Computers could soon be helping to diagnose cancer in patients with the help of artificial intelligence that has been trained to spots the early signs of the disease. An AI machine capable of accurately diagnosing breast cancer 92 per cent of the time has been developed by researchers. While it is still not quite as good as human specialists – who are correct 96 per cent of the time – it suggests that AI could soon be used to speed up and improve cancer screening. Scientists have used machine learning to create an artificial intelligence system capable of diagnosing breast cancer from lymph node biopsies with 92 per cent accuracy (cancer cells in a lymph node pictured). When combined with a human pathologist this accuracy increased to 99.5 per cent The system was developed by computer scientists at Harvard Medical School gave a machine learning algorithm slides of lymph nodes from breast cancer patients.

In the future, will artificial intelligence be so sophisticated that it will be able to tell when someone is trying to deceive it? A Carnegie Mellon University professor and his team is working on technology that could move this idea from the realm of science fiction to reality. Their work -- rooted in game theory and machine learning -- is part of a larger push for more advanced AI. As AI becomes more commonplace in the technology we use every day, detractors and supporters are becoming more vocal about its potential risks and benefits. For some, smarter AI sets up a dangerous precedent for a future too reliant on machines to make decisions about everything from medical diagnoses to the operation of self-driving cars.

The traditional definition of artificial intelligence is the ability of machines to execute tasks and solve problems in ways normally attributed to humans. Some tasks that we consider simple--recognizing an object in a photo, driving a car--are incredibly complex for AI. Machines can surpass us when it comes to things like playing chess, but those machines are limited by the manual nature of their programming; a 30 gadget can beat us at a board game, but it can't do--or learn to do--anything else. This is where machine learning comes in. Show millions of cat photos to a machine, and it will hone its algorithms to improve at recognizing pictures of cats.

If you write marketing or advertising text for a living, you may want to get a second job skill. That's because software that writes text is here, and it is tackling a growing list of assignments. Several companies offer software that regularly churns out thousands of stories and reports based on structured data, like financial results. Ads that literally write themselves emerged last week, as IBM announced a new service based on its Watson supercomputer. A program called Quakebot has generated earthquake stories for the LA Times.

Intel's strategy for tackling the AI CPU market, where it is facing competition from leading GPU makers and potentially also big customers that make their own specialized processors for this purpose, such as Google, rests to a great extent on designing systems that scale out rather than up. The latter, according to the chipmaker, is the conventional but inefficient approach to architecting these systems. Software code in today's machine learning systems (machine learning is one of the most active subfields in the development of artificial intelligence) is tough to scale and usually lives in a single box, Charles Wuischpard, VP of the Intel Data Center Group and general manager of the giant's HPC Platform Group, said. Companies generally buy high-power scale-up systems filled with GPUs. "In a way, there's an efficiency loss here," he said on a call with reporters last week.

The artificial intelligence (AI) system is "based on deep learning, a machine-learning algorithm used for a range of applications including speech recognition and image recognition," explains Andrew Beck, an associate professor in pathology at Harvard Medical School, who heads the team developing the new system at Beth Israel Deaconess Medical Center (BIDMC), in Boston, MA. Prof. Beck and colleagues demonstrated the new AI system in a competition held at the annual meeting of the International Symposium of Biomedical Imaging (ISBI 2016) in Prague in April. He and his colleagues are developing AI methods that train computers to interpret pathology images to improve the accuracy of diagnoses. The approach they are using teaches computers to interpret the complex patterns seen in such images by "building multi-layer artificial neural networks," says Prof. Beck. The process is thought to be similar to the way learning takes place in the layers of neurons in the neocortex of the brain, the region where thinking occurs.

When introducing its monster 72-core Xeon Phi chip, Intel couldn't help but take a swipe at graphics processors for being sluggish for some tasks. Ironically, Xeon Phi is a byproduct of Larrabee, which was supposed to be Intel's first major GPU but was abandoned in 2009 after multiple delays. The swipe was a shot at Nvidia, whose GPUs are flourishing in the gaming and machine learning areas. But Nvidia's success has also raised questions about whether Intel should've been patient and pursued Larrabee. Nevertheless, Xeon Phi has been successfully used in supercomputing, and now Intel wants to challenge Nvidia's GPU by bringing the chip to machine learning.

Nasa has announced that it has found evidence of flowing water on Mars. Scientists have long speculated that Recurring Slope Lineae -- or dark patches -- on Mars were made up of briny water but the new findings prove that those patches are caused by liquid water, which it has established by finding hydrated salts. Several hundred camped outside the London store in Covent Garden. The 6s will have new features like a vastly improved camera and a pressure-sensitive "3D Touch" display

With every passing day, it feels like the robot uprising is getting a little closer. Robots are being beaten down by their human overlords, even as we teach them to get stronger. Now, they're starting to break free. A robot in Russia escaped from a research lab in the town of Perm yesterday, June 15, reports the BBC. An engineer at robotics company Promobot had forgotten to close a gate, and the runaway bot caused a traffic jam as it bolted out into the world.

A research team from Beth Israel Deaconess Medical Center (BIDMC) and Harvard Medical School (HMS) recently developed artificial intelligence (AI) methods aimed at training computers to interpret pathology images, with the long-term goal of building AI-powered systems to make pathologic diagnoses more accurate. "Our AI method is based on deep learning, a machine-learning algorithm used for a range of applications including speech recognition and image recognition," explained pathologist Andrew Beck, MD, PhD, Director of Bioinformatics at the Cancer Research Institute at Beth Israel Deaconess Medical Center (BIDMC) and an Associate Professor at Harvard Medical School. "This approach teaches machines to interpret the complex patterns and structure observed in real-life data by building multi-layer artificial neural networks, in a process which is thought to show similarities with the learning process that occurs in layers of neurons in the brain's neocortex, the region where thinking occurs." The Beck lab's approach was recently put to the test in a competition held at the annual meeting of the International Symposium of Biomedical Imaging (ISBI), which involved examining images of lymph nodes to decide whether or not they contained breast cancer. The research team of Beck and his lab's post-doctoral fellows Dayong Wang, PhD and Humayun Irshad, PhD, and student Rishab Gargya, together with Aditya Khosla of the MIT Computer Science and Artificial Intelligence Laboratory, placed first in two separate categories, competing against private companies and academic research institutions from around the world. The research team today posted a technical report describing their approach to the arXiv.org