One amazing feat of combined neuroscience, engineering and computing was achieved by Edda Bilek, Andreas Myer-Lindenberg and colleagues from the Mannheim Central Institute of Mental Health in Germany. They invented a way to study information flow between human pairs during real-time social interaction, using functional magnetic resonance imaging (fMRI), which measures changes in blood flow in the brain. They were particularly interested in studying joint attention because it arises in early development and is important for social learning.

The AI system, called FlowMachines, works by first analyzing a database of songs, and then following a particular musical style to create similar compositions. The final result does have a human touch, however. In the case of both songs above, French composer Benoît Carré arranged the songs and wrote the lyrics.

Smartphones are quickly evolving from pure information delivery tools into devices with advanced software capable of diagnosing and evaluating a number of health issues. Not only are they being used to expand the reach of mobile eye exams, but with the help of some new research and innovative artificial intelligence tools, they're stepping into the domain of behavioral health. Emotions analytics company Beyond Verbal has launched its Beyond mHealth Research platform in the hopes that it will eventually be able to identify physiological markers in the voice that point to different health-related issues. The project has been getting attention from many in the healthcare and tech communities, including Mark Zuckerberg. According to VentureBeat, the Facebook head has praised the platform for its interesting potential.

Good aim, at the wrong target, is always a miss. This describes much of the current work in Artificial Intelligence: Brilliant minds, clever programmers, amazing algorithms, all pointed at the wrong target with stupefying aim. Despite their brilliance, cleverness, and coding, someone will get hurt if we continue pursuing the type of AI in vogue. A few weeks ago, Google put out guidance for research on preventing harm from AI. This last week, the Federal Government did the same.

The modern world seems really fast and dynamic with a multitude of new products being launched. Marketing agencies are making fortune by monitoring the markets and delivering reports on consumers' opinions. For today, the feedback analysis is a separate area, let's say a growing industry with an array of products and services. And the prices for those services are pretty exorbitant. So, do vendors have a chance to cut down expenses?

PRNewswire has recently reported on ABI Research regarding Mobile Broadband Operators. They are ramping up spending for big data and machine learning as they transform into digital service providers. With a long history of handling huge datasets, and with their path now blazed by the IT ecosystem, mobile operators will devote more than 50 billion to big data and machine learning analytics through 2021, forecasts ABI Research. Machine learning technologies will lead operators to profoundly change how they manage the telecom business. "Machine learning-based predictive analytics are applicable to all aspects of the telecom business," says Joe Hoffman, Managing Director and Vice President at ABI Research.

Machine learning is at the core of many recent advances in science and technology. With computers beating professionals in games like Go, many people have started asking if machines would also make for better drivers or even better doctors. In many applications of machine learning, users are asked to trust a model to help them make decisions. A doctor will certainly not operate on a patient simply because "the model said so." Even in lower-stakes situations, such as when choosing a movie to watch from Netflix, a certain measure of trust is required before we surrender hours of our time based on a model.

Up until very recently, computers needed a complicated and extremely precise set of instructions in order to accomplish even the simplest of tasks. Who among us remembers programming via punch cards? Computer programming languages have evolved over the years, but the biggest step has been moving towards the elimination of complicated programming. In other words, teaching computers to learn for themselves, dubbed machine learning. Because machine learning is such a promising leap forward in technological ability, it has the very real potential to affect every person in every field of business in the near future.

With all the hustle and bustle going on in the world of chatbots at the moment, one crucial event that is very relevant to the future of A.I. has gone relatively unnoticed. If you are interested in chatbots and A.I., you may have heard about the Loebner Prize, which was created by Hugh Loebner and is a form of the Turing Test that Alan Turing first set up in the 1950s. The Loebner Prize is an annual event in which A.I. specialists from around the world come together to play their bots off against a panel of judges in a battle for the most humanlike A.I. The goal is to trick the judges into thinking they are talking to a real person. The event has been hosted all over the world, but since 2014, it has been held in Bletchley Park in the U.K., due to the connection between Alan Turing and Bletchley during the second World War.

Robots are hailed for their intelligence and work efficiency, but excessive heating from prolonged hours of work often affects their performance. To address the heating problem faced by humanoid robots, Japanese researchers have devised an out-of-the-box solution. Using the analogy of sweating that happens in the human body as a result of continuous activity that cools the heated muscles, researchers at the University of Tokyo's JSK Lab presented a novel method at the IEEE/RSJ International Conference on Intelligent Robots and Systems held in South Korea. Their cooling solution addresses the heating problem of a musculoskeletal humanoid robot called Kengoro, which stands at 1.7 meters (5.6 feet) tall and weighs 56 kilograms (123.5 pounds). The Japanese researchers' cooling solution involves tinkering to make the robot "sweat" water straight out of its frame.