Crucial time and tremendous amounts of resources are lost every day in the world's healthcare systems. Misdiagnoses cost unnecessary additional tests, result in delayed treatment plans and diminished survival or remission rates from what would have transpired had it been caught and identified correctly earlier. Trials, treatments and research completed in silos so there's no leveraging the insights across the country or the world. Some healthcare and technology innovators are collaborating and trying to change our current reality by experimenting with artificial intelligence (AI) and machine learning. Computers and the algorithms they run can scrub colossal amounts of data--much faster and more accurately than human scientists or medical professionals--to unearth patterns and predictions to enhance disease diagnosis, inform treatment plans and enhance public health and safety.

SAN FRANCISCO (Reuters) - Microsoft Corp said on Monday it was looking into whether Russians bought U.S. election ads on its Bing search engine or on other Microsoft-owned products and platforms, after rival Google said it had discovered such ads on its products.

"One of these things is not like the others," the television show Sesame Street taught generations of children. Let's move to the next level: "One or more of these things may or may not be like the others, and those variances may or may not represent systems vulnerabilities, failed patches, configuration errors, compliance nightmares, or imminent hardware crashes." Looking through gigabytes of log files and transactions records to spot patterns or anomalies is hard for humans: it's slow, tedious, error-prone, and doesn't scale. Fortunately, it's easy for artificial intelligence (AI) software, such as the machine learning algorithms built into Oracle Management Cloud. What's more, the machine learning algorithms can be used to direct manual or automated remediation efforts to improve security, compliance, and performance.

Even as machines known as "deep neural networks" have learned to converse, drive cars, beat video games and Go champions, dream, paint pictures and help make scientific discoveries, they have also confounded their human creators, who never expected so-called "deep-learning" algorithms to work so well. No underlying principle has guided the design of these learning systems, other than vague inspiration drawn from the architecture of the brain (and no one really understands how that operates either). Original story reprinted with permission from Quanta Magazine, an editorially independent publication of the Simons Foundation whose mission is to enhance public understanding of science by covering research developments and trends in mathematics and the physical and life sciences. Like a brain, a deep neural network has layers of neurons--artificial ones that are figments of computer memory. When a neuron fires, it sends signals to connected neurons in the layer above.

A simple example of Bayesian inference applied to a medical diagnosis problem. Here the problem is diagnosing a rare disease using information from the patient's symptoms and, potentially, the patient's genetic marker measurements, which indicate predisposition (gen pred) to this disease. In this example, all variables are assumed to be binary. The relationships between variables are indicated by directed arrows and the probability of each variable given other variables they directly depend on is also shown. Yellow nodes denote measurable variables, whereas green nodes denote hidden variables.

Four new features that make Google Home a better connected speaker than it was--but it still has growing pains, reports USA TODAY's Jefferson Graham on Talking Tech. SAN FRANCISCO -- In another era, young tech geeks saved their money for a state-of-the-art stereo system: a great turntable, powerful amplifier and an absolute killer set of stereo hi-fi speakers. That went away with the advent of digital and listening to music on computers, then phones and earbuds or tiny, portable speakers. Cut to 2017, and it's the odd return to the loud-speaker wars. Google's Rishi Chandra speaks about the Google Home Max speaker.

A self-driving car powered by one of the more popular artificial intelligence techniques may need to crash into a tree 50,000 times in virtual simulations before learning that it's a bad idea. But baby wild goats scrambling around on incredibly steep mountainsides do not have the luxury of living and dying millions of times before learning how to climb with sure footing without falling to their deaths. And a psychologist's 3-year-old daughter did not need to practice millions of times before she figured out, upon a whim, how to climb through an opening in the back of a chair. Today's most powerful AI techniques learn almost everything about the world from scratch with the help of powerful computational resources. By comparison, humans and animals seem to intuitively understand certain concepts--objects and places and sets of related things--that allow them to quickly learn about how the world works. That begs an important "nature vs. nurture" question: Will AI learning require built-in versions of that innate cognitive machinery possessed by humans and animals to achieve a similar level of general intelligence?

The following feature is excerpted from TIME Artificial Intelligence: The Future of Humankind, available at retailers and at the Time Shop and Amazon. Medicine is both art and science. While any doctor will quickly credit her rigorous medical training in the nuts and bolts of how the human body works, she will just as adamantly school you on how virtually all of the decisions she makes--about how to diagnose disease and how best to treat it--are equally the product of some less tangible measures: her experience from previous patients; her cumulative years of watching and learning from patients, colleagues and the human body. Which is why the idea of introducing machines into medicine seems misguided at the very least, and also foolhardy. How can a robot, no matter how well-trained, take the place of a doctor?

Stefano Domenicali has abandoned his uniform. This is a man who, based on his public appearances, lives in suits worth more than your car. He is, after all, the CEO of Lamborghini. Any element of his appearance that doesn't live up to the combination of wealth, style, and Italian-ness that his company represents would be an offense. Today he's wearing the suit, but has left the collar of his perfectly pressed shirt unbuttoned. He chose the casual look, he says, out of deference to the aggressively lenient sartorial sense of Silicon Valley.

Uber drivers could one day be spared from engaging in small talk with drunks if a National Transport Commission suggestion to allow people under the influence of alcohol to use fully automated vehicles is adopted by state road authorities. The NTC, an independent statutory body tasked with reforming Australia's driving laws to prepare for the arrival of driverless cars, has recommended an "exemption" from drink and drug-driving laws for people who ride in fully automated vehicles. In a new discussion paper it argues there is a "clear-cut" justification for an exemption from drink-driving laws because there is "no possibility that a human could drive a dedicated automated vehicle". "The situation is analogous to a person instructing a taxi driver where to go," the NTC report states. State traffic laws prohibit driving under the influence of varying levels of alcohol but the NTC says they could be a "barrier" to the benefits of driverless vehicles, which it argues could improve road safety by reducing the incidence of drink-driving.