"This is a propaganda machine. It's targeting people individually to recruit them to an idea. It's a level of social engineering that I've never seen before. They're capturing people and then keeping them on an emotional leash and never letting them go," said professor Jonathan Albright. Albright, an assistant professor and data scientist at Elon University, started digging into fake news sites after Donald Trump was elected president. Through extensive research and interviews with Albright and other key experts in the field, including Samuel Woolley, Head of Research at Oxford University's Computational Propaganda Project, and Martin Moore, Director of the Centre for the Study of Media, Communication and Power at Kings College, it became clear to Scout that this phenomenon was about much more than just a few fake news stories. It was a piece of a much bigger and darker puzzle -- a Weaponized AI Propaganda Machine being used to manipulate our opinions and behavior to advance specific political agendas. By leveraging automated emotional manipulation alongside swarms of bots, Facebook dark posts, A/B testing, and fake news networks, a company called Cambridge Analytica has activated an invisible machine that preys on the personalities of individual voters to create large shifts in public opinion.

The image above - one of around 400,000 mammograms belonging to Zebra Medical Vision - shows what a breast looks like to AI. It has been colour-coded to make it easier for a self-teaching neural network to identify breast cancer. Using this technique, Zebra Medical claims to have been able to detect cancerous cells with 91 per cent accuracy. This is an improvement on the typical radiologists' rate of 88 per cent, with fewer false positives. "Right now," says Zebra Medical founder Elad Benjamin, "this is better than human performance."

Magic Leap, the augmented reality startup that has raised $1.4 billion in funding but has yet to release a product, has made an acquisition to expand its work in computer vision and deep learning, and to build out its operations into Europe. The company has acquired the 3D division of Dacuda, a computer vision startup based out of Zurich. One of Dacuda's focuses had been developing algorithms for consumer-grade cameras (and not just cameras, but any device with a camera function) to capture 2D and 3D imaging in real-time, "making 3D content as easy as taking a video." Dacuda has confirmed the acquisition with a short announcement on its site. It notes that the whole 3D team has moved to Magic Leap and that Dacuda's founder, Alexander Ilic, is now leading Magic Leap Switzerland.

Artificial Intelligence already had a massive impact in the past years, but where will AI be in the coming years? Here is a list of predictions. Waiting to make their moveAsia's looming labour shortage p There is an obvious solution p print-edition iconFrom the print edition Asia p Feb 11th 2017 p THE … The first FDA approval for a machine learning application to be used in a clinical setting is a big step forward for AI and machine learning in healthcare and industry as a whole. Until recently, artificial intelligence (AI) was similar to nuclear fusion in unfulfilled promise. ManpowerGroup, one of the world's largest jobs companies, released a report detailing how the technological revolution is going to change the … Replacing the real world with a virtual one is a neat trick.

Learn how intelligent automation in the cloud can enable you to transform your business, make your processes more flexible, reduce your security risk and lower your IT security OpEx 40-60%. Forrester's research shows that leveraging the cloud is difficult because of the cost and complexity of security compliance. Regulated industries like financial services (banking, investments, insurance), retail, and healthcare have to maintain compliance with industry standards (FFIEC, PCI-DSS, HIPAA). Forrester's recommendation is that IT automation is the only way to cost effectively leverage the cloud and deliver security compliance on-premise and in the cloud. All major researchers estimate there will be tens of billions devices - computers, smartphones, tablets, and sensors - connected to the Internet by 2020.

John Nhial was barely a teenager when he was grabbed by a Sudanese guerrilla army and forced to become a child soldier. He spent four years fighting, blasting away on guns almost too heavy to hold, until one day the inevitable happened: he was seriously injured, treading on a landmine while he was on morning patrol. "I stepped on it and it exploded," he recalled. "It threw me up and down again – and then I tried to look for my leg and found that there was no foot." His comrades carried him back to base camp, but there was hardly any medical care available. It took 25 days before he received proper treatment, during which time he developed tetanus down one side of his body.

The Tokyo Institute of Technology, also known as Tokyo Tech, has revealed that the TSUBAME 3.0 supercomputer scheduled to be installed this summer will provide 47 half precision (16-bit) petaflops of performance, making it one of the most powerful machines on the planet for artificial intelligence computation. For Tokyo Tech, the use of NVIDIA's latest P100 GPUs is a logical step in TSUBAME's evolution. The original 2006 system used ClearSpeed boards for acceleration, but was upgraded in 2008 with the Tesla S1040 cards. In 2010, TSUBAME 2.0 debuted with the Tesla M2050 modules, while the 2.5 upgrade included both the older S1050 and S1070 parts plus the newer Tesla K20X modules. Bringing the P100 GPUs into the TSUBAME lineage will not only help maintain backward compatibility for the CUDA applications developed on the Tokyo Tech machines for the last nine years, but will also provide an excellent platform for AI/machine learning codes. In a press release from NVIDIA published Thursday, Tokyo Tech's Satoshi Matsuoka, a professor of computer science who is building the system, said, "NVIDIA's broad AI ecosystem, including thousands of deep learning and inference applications, will enable Tokyo Tech to begin training TSUBAME 3.0 immediately to help us more quickly solve some of the world's once unsolvable problems."

There is an urgent need to expand the AI epistemic community beyond the specific geographies in which it is currently clustered. Artificial Intelligence (AI) is no longer the subject of science fiction and is profoundly transforming our daily lives. While computers have already been mimicking human intelligence for some decades now using logic and if-then kind of rules, massive increases in computational power are now facilitating the creation of'deep learning' machines i.e. algorithms that permit software to train itself to recognise patterns and perform tasks, like speech and image recognition, through exposure to vast amounts of data. These deep learning algorithms are everywhere, shaping our preferences and behaviour. Facebook uses a set of algorithms to tailor what news stories an individual user sees and in what order.

As I read this post from the World Economic Forum, This is why China has the edge in Artificial Intelligence, what struck me wasn't whether China has an edge in AI, or even if I care. It made me wonder, are these factors essential to building a solid foundation for AI? Does high performance in these areas give an edge to AI projects? And, overall, my answer was: somewhat, but misleading. Your focus should not be on the amount of data, but the data available that could apply to the problem you have defined.

The second robot Tokyo Electric Power Company (TEPCO) sent into Fukushima's unit 2 reactor also failed to finish its mission. Now, it's the machine's left crawler belt that stopped working (PDF) altogether, forcing TEPCO to cut off its tether and to leave it inside. Toshiba designed these scorpions specifically to examine Unit 2's condition and to locate the melted uranium fuel within. The information would help Tepco figure out the best and safest way to clean the fuel up. The power company still isn't sure whether the robot's crawler belt stopped working due to the radiation levels inside or due to all the debris the first machine wasn't able to clear.