Scientists at Los Alamos National Laboratory and the State Key Laboratory for Mechanical Behavior of Materials in China have used a combination of machine learning, supercomputers, and experiments to speed up discovery of new materials with desired properties. The idea is to replace traditional trial-and-error materials research, which is guided only by intuition (and errors). With increasing chemical complexity, the possible combinations have become too large for those trial-and-error approaches to be practical. The scientists focused their initial research on improving nickel-titanium (nitinol) shape-memory alloys (materials that can recover their original shape at a specific temperature after being bent). But the strategy can be used for any materials class (polymers, ceramics, or nanomaterials) or target properties (e.g., dielectric response, piezoelectric coefficients, and band gaps).

Since then, making clothing has become so automated and so cheap, companies can mass produce clothing of all shapes, sizes and colors. Now, when Sirosh needs new clothes, he goes to a department store. That's how he envisions the future of machine... Read More

No one can predict the future of technology with 100 percent accuracy. But these four pillars are certainly at the forefront of innovation in the years ahead. Speaking at a machine learning and artificial intelligence event hosted by Madrona Venture Group in Seattle on Wednesday, Joseph Sirosh, corporate VP of the Data Group at Microsoft, outlined five trends to watch in a world he described as "ACID": Algorithm, Cloud, IoT, and Data. "We live in a time of great change in computing, where unreasonable effectiveness of algorithms, cloud, IoT, and data are changing how applications are built, period," he said. "Even if you are on the right track, if you don't hop on this bandwagon and actually build things and deploy them and take advantage of their strength, you won't be very effective."

This is largely due to the invaluable insights we gain through the analysis of thousands of calls received each day by the typical call centre. With speed being of the essence in making the right decision at the right time for each caller many call centres are turning to machine learning to automate their data analysis and make crucial customer experience decisions within seconds. Whether you're running an inbound or outbound contact centre, the interactions between your company representatives and your customers is a crucial area for customer success. Thanks to machine learning algorithms, businesses are able to manage those customer-facing moments more efficiently. According to techtarget.com, "Emotion analysis through text and speech analytics can paint a more complete picture when combined with the overall first call resolution (FCR) metric, indicating the level of confidence customers feel about whether the answer they received has resolved the issue at hand."

While the human brain is often seen as the ultimate model for robotic intelligence, scientists are also learning plenty from the neurobiological structures and processes of more humble creatures, from fruit flies to rodents. Take the fruit fly – or rather, the maggot that grows up to be a fruit fly. Drosophila fruit fly larvae have fewer than 10 000 neurons – compared to about 100 billion in the human brain. But they display a range of complex orientation and learning behaviours that computational theory does not adequately explain at present. By studying how the larvae change their response to stimuli such as smells when these are associated with reward or punishment, the EU-funded MINIMAL project aims to unpick the exact mechanism underlying learning processes.

Computers have slowly started to encroach on activities we previously believed only the brilliantly sophisticated human brain could handle. IBM's Deep Blue supercomputer beat Grand Master Garry Kasparov at chess in 1997, and in 2011 IBM's Watson beat former human winners at the quiz game Jeopardy. But the ancient board game Go has long been one of the major goals of artificial intelligence research. It's understood to be one of the most difficult games for computers to handle due to the sheer number of possible moves a player can make at any given point. Researchers at Google DeepMind, the Alphabet-owned artificial intelligence research company, announced today that it had created an artificial intelligence system that has beat a professional Go player at the game.

Getting stitched up by Dr. Robot may one day be reality: Scientists have created a robotic system that did just that in living animals without a real doctor pulling the strings. Much like engineers are designing self-driving cars, Wednesday's research is part of a move toward autonomous surgical robots, removing the surgeon's hands from certain tasks that a machine might perform all by itself. No, doctors wouldn't leave the bedside -- they're supposed to supervise, plus they'd handle the rest of the surgery. Nor is the device ready for operating rooms. But in small tests using pigs, the robotic arm performed at least as well, and in some cases a bit better, as some competing surgeons in stitching together intestinal tissue, researchers reported in the journal Science Translational Medicine.

PointGrab has announced a new white paper that explores the impact of deep learning-based smart sensor technology on building automation management. The white paper was developed to help building automation industry stakeholders, from device manufacturers to building managers, better understand the long-term benefits of deep learning-based technology. The paper, "Smarter Sensors: How Deep Learning is Transforming Building Automation," addresses the challenges and opportunities presented by Internet of Things (IoT) device proliferation and data collection and analytics throughout the smart building ecosystem. In this data-rich environment, sensors can be much smarter by sourcing and analyzing richer levels of data and enabling the execution of more sophisticated tasks that go beyond traditional energy consumption management.

OpenAI Gym is a toolkit for developing and comparing reinforcement learning algorithms. This is the gym open-source library, which gives you access to an ever-growing variety of environments. You can use it from Python code, and soon from other languages. If you're not sure where to start, we recommend beginning with the docs on our site. There are two basic concepts in reinforcement learning: the environment (namely, the outside world) and the agent (namely, the algorithm you are writing).

Artificial intelligence, or AI, no longer simply exists in science fiction movies and books. Scientists warn AI has and will continue to change almost every aspect of how people conduct business and live. Researchers say artificial intelligence can be a threat, as well as helpful, to humans. From the iPhone personal assistant Siri, to doing searches on the Internet, to the autopilot function, simple artificial intelligence has been around for some time, but is quickly getting more complex and more intelligent. "If we are going to make systems that are going to be more intelligent than us, it's absolutely essential for us to understand how to absolutely guarantee that they only do things that we are happy with," said Stuart Russell, computer science professor at the University of California Berkeley.