As the automation of work processes continues apace by means of artificial intelligence, what does this mean for our jobs? Research carried out by McKinsey, analysing around 2,000 individual work activities, suggests that, rather than eliminating jobs altogether, automation will take over discrete tasks and thus redefine current roles – at least in the short term. Here are McKinsey's four fundamentals of workplace automation: Almost half of the activities individuals are paid to perform could be automated using currently available technologies. Narrative Science's artificial intelligence system, Quill, for example, can analyse raw data and write reports in natural language that readers would assume had a human author – all in a matter of seconds. In short, automation is likely to change the vast majority of occupations – at least to some extent.

Thanks to the supercomputer, SMU's food AI project has successfully recognised the 100 most popular dishes in Singapore. The Singapore Management University (SMU) will be using the NVIDIA DGX1 deep learning supercomputer to conduct AI research for Singapore's Smart Nation project. The research will take place at the SMU Living Analytics Research Centre (LARC), which is supported and funded by Singapore's National Research Foundation (NRF). Providing performance that is equivalent to 250 conventional servers, the supercomputer will reduce the time it takes for researchers to train sophisticated deep neural networks. The solution is also built on NVIDIA Tesla P100 GPUs, which utilise the latest Pascal GPU architecture.

Sometimes the biggest gifts arrive in the most surprising ways. A couple in Singapore, Tianqiao Chen and Chrissy Luo, were watching the news and saw a Caltech scientist help a quadriplegic use his thoughts to control a robotic arm so that -- for the first time in more than 10 years -- he could sip a drink unaided. Inspired, Chen and Luo flew to Pasadena to meet the scientist, Richard Andersen, in person. Now they've given Caltech $115 million to shake up the way scientists study the brain in a new research complex. Construction of the Tianqiao and Chrissy Chen Institute for Neuroscience at Caltech will begin as early as 2018 and bring together biology, engineering, chemistry, physics, computer science and the social sciences to tackle brain function in an integrated, comprehensive way, university officials announced Tuesday.

We are approaching the end of another year, and with that comes time for reflection on how far we've come in the last 12 months and speculation about what's ahead. If we look back on the last year in the world of sales and marketing, there's one trend in particular that's sparked notable enthusiasm from B2B marketers: the emergence of Account-Based Marketing (ABM) as a category. In 2016, we saw explosive growth in ABM acceleration and adoption, with more than 70 percent of B2B companies focused on driving ABM programs, according to a recent study by SiriusDecisions. This growth has definitely impacted how marketers structure their tech stacks today. ABM has gone from a "nice to have" marketing tactic to a key solution that marketing departments are actively working to execute within their organizations.

The new platform uses artificial intelligence to help drive the transition from traditional marketing creative to digital-first, mobile video marketing for businesses of all sizes. Magisto for Marketers introduces Intelligent Marketing creative into Magisto's patented artificial intelligence engine – Emotional Sense Technology . This gives marketing professionals the ability to easily create marketing videos for a variety of disciplines including content marketing, product marketing, brand marketing and performance marketing. Magisto's new offering allows marketers to create multiple variations of optimized videos by use case, platform, audience, message, product and ROI performance. The AI technology incorporates easy-to-use editing styles designed for businesses, the ability to add custom branding to clone videos and to direct Magisto's AI to refine messaging and produce multiple A/B test creatives.

Tthere's a input x, like sentence from review, a classified MODEL that generates the output, y. The predicted class, like positive or negative reviews. Personalized medical diagnosis: input of indicators like temperature, x-ray analysis, medical tests, for a disease classifier model, that predicts healthy, cold, flu, pneumonia etc. And the last, the application for reading minds, with brain images that, applied to a model classifier it is possible to discover which image is seeing by the person.

Employers: do the ladies on your payroll have any "female weaknesses" that would make them mentally or physically unfit for the job? The question comes to you courtesy of the year 1943. It was posed in a guide to hiring women, written for the flummoxed male supervisors at Transportation Magazine tasked with integrating a new female workforce during a wartime shortage of manpower. Back then, you wouldn't be surprised to see logical reasoning like "Men are to programmers as women are to homemakers". Or "Men are to surgeons what women are to nurses".

One contribution to this pragmatic efficiency is supplied by the guidance a representation provides for organizing information so as to facilitate making the recommended inferences.

A few weeks ago, Montreal-based AI pioneer Yoshua Bengio launched Element AI, a Silicon Valley-style startup incubator dedicated to deep learning. Despite its modest (but growing) startup scene, Montreal is already a hotbed in AI talent, with a trove of deep learning researchers across the city. Bengio -- along with Jean-François Gagné, Nicolas Chapados, Jean-Sébastien Cournoyer, and the rest of their team of tech mavericks -- is hoping to accelerate the proliferation of AI startups and researchers in Montreal to turn the city into an AI center. As a proud Montrealer and bot maker, I couldn't be happier with this news. Surprisingly, though, the launch received some mixed feedback locally.

Nvidia cofounder Chris Malachowsky is eating a sausage omelet and sipping burnt coffee in a Denny's off the Berryessa overpass in San Jose. It was in this same dingy diner in April 1993 that three young electrical engineers--Malachowsky, Curtis Priem and Nvidia's current CEO, Jen-Hsun Huang--started a company devoted to making specialized chips that would generate faster and more realistic graphics for video games. East San Jose was a rough part of town back then--the front of the restaurant was pocked with bullet holes from people shooting at parked cop cars--and no one could have guessed that the three men drinking endless cups of coffee were laying the foundation for a company that would define computing in the early 21st century in the same way that Intel did in the 1990s. "There was no market in 1993, but we saw a wave coming," Malachowsky says. "There's a California surfing competition that happens in a five-month window every year. When they see some type of wave phenomenon or storm in Japan, they tell all the surfers to show up in California, because there's going to be a wave in two days. We were at the beginning."