New research indicates that Artificial Intelligence, or AI, as it is defined and practised today, has several limits. New buzzwords only serve to mystify the populace, and it is increasingly clear to me that many technologists and information technology (IT) managers are just groping about in the dark. They throw out terms such as "neural networks", "deep learning", "big data", "black box systems", and so on, hoping to mask the fact that they know very little of how this technology may evolve over the next several years. As an observer, I can't help but think there is an important question in front of us: are the ramblings of these pundits in fact a case of the one-eyed man becoming king in the land of the blind--or, instead, more akin to the parable of the five blind men, who all encountered an elephant and, after inspecting various parts of the elephant by touch, came away with different definitions of what an elephant is like? The vital premise in today's AI is that the computer program itself learns as it goes along, creating a database of information, and then, uses that database to automatically generate additional computer programming codes as it'learns' more--without the need for human programmers.

Given the subtle differences between AI and human intelligence, it's reasonable to expect that an autonomous AI system will generate its own criteria for judging what passes as new [scientific] knowledge. New knowledge which humans no longer experience as something they themselves have produced would shake the foundations of human culture." This will start with dense, urban areas, but over time every single square meter of every part of the globe will be recorded. Advances in computer vision & AI mean this data will be usable at scale, which will revolutionise advertising, law enforcement, and bring us back to a pre-privacy world." "Text Classification Using Neural Networks" submitted by gk_(@gk_).

Light-based (photonic) technologies offer many benefits when it comes to building a computer: they are efficient, have high bandwidths, and deliver fast processing speeds. But progress in photonics-based computing has almost always been outpaced by advances in semiconductor electronics, which make up the logical elements in today's computers. Where photonics does seem to be making headway is with alternative computation schemes, which solve problems differently than conventional transistor-based (binary) computers. One example of an alternative scheme is reservoir computing, which uses interconnected devices to mimic the neuronal architecture of the brain. Laurent Larger, of the French National Center for Scientific Research (CNRS) and the University of Burgundy Franche-Compté, and co-workers [1] have taken a photonics-based reservoir computer design and refitted part of it with optoelectronic components to achieve a threefold increase in processing speed.

It's easy to forget that even with the fanciest of machine learning models, we still need humans in the trenches cleaning input data. Descartes Labs, a startup that combines satellite imagery with data about our planet to produce insights and forecasts, knows this all too well. The company ended up building its own cloud-based parallel computing infrastructure to clean and process its massive corpus of satellite imagery. Companies like Descartes Labs cannot just throw raw satellite imagery into machine learning models to extract insights. Images captured contain clouds, cloud shadows and other atmospheric aberrations that make it impossible to compare images taken at different times.

Can artificial intelligence make you a better skier? The ski manufacturer teamed up with sports tech company PIQ to put an AI-powered computer -- complete with an LED display -- right on a pair of skis. Called the Rossingol Hero Master, the idea is similar to the PIQ Robot accessory, which attaches to ski boots and analyzes your turns, speed and other data while you ride. This takes that idea to the next level with the technology built right into the skis. Rather than just sending the data to an app on your phone, the Hero Master comes with its own LED display so skiers can see real-time stats and other information.The prototype uses onboard sensors and PIQ's algorithms to analyze speed, turning angles, and other data.

Back in 1994, Tony was learning to play chess under the tutelage of long-time Costa Rican National Champion Bernal Gonzalez. While the chess training didn't stick, he very clearly remembers a conversation where the teacher explained that the world's best chess playing computer wasn't quite strong enough to be among the top 100 players in the world. Technology can advance exponentially, and just 3 years later in 1997, World Champion Garry Kasparov was defeated by IBM's chess playing supercomputer Deep Blue. But chess is a game of logic where all potential moves are sharply defined and a powerful enough computer can simulate many moves ahead. Things got much more interesting in 2011, when IBM's Jeopardy playing computer Watson defeated Ken Jennings who held the record of winning 74 Jeopardy matches in a row.

Physicians today face increased pressure to produce more accurate, complete and compliant clinical documentation while simultaneously maintaining their focus on patient care and satisfaction. However, without better clinical documentation processes and technology in place, physicians and hospitals risk receiving inaccurate quality scores and lower reimbursement rates. CMS continues to advance the level of reimbursement it ties to clinical performance. By 2018, CMS expects to base payment criteria for Medicare value-based purchasing programs wholly on clinical performance according to efficiency of care (25 percent), patient outcomes (25 percent), patient experience (25 percent) and safety (25 percent). To achieve full reimbursement from payers in value-based care, "clinical coding should capture the complexity of the patient's condition -- the number and severity of comorbidities, the relationships between conditions and the results of treatments and interventions," Anthony Oliva, MD, vice president and CMO at Nuance, said during a webinar sponsored by Becker's Hospital Review Jan. 24.

I mean, I know they are, but around me the 3 major supermarkets within walking distance got rid of them....Anyway, I know they still exist, but I do think our robot future is not quite as inevitable as people think. Worrying about the impact of future automation on jobs seems to be a cool tech away of ignoring the current fucked and bullshit jobs situation. And, yes, automation has been going on for decades, which is actually my point. There's nothing new about it, and I don't know why people think there will be this sudden automation discontinuity. The robots have been here for awhile, and they aren't really going away, but that doesn't mean the sci-fi dystopian workless future is just around the corner.

When The Six Million Dollar Man first aired in the Seventies, with its badly injured astronaut being rebuilt with machine parts, the TV show seemed a far-fetched fantasy. But fast-forward 40 years and the idea of a part-man, part-robot doesn't seem so extraordinary after all. Today, the idea of a part-man, part-robot doesn't seem so extraordinary after all. Just last week, it was reported that former policewoman Nicki Donnelly, 33, paralysed from the waist down after a driver smashed into her police car, is now able to walk her daughter to school, thanks to a robotic exoskeleton that does the walking for her. And today, the Mail reveals that robotic arms controlled by thought are now being developed in Britain.

Self-driving cars and robots that can zoom on their own around warehouses are just some of what's possible because of artificial intelligence. But expect unforeseen consequences if researchers ignore the inherent ethical dilemmas in the emerging technology. That's one of the takeaways from a panel about AI ethics and education in San Francisco that was hosted by the Future of Life Institute, a research group focused on preventing societal problems created by the technology. Although humans typically program AI-powered robots to accomplish a particular goal, these robots will typically make decisions on their own to reach the goal, explained Benjamin Kuipers, a computer science professor and AI researcher at the University of Michigan. It's these smaller decisions that robots make on their own that can cause trouble because human programmers may fail to take all of a robot's possible choices into account, Kuipers said.