A new algorithm can visually discern malignant skin lesions from less dangerous ones. A team from the Stanford Artificial Intelligence Laboratory set up a machine-learning AI to teach itself to diagnose cancer by looking at skin lesions just as a doctor would during an exam. The team tested the algorithm against 21 board-certified dermatologists, and it performed just as well as its human counterparts. "We realized it was feasible, not just to do something well, but as well as a human dermatologist," Sebastian Thrun, an adjunct professor in the Stanford Artificial Intelligence Laboratory, said in a statement. "That's when our thinking changed. That's when we said, 'Look, this is not just a class project for students, this is an opportunity to do something great for humanity.'"

Artificially intelligent computer systems are now able to recognise skin cancer just as well as human scientists, according to a new report. Researchers from Stanford University used the internet to gather 130,000 pictures of skin lesions, representing over 2,000 different diseases. They then used these pictures to train an artificial intelligence algorithm - originally developed by Google to differentiate between cats and dogs - to tell the difference between cancerous and non-cancerous lesions. After training the algorithm, they tested it on 370 high-quality, biopsy-confirmed images, and compared its responses with those of 21 professional dermatologists. In its diagnoses of the skin lesions, which represented the most common and deadliest skin cancers, the algorithm matched the performance of the dermatologists.

A huge two mile-long crack has been discovered in the desert in Arizona. Drone footage uploaded to YouTube by the Arizona Geological Survey shows the massive fissure splitting the desert's surface in the Tator Hills area of southern Pinal County. The film shows people dwarfed by the crack as they stand next to the edge, while the drone flies over the wide-open fissure which extends farther into the earth then the eye can see. It is the first time that the AZGS has used drone footage to examine fissures in this way. 'AZGS is experimenting with drone technology as a tool for mapping fissures and other surface features, e.g.

Why do so few women end up in physics, mathematics and other fields traditionally associated with "brilliance"? Part of the answer may lie in what happens to girls by the time they're out of kindergarten. A new study finds that 6-year-old girls are less likely than boys to think members of their own gender can be brilliant -- and they're more likely than boys to shy away from activities requiring that exceptional intelligence. That's a serious change from their attitudes at age 5, when they're just as likely as boys to think their own gender can be brilliant, and just as willing to take on those activities for brilliant children. The results, described in the journal Science, shows how early these gender stereotypes begin to affect the self-perception and behavior of girls -- which may limit their aspirations and careers into adulthood.

The study entailed consumer-led research conducted by the University of Goldsmiths, and the report provides guidelines for brands developing their own customer-facing bots in preparation for an AI first world. Its recommendations are informed by a thought-provoking set of findings: not least that people prefer to confide in a bot when it comes to finance and medical issues, but still like to know when a bot is a bot. In other words, bots should never be too humanoid. As one participant in the research commented: "I would prefer the interaction with the AI over the human for things like budgeting and managing my money. I think it would be quite intrusive if an actual person was trying to advise you on your spending, but if you could set an AI goals to manage your money better, and you could get advice and tips, that would be a really nice feature."

Driverless cars could be dangerous because of the time it takes for drivers to retake control if something goes wrong, scientists have warned. A new study found it could take 25 seconds for drivers to take the wheel in a non-emergency situation. It is feared this lag could cause driverless cars to swerve, make sudden lane changes or brake harshly if people do react fast enough. Research by the University of Southampton found drivers took up to 25.7 seconds when distracted to respond to a command to take over from the car's computer. Professor Neville Stanton, lead author, shown in Southampton's driving simulator But initial models are not expected to drive themselves for a full journey.

If there's one truth that the tech revolution has proven itself repeatedly, it's this: just because we can build something doesn't mean we should. Take, for instance, Google Glass, iTunes Ping, and Clippy (to say nothing of the smart fork.) Likewise, imagine an app that can take a snapshot of a student's homework assignment, chew on the questions using cloud-based artificial intelligence, and spit out the answers. In theory, this sounds great for students, yet terrible for learning -- that is, until you put Socratic to the test. "Every student today goes to the Internet, goes to Google, to ask all of their questions -- this is something that's happening anyway," says Shreyans Bhansali, the co-founder and head of engineering for the free homework-helping app that's currently topping Apple's App Store for education software. "We read the question, we figure out what they need to learn to answer it, and then we teach them that stuff."

Google parent Alphabet reports fourth-quarter earnings after the close on Thursday. SAN FRANCISCO -- Google parent Alphabet is scheduled to announce third-quarter earnings after the market closes Thursday. REVENUE FORECAST: Analysts expect revenue of $25.18 billion, excluding payments to advertising partners, up 18% from the year-ago period. EARNINGS FORECAST: Earnings, excluding some items, are expected to increase to $9.61 a share. MADE BY GOOGLE: Google is not new to hardware but in the fourth quarter it launched a major line-up of devices including the Pixel phone and Google Home, the home speaker.

DeepTraffic is a gamified simulation of typical highway traffic. Your task is to build a neural agent – more specifically design and train a neural network that performs well on high traffic roads. Your neural network gets to control one of the cars (displayed in red) and has to learn how to navigate efficiently to go as fast as possible. The car already comes with a safety system, so you don't have to worry about the basic task of driving – the net only has to tell the car if it should accelerate/slow down or change lanes, and it will do so if that is possible without crashing into other cars. The page consists of three different areas: on the left you can find a real time simulation of the road, with different display options, using the current state of the net.

Researchers at Stanford University have created an AI algorithm that can identify skin cancer as well as a professional doctor. The program was trained on nearly 130,000 images of moles, rashes, and lesions using a technique known as deep learning. It was then tested head-to-head against 21 human dermatologists, where its creators say it performed with an accuracy on par with humans ("at least" 91 percent as good). In the future, they suggest it could be used to create a mobile app for spotting skin cancer at home. Each year in the United States, some 5.4 million new cases of skin cancer are diagnosed.