The chairman of the Commons Science and Technology Committee has thrown his weight behind recommendations for the widespread introduction of artificial intelligence (AI) to the NHS. Norman Lamb said the rewards could be "immense" in terms of cost savings and diagnosing patients more quickly. But he warned people's privacy must be protected and that the health service should get a "fair deal" from technology companies implementing the systems. The report, by the Reform think-tank, said AI could be used to target treatment by predicting which individuals or groups might be at risk of illness, to send patients to the most appropriate services or to enable them to "self-care". The technology can also be used to improve diagnoses, Reform said, including for breast cancer – 30 times faster and more accurately than humans, the group claimed.

IN A POST entitled "Machine Learning: Bane or Blessing for Mankind?", I noted that the renowned theoretical physicist Stephen Hawking along with his colleagues Stuart Russell, Max Tegmark, and Frank Wilczek recommend moving cautiously in the development of artificial intelligence (AI), especially in the area of autonomous weapon systems. Hawking and his colleagues understand, however, that the AI genie has already been released from the bottle and there is no way to get it back in. After noting Hawking's concerns, Ron Neale comments, "Such a warning about the application of AI and its derivative intelligent machines (IMs), especially in the area of military application, might be appropriate. But what if IMs are really just a new branch on the tree of evolution that has led us from the original Protists to where we are today?"

At least 15 trusts in England are said to be interested in new machine learning software designed to support the diagnosis of heart disease, which its developer is planning to offer for free to the NHS. The machine learning algorithm, developed by Oxford-based start-up Ultromics, analyses echocardiogram images for signs of disease. The system is said to be capable of spotting warning signs that might be missed by a clinician, so reducing the risk of a patient suffering a heart attack or other complications. Ultromics hit the headlines over the festive period after it was reported its machine learning software could be rolled out to NHS trusts for free starting this summer. CEO Ross Upton suggested the technology could save the NHS £300 million a year by reducing the number of people who are incorrectly sent for heart surgery, or are otherwise given the all-clear and later suffer a heart attack that requires treatment.

Microsoft, in collaboration with others, is using algorithms to convert satellite images into information about categories of land cover, such as forests.Credit: Microsoft Earlier this year, I became Microsoft's first chief environment scientist. I've been tasked with deploying the company's deep investments in artificial intelligence (AI) research and technology to help people around the world monitor, model and ultimately manage Earth's natural systems. Most people I meet are surprised that one of the world's leading technology companies has a role such as mine. Yet I believe that in the next few years, every major tech firm will be working on applying AI to sustainability. It is the ethical thing to do. It is good for business.

In written testimony prepared for a hearing held Tuesday by the House Armed Services Subcommittee on Emerging Threats and Capabilities, William Carter, the deputy director for CSIS' technology policy program, said: China sees offensive cyber capabilities, anti-satellite weapons, electronic warfare tools, hypersonic weapons, artificial intelligence, and quantum technologies as key to enabling the PLA [People's Liberation Army] to win wars in future, high-tech conditions and offset the advantages of the U.S. military, and has made significant strides in all of these areas. Specifically, China is already able to "significantly disrupt, degrade, and even destroy the ICT [information and communication technology] infrastructure on which our military depends," namely satellites, Carter told lawmakers, suggesting that by doing so Beijing could essentially take down the American armed forces. "We may have more and better aircraft carriers, tanks, and missiles than the PLA, but without access to data and connectivity many of these systems are ineffective or even inoperable," warned the CSIS expert. He went on to note that China considers satellites to be the U.S. military's "Achilles heel," adding: The PLA has tested a range of anti-satellite weapons, including conventional ground-based kinetic kill vehicles, directed energy weapons, jamming and spoofing capabilities, and "kill-satellites" designed to disable or destroy other satellites on orbit. They have expanded their electronic warfare capabilities, testing their capabilities to jam radar and communications and spoof GPS systems.

Let's make one thing clear: one year isn't going to fix decades of gender discrimination in computer science and all the problems associated with it. Recent diversity reports show that women still make up only 20 percent of engineers at Google and Facebook, and an even lower proportion at Uber. But after the parade of awful news about the treatment of female engineers in 2017--sexual harassment in Silicon Valley and a Google engineer sending out a memo to his coworkers arguing that women are biologically less adept at programming, just to name a couple--there is actually reason to believe that things are looking up for 2018, especially when it comes to AI. At first glance, AI would seem among least likely areas of programming to be friendly to women. Writing in Fast Company recently, Hanna Wallach, an AI researcher and cofounder of the Women in Machine Learning Conference, said that only 13.5 percent of those working in machine learning are female. In the midst of the #MeToo movement, researchers in artificial intelligence also dealt with sexual harassment allegations, as well as complaints that inappropriate jokes were made at a parties around NIPS, a major industry conference.

Current machine learning systems operate, almost exclusively, in a statistical, or model-free mode, which entails severe theoretical limits on their power and performance. Such systems cannot reason about interventions and retrospection and, therefore, cannot serve as the basis for strong AI. To achieve human level intelligence, learning machines need the guidance of a model of reality, similar to the ones used in causal inference tasks. To demonstrate the essential role of such models, I will present a summary of seven tasks which are beyond reach of current machine learning systems and which have been accomplished using the tools of causal modeling.

How can AI impact Cybersecurity? As more companies rely on IT systems in their infrastructure, the threat of a cybersecurity breach and the damage it can cause is fast growing. The number of devices and amount of data businesses are required to analyse in order to detect and prevent cybersecurity attacks is increasing at a much faster rate than trained personnel are becoming available to manually analyse such data. As a result there is a noticeable gap between what the industry needs in order to avoid more advanced cyberattacks and the number of skilled personnel available to join the fight. AI, and more particularly machine learning, makes the possibility of autonomous security systems a reality.

Democratic Gov. Andrew Cuomo called the drones state-of-the-art technology and said they will improve the state police's ability to respond to situations quickly and effectively. Officials say the equipment will also protect troopers by doing jobs too hazardous for humans.

They talk about all the weird, wonderful, and unnecessary gadgets and tech they have seen so far at the convention, like the laundry-folding robots that might not be very good at folding laundry, bizarre tech for your pets, drones and self-driving cars, smart mirrors, and even a smart couch. They discuss the cybersecurity concerns surrounding Intel and how they've handled the situation so far, and the big battle between Amazon's Alexa and Google's new A.I. assistants.