Not only have robots been able to use GPS waypoint technology to travel from one location to another, but the systems have slowly learned how to maneuver independently around other objects or obstacles in real time. Systems like the well-known Packbot progressively leveraged technology to use different software packages for different sensing or detection missions with greater levels of autonomy. The Army is transforming its fleet of transportable robots to a common set of standards to expedite modernization, interoperability, autonomy and mission flexibility. During the last decade and a half of ground wars in Iraq and Afghanistan, the Army acquired and fast-tracked as many as 7,000 unique robotic systems in an effort to keep pace with the emerging threat of enemy IEDs. Building upon these developments, which included the deployment of multiple transportable cave- and road-clearing robots, the service now seeks to architect design a common fleet with a single robotic chassis configurable to a wide range of varying missions, Bryan McVeigh, the Army's project manager for Force Projection, said in a service statement.

Watershed technologies like AlphaGo make it easy to forget that artificial intelligence (AI) isn't just a futuristic dream. Sensing traffic lights, fraud detection, mobile bank deposits, and, of course, internet search -- each of these technologies involves AI of some kind. As we have grown used to AI in these instances, it has become part of the scenery -- we see it, but we no longer notice it. Expect that trend to continue: As AI grows increasingly ubiquitous, it'll become increasingly invisible. Major advancements in technologies dependent on AI -- like robotics, machine vision, natural-language processing, and machine learning -- will soon work their way into our daily lives. AI's integration into our world will transform employment, economic activity, and possibly the character of our society. Healthcare is ground zero for AI. In fact, AI has been quietly helping doctors treat diseases for almost its entire existence. In 1963, a Midwestern radiologist named Gwilym S. Lodwick published a paper in Radiology Society of America that described a technique he invented for predicting the survival span of lung cancer patients: Lodwick took X-rays and coded their features to represent tumor characteristics using numerical values. Then, as he explained, these numbers could "be manipulated and evaluated by the digital computer." Armed with (rudimentary) image processing, in the 1970s radiologists began using machine vision to generate data directly from images. These were the logic-based days of early AI, so algorithms followed a sequence of rules to identify body parts: If there's an oval here attached to a thick line, we're looking at a hip bone connected to a thigh bone. Lodwick called his technique "computer-aided diagnosis," and CAD has been an invisible tool of medicine ever since. By the 1980s and 1990s, doctors were using CAD to give them a second opinion for diagnosing everything from lumbar hernias to gastric pain.

In 1943, at the height of World War II, the U.S. military hired an audacious psychologist named B.F. Skinner to develop pigeon-guided missiles. These were the early days of munitions guidance technology, and the Allies were apparently quite desperate to find more reliable ways to get missiles to hit their targets. It went like this: Skinner trained pigeons to peck at an image of the military target projected onto a screen. Whenever their beaks hit the moving target dead center, he rewarded the birds with food pellets. Once the pigeons had learned how to peck at targets, they earned their wings: Skinner would strap three of his little pilots into a missile cockpit specially fitted with straps attached to gyroscopes that would steer the bomb. Now, when American jets released their pigeon-filled bombs, the birds would peck at an image of the bomb's target, their little straps twisting and bending, gyroscopes whirling, guiding the bomb and the birds to their final resting place. Used with permission of the artist. The military eventually pulled the plug on Project Pigeon, while Skinner continued to develop a discipline that came to be known as behavioral psychology. He just wanted to discover how to train animals (and his children) using scientific techniques of stimulus, reward, and punishment. Over the past three years, using techniques similar to those pioneered by Skinner, DeepMind has developed some of the most sophisticated machine-learning techniques in order to train a computer with artificial intelligence (AI) to master the ancient board game of Go. Weirdly enough, this millenia-old board game is the perfect demonstration of human complexity, machine limitations, and how powerful AI has become. For decades, researchers considered playing Go to be the holy grail of game-playing AI. No computer had ever come close to beating a professional in an even, full-board game. Intriguingly, AlphaGo plays Go with something akin to human-like intuition. Computers have always been good at doing the kinds of tasks that we can logically define, like multiplying large numbers, storing information, and playing recorded movies.

MIT Technology Review spends most of the year identifying and writing about the most important emerging technologies. One day each year we highlight the worst of the lot. Some ideas just do not belong together. This year you can add "DIY--gene therapy" to the list. Josiah Zayner did it on video in August, injecting himself with a syringe full of the DNA-slashing chemicals known as CRISPR, in a blend he concocted himself to strengthen his muscles. Zayner, who operates an online shop for biohackers called (what else?)

The right combination of artificial intelligence and augmented reality could put and end to lies. That is the ambitious working hypothesis of several different teams of scientists, businesses, and institutions that have committed themselves to fighting deception by means of technology, so as to assure the safety of citizens around the world. The ethical debate resulting from these innovations is as complex as the mechanisms that must be applied to achieve this objective. Be that as it may, the first steps in this direction are being taken by means of devices that are as practical and affordable as smart phones and smart glasses. Traditional lie detecting machines--that is to say, polygraphs--measure breathing, blood pressure, and other physiological indicators to detect levels of stress in those who are being interrogated.

During the 1830s, England entered into the Industrial Revolution's era of big data. Its government began producing vast troves of statistical information about everything from the cost of "pease and beans" to the export of hats. Processing all that information was time-consuming. Its civil service had to employ an army of clerks to read handwritten census records from every single parish, tabulate the data on large sheets of paper, make tick marks, count the ticks, fold the pages over, and convert everything into new tables, over and over again for every statistic the government wanted to know. They were known as "computers." Many suffered nervous breakdowns from all the ticking and counting.

The biggest knock against sending robots to explore the solar system for signs of life has always been their inability to make intuitive, even creative decisions as effectively as humans can. Recent advances in artificial intelligence (AI) promise to narrow that gap soon--which is a good thing, because … Read more: How NASA's Search for ET Relies on Advanced AI

Speed to insight is going to emerge as the key competitive differentiator for businesses, as they start stepping into the era of compute-and-speed-hungry artificial intelligence(AI), and deep learning workloads. IBM, recently announced a new line of accelerated IBM Power Systems Servers, keeping this new requirement of businesses in mind. The systems are built on its new POWER9 processor, which reduces the training times of deep learning frameworks significantly from days to hours and allows building more accurate AI applications in considerably less time. "The era of AI demands a tremendous amount of processing power at unprecedented speed," said Monica Aggarwal, Vice President, IBM India Systems Development Lab. "To meet the demands of the cognitive workload, businesses need to change everything right from the start- the algorithms, the software, and the hardware as well. POWER9 systems bring an integrated AI platform designed to accelerate machine learning and deep learning with both software and hardware that are optimized to work together."

Background: Deep learning models are typically trained using stochastic gradient descent or one of its variants. These methods update the weights using their gradient, estimated from a small fraction of the training data. It has been observed that when using large batch sizes there is a persistent degradation in generalization performance - known as the "generalization gap" phenomena. Identifying the origin of this gap and closing it had remained an open problem. Contributions: We examine the initial high learning rate training phase. We find that the weight distance from its initialization grows logarithmically with the number of weight updates. We therefore propose a "random walk on random landscape" statistical model which is known to exhibit similar "ultra-slow" diffusion behavior. Following this hypothesis we conducted experiments to show empirically that the "generalization gap" stems from the relatively small number of updates rather than the batch size, and can be completely eliminated by adapting the training regime used. We further investigate different techniques to train models in the large-batch regime and present a novel algorithm named "Ghost Batch Normalization" which enables significant decrease in the generalization gap without increasing the number of updates. To validate our findings we conduct several additional experiments on MNIST, CIFAR-10, CIFAR-100 and ImageNet. Finally, we reassess common practices and beliefs concerning training of deep models and suggest they may not be optimal to achieve good generalization.

Researchers have also applied neural network-based approaches to cybersecurity tasks. Ryan et al. \shortciteryan1998intrusion train a standard neural network with one hidden layer to predict the probabilities that each of a set of ten users created a distribution of Unix commands for a given day. They detect a network intrusion when the probability is less than 0.5 for all ten users of the network. Differing from our work, their input features are not structured, and they do not train the network in an online fashion. Early work on modeling normal user activity on a network using RNNs was performed by Debar et al. \shortcitedebar1992neural. They train an RNN to convergence on a representative sequence of Unix command line arguments (from login to logout) and predict network intrusion when the trained network for that user does poorly at predicting the login to logout sequence.