean grandmaster Lee Sedol, the best player of the last decade. But then, about a year ago, DeepMind redesigned the system.

At first, Fan Hui thought the move was rather odd. But then he saw its beauty. I've never seen a human play this move," he says. It's a word he keeps repeating. The move was the 37th in the second game of the historic Go match between Lee Sedol, one of the world's top players, and AlphaGo, an artificially intelligent computing system built by researchers at Google. Inside the towering Four Seasons hotel in downtown Seoul, the game was approaching the end of its first hour when AlphaGo instructed its human assistant to place a black stone in a largely open area on the right-hand side of the 19-by-19 grid that defines this ancient game. And just about everyone was shocked. I've never seen a human play this move.' "That's a very surprising move," said one of the match's English language commentators, who is himself a very talented Go player. Then the other chuckled and said: "I thought it was a mistake." But perhaps no one was more surprised than Lee Sedol, who stood up and left the ...

Technological history saw a new advancements in a match of Go between an artificial intelligence developed by Google and 18-time world champion, Lee Sedol in Seoul, South Korea. Five matches were held over a span of a week, starting on March 9 and ending March 15. "Yesterday, I was surprised," said Sedol after his defeat in game two, "but today, more than that, I'm speechless…there was not a moment in time when I felt that I was leading the game." AlphaGo, the AI machine, claimed victory 4–1 against Sedol. The win was a shock as experts had predicted that Go would not be conquered by a machine for another decade.