PREDICTING HOW artificial intelligence technology will evolve in the following ten or 20 years, or even beyond, is very difficult to say the least. However, certain is the fact that there is much to be gained to go around for everyone. It is estimated that by the year 2018, robots will literally be supervising more than three million of us at work; and by 2020, smart machines will become a major investment priority amongst at least 30% of all CIOs. Right now, many different fields, spanning from customer service to journalism, are already being set aside by increasingly able AI that can replicate human abilities and experience. Already before our eyes is an aspect that we once thought only belonged in future technology.

A Canadian startup called Lyrebird has announced that it has developed a platform capable of mimicking human voice with a fraction of the audio samples required by other platforms such as Google DeepMind and Adobe Project VoCo. The Lyrebird synthesis software requires only 60 seconds of sample audio to produce it's synthetic sample. VoCo needs about 20 minutes to do the same. The quality of the voice reproductions that the software can make are mixed. Some are better than others.

It is a scene we all dread – an enormous space rock colliding with Earth, causing widespread chaos and destruction. And experts have warned that the dreaded scenario could become a reality this year. A leading astronomer from Nasa has tweeted a list of five known asteroids expected to fly scarily close to Earth within the coming year. It is a scene we all dread – an enormous space rock colliding with Earth, causing widespread chaos and destruction. And experts have warned that the dreaded scenario could become a reality this year (artist's impression) He said: 'A list of known asteroid close approaches to Earth - less than five Lunar Distance - within the coming year', along with a table detailing the five asteroids.

Soren Schwertfeger finished his postdoctorate research on autonomous robots in Germany and seemed set to continue his work in Europe or the United States, where artificial intelligence was pioneered and established. Instead, he went to China. "You couldn't have started a lab like mine elsewhere," Schwertfeger said. The balance of power in technology is shifting. China, which for years watched enviously as the West invented the software and the chips powering today's digital age, has become a major player in artificial intelligence, what some think may be the most important technology of the future.

Multi-target tracking (MTT) is focused on the accurate detection and localization for multiple dynamic targets when measurements from these targets often come from numerous spatially distributed sensors. Obtaining the locations of the targets can be complex when sensors have limited sensing capabilities. Due to the potential applications of MTT, MTT can be dated back to 1960's initially related to aerospace applications [1]. The theoretical advances in MTT, new sensor capabilities, and more computational power have made it possible to apply MTT in numerous applications such as surveillance [2], [3], computer vision [4], [5], network and computer security [6] and sensor network [7]. In general, solving the MTT problem involves three tasks: (i) Extraction - extract target related information from the raw data acquired from the sensors; (ii) Data association - identify each target's corresponding measurements; and, (iii) Estimation - estimate the position of targets via single target tracking techniques (as shown [8]-[10]). Perhaps the most challenging task is to conduct data association because if data associated with each target is determined, it becomes much easier to conduct estimation for each individual target. In this paper, our focus is also on the data association problem. The main objective of this paper is to investigate the applicability of machine learning algorithms for the data association problem and then develop a new multi-layer learning algorithm by leveraging the advantages of different machine learning algorithms.

Researchers have unveiled a new way to test self driving cars and says it could allow them to perform he equivalent of 100 million miles of driving in just 1,000. The researchers at the University of Michigan say their find would allow manufacturers to bypass the billions of miles they would need to log for consumers to consider them road-ready. The process, which was developed using data from more than 25 million miles of real-world driving, can cut the time required to evaluate robotic vehicles' handling of potentially dangerous situations by 300 to 100,000 times, saving 99.9 percent of testing time and costs, the researchers say. Researchers at the University of Michigan say their find would allow manufacturers to bypass the billions of miles they would need to log for consumers to consider them road-ready. The new accelerated evaluation process breaks down difficult real-world driving situations into components that can be tested or simulated repeatedly, exposing automated vehicles to a condensed set of the most challenging driving situations. In this way, just 1,000 miles of testing can yield the equivalent of 300,000 to 100 million miles of real-world driving.While 100 million miles may sound like overkill, it's not nearly enough for researchers to get enough data to certify the safety of a driverless vehicle.

Trust is at the bedrock of our human social system. We need it for trade, politics, human interactions and for many more things. Hillary Clinton lost the 2016 US elections1 for the lack of it. Another increasing trend is that more and more companies are building machine learning models for trust. Yet as trust is becoming increasingly crucial, it is becoming harder to build systems for automated trust decisions. The number of decisions are far too many for any enterprise to tackle manually.

The insideBIGDATA Guide to Deep Learning & Artificial Intelligence is a useful new resource directed toward enterprise thought leaders who wish to gain strategic insights into this exciting area of technology. In this guide, we take a high-level view of AI and deep learning in terms of how it's being used and what technological advances have made it possible. We also explain the difference between AI, machine learning and deep learning, and examine the intersection of AI and HPC. We present the results of a recent insideBIGDATA survey that reflects how well these new technologies are being received. Finally, we take a look at a number of high-profile use case examples showing the effective use of AI in a variety of problem domains.

Close-up image from NASA's Galileo spacecraft of a portion of Europa's icy surface, cracked in patterns suggesting floating sea ice. NASA is setting its sights on getting a much closer, deeper look at Jupiter's tantalizing moon, Europa, and the mysterious ocean hidden beneath its icy crust. With two orbital missions already in the works, by NASA and the European Space Agency, NASA is looking further into the future toward a possible mission to put a robot on the surface. Europa's ocean, which may lie under only a few miles of ice--perhaps only a few hundred feet in some places--may be as deep as 30 miles, and contains more water than in all of Earth's oceans. With the possibility of some form of hydrothermal vents supplying heat and life-supporting chemicals on the ocean's floor, like those on Earth, the tiny moon has become one of the hottest subjects in the search for extraterrestrial life in the solar system.

Mobile health apps and wearable devices that use artificial intelligence to help diagnose or even treat medical conditions pose a new regulatory challenge for the U.S. Food and Drug Administration. The government agency has responded by starting to assemble a team of computer scientists and engineers to help oversee and anticipate future developments in AI-driven medical software. This comes at a time when medical devices have evolved from fairly self-contained gadgets into implants and wearables that communicate wirelessly with medical software on separate computers or in the cloud. The definition of medical device has also stretched as smartphone apps and online services--often backed by machine-learning algorithms--promise to deliver medical diagnoses that once would have required a visit to a doctor's office and specialized lab equipment. That is why the FDA aims to create a new digital health unit around people having both the technical expertise and industry experience to understand how machine learning AI and related subjects such as big data, cybersecurity, and cloud computing will all affect health care for Americans.