Over the past several years, many new technological innovations in the healthcare industry have come and gone, but only a handful of trends matured and achieved widespread adoption among healthcare organizations. Bots are another new development that is piquing the interest of healthcare organizations and attempting to become more than a fleeting trend. There's no doubt that mobile health technology improves patient care, engagement, and physician productivity. But, the security risks are vast. Learn how to protect your mHealth devices and data in this exclusive e-guide.

One day in January 2013, I found myself wasting time on the internet. This wasn't a good idea: I was as busy as anyone 2.5 years into their PhD. I had to finish a presentation on some yeast genetics research, I was months behind on a paper with an NYU collaborator and even farther behind on some leftover undergraduate research. I was also busy in my personal life--I had returned from a trip to Israel and had just taken up Brazilian Jiu-Jitsu and jogging. But this one day, I was wasting time by answering a stranger's question about the beta distribution. The question was on Cross Validated, the statistics sister site of developer Q&A site Stack Overflow.

SITTING IN AN office in San Francisco, Igor Barani calls up some medical scans on his screen. He is the chief executive of Enlitic, one of a host of startups applying deep learning to medicine, starting with the analysis of images such as X-rays and CT scans. It is an obvious use of the technology. Deep learning is renowned for its superhuman prowess at certain forms of image recognition; there are large sets of labelled training data to crunch; and there is tremendous potential to make health care more accurate and efficient. Dr Barani (who used to be an oncologist) points to some CT scans of a patient's lungs, taken from three different angles.

"I think there's recognition it makes sense to have some people thinking about [AI safety] now," says Oxford University philosophy professor Nick Bostrom. Over the past year, Oxford University philosophy professor Nick Bostrom has gained visibility for warning about the potential risks posed by more advanced forms of artificial intelligence. He now says that his warnings are earning the attention of companies pushing the boundaries of artificial intelligence research. Many people working on AI remain skeptical of or even hostile to Bostrom's ideas. But some prominent technologists and scientists--including Elon Musk, Stephen Hawking, and Bill Gates--have echoed some of his concerns.

Promobot RI77 escaped its high-tech lab and evaded Russian scientists for the i second /i time this month. LOS ANGELES, CA (Catholic Online) - Promobot, short for "promotional robot," was equipped with artificial intelligence, allowing it to learn through ... continue reading In another sign the U.S. is slipping internationally, China has unveiled the world's fastest supercomputer, that is five times more powerful that the fastest U.S. supercomputer. It is also built with all Chinese microprocessors. LOS ANGELES, CA (California Network) - ... continue reading Security robots have gone to work in a Silicon Valley mall and designers are shocked at the reactions they're getting from the public. The mixed response means the robots have to be capable of protecting themselves if needed.

America's tax enforcement agency, the Inland Revenue Service, is pretty sure Paulo Marques is an international tax evader. For the last five years, without fail, he's been summoned by the authorities to spend hours explaining the ins and outs of his revenue streams. But the Inland Revenue's computer doesn't know that, Marques tells the audience WIRED Money 2016. In fact, when it comes to stopping fraud, machines get it wrong far too often. "If you want to stop fraud, you really need to understand human behaviour," says Marques, who founded Feedzai, a company that uses big data to combat fraud.

Like many autistic children, Julian Brown has trouble reading emotions in people's faces, one of the biggest challenges for people with the neurological disorder. Now the 10-year-old San Jose boy is getting help from'autism glass' -- an experimental device that records and studies faces in real-time and alerts him to the emotions they're expressing. The facial recognition software was developed at Stanford University and runs on Google Glass, a computerised headset with a front-facing camera and a tiny display just above the right eye. Julian Brown has trouble reading emotions in people's faces, one of the biggest challenges for people with the neurological disorder. Now the 10-year-old San Jose boy is getting help from'autism glass' Autism glass records and studies faces in real-time.

Here's the worst case scenario: By 2045, 70 million additional car-bound people choke American highways. Bridges, tunnels, and freeways continue to crumble, risking lives and more traffic delays. Luckily, solutions are on the way, many already accessible at the tap of an iPhone. Uber, Lyft, Zipcar, bike share, drone grocery delivery: Technology has repainted the picture of American mobility, and especially in cities. Early adopters are those with the social capital, money, and time to play with radical new mobility options.

Drones may be fantastic as a consumer hobby, but these small unmanned aerial vehicles pose a significant security threat when they fall into the wrong hands. One weaponized drone is bad enough, but drone swarms are a security nightmare for military officials. Drones, whether alone or in groups, are difficult to spot when approaching, tough to follow on radar, and cannot be easily shot down using conventional weapons. To combat this potential threat, Lockheed Martin is developing a laser-based weapon that'll blow such drones out of the sky. Traditional military protocol relies on the use of missiles and guns fired from the ground or from ships or vehicle platforms.

Computational linguists and computer scientists, among them University of Texas professor Jason Baldridge, have been working for over fifty years toward algorithmic understanding of human language. They are, however, doing a pretty good job with important tasks such as entity recognition, relation extraction, topic modeling, and summarization. These tasks are accomplished via natural language processing (NLP) technologies, implementing linguistic, statistical, and machine learning methods. Voice response and personal assistants -- Siri, Google Now, Microsoft Cortana, Amazon Alexa -- rely on NLP to interpret requests and formulate appropriate responses. Search and recommendation engines apply NLP, as do applications ranging from pharmaceutical drug discovery to national security counter-terrorism systems.