Fifty years ago, the first industrial robot arm (called Unimate) assembled a simple breakfast of toast, coffee, and champagne. While it might have looked like a seamless feat, every movement and placement was coded with careful consideration. Even with today's more intelligent and adaptive robots, this task remains difficult for machines with rigid hands. They tend to work only in structured environments with predefined shapes and locations, and typically can't cope with uncertainties in placement or form. In recent years, though, roboticists have come to grips with this problem by making fingers out of soft, flexible materials like rubber.

The new AI algorithm was able to efficiently automate classifying amyloid plaques and blood vessel abnormalities in postmortem brains of Alzheimer's patients. Researchers at UC Davis and UC San Francisco have found a way to teach a computer to precisely detect one of the hallmarks of Alzheimer's disease in human brain tissue, delivering a proof of concept for a machine-learning approach capable of automating a key component of Alzheimer's research. Amyloid plaques are clumps of protein fragments in the brains of people with Alzheimer's disease that destroy nerve cell connections. Much like the way Facebook recognizes faces based on captured images, the machine learning tool developed by a team of University of California scientists can "see" if a sample of brain tissue has one type of amyloid plaque or another -- and do it very quickly. The findings, published May 15, 2019 in Nature Communications, suggest that machine learning can augment the expertise and analysis of an expert neuropathologist.

San Francisco -- A group of researchers are quietly commercialising an artificial intelligence (AI)-driven lie detector, which they hope will be the future of airport security. Discern Science International is the start-up behind a deception detection tool named the Avatar, which features a virtual border guard that asks travelers questions. The machine, which has been tested by border services and in airports, is designed to make the screening process at border security more efficient, and to weed out people with dangerous or illegal intentions more accurately than human guards are able to do. But its development also raises questions about whether a person's propensity to lie can be accurately measured by an algorithm. The Avatar -- whose "face" appears on a screen -- asks travelers a series of pre-configured questions and decides whether they are lying.

Machine learning-guided virtual reality simulators can help neurosurgeons develop the skills they need before they step in the operating room, according to a new study. Research from the Neurosurgical Simulation and Artificial Intelligence Learning Centre at The Neuro (Montreal Neurological Institute and Hospital) and McGill University shows that machine learning algorithms can accurately assess the capabilities of neurosurgeons during virtual surgery, demonstrating that virtual reality simulators using artificial intelligence can be powerful tools in surgeon training. Fifty participants were recruited from four stages of neurosurgical training; neurosurgeons, fellows and senior residents, junior residents, and medical students. They performed 250 complex tumor resections using NeuroVR, a virtual reality surgical simulator developed by the National Research Council of Canada and distributed by CAE, which recorded all instrument movements in 20 millisecond intervals. Using this raw data, a machine learning algorithm developed performance measures such as instrument position and force applied, as well as outcomes such as amount of tumor removed and blood loss, which could predict the level of expertise of each participant with 90 per-cent accuracy.

Artificial intelligence, specifically machine learning (ML), enables a new world of complex decision-making using novel relationships between data. This paradigm of a system "learning" from data instead of tedious rules-based programming on an outcome, while exciting in its possibilities, opens up a series of new challenges. Distrust, unfairness, bias and ethical ramifications of automated ML decisions are now increasingly common. The recent story about the inadvertent bias in Amazon's recruiting or face recognition software are examples of unforeseen effects of these applications of AI. They occur because, by and large, the relationships absorbed are opaque, thereby dissuading model developers in fixing it.

The ride-hailing service wants to ferry the world around in self-driving cars and on electric scooters, deliver our takeouts and groceries by drone, and ship freight via robot trucks. But first Uber needs to answer a big question: will it ever make any money? This week, Wall Street will have the chance to ask that question. Uber went public in May in one of the most anticipated initial public offerings in years. To say it stalled would be an understatement.

In moments of digital anxiety I find myself thinking of my father's desk. Dad was a travelling furniture salesman in the 1980s, a job that served him well in the years before globalisation hobbled the Canadian manufacturing sector. He was out on the road a lot, but when he worked from home he sat in his office, a small windowless study dominated by a large teak desk. And yet every day Dad spent hours there, making notes, smoking Craven "A"s, drinking coffee and yakking affably to small-town retailers about shipments of sectional sofas and dinette sets. This is what I find so amazing.

Like most retail owners, he'd had problems with shoplifting – largely carried out by a relatively small number of repeat offenders. Then a year or so ago, exasperated, he installed something called Facewatch. It's a facial-recognition system that watches people coming into the store; it has a database of "subjects of interest" (SOIs), and if it recognises one, it sends a discreet alert to the store manager. "If someone triggers the alert," says Paul, "they're approached by a member of management, and asked to leave, and most of the time they duly do." Facial recognition, in one form or another, is in the news most weeks at the moment. Recently, a novelty phone app, FaceApp, which takes your photo and ages it to show what you'll look like in a few decades, caused a public freakout when people realised it was a Russian company and decided it was using their faces for surveillance.

Brain Corp is a San Diego-based AI company that specializes in the development of self-driving technology. Our AI tech represents the next generation of artificial brains for robots - it enables machines to perceive, learn, and navigate complex environments, while avoiding people and obstacles. We partner with commercial equipment manufacturers, and global consumer electronics brands, to transform their products into self-driving robots. Our robots use variety of sensors including depth cameras, RGB cameras and LIDARs. This position is for a Software Engineer/Research Scientist specialized in robotic perception algorithms, sensory fusion and SLAM using machine learning and traditional techniques.

The Greater Buffalo Niagara Regional Transportation Council held a community forum at the University at Buffalo Saturday. It's part of a series of discussions organized by Arizona State University's consortium for science. People had the chance to learn more about the technology and its possible uses. The input from the discussion will be shared with transportation planners from around the world.