Big data and artificial intelligence will have the greatest impact in the health-tech industry in the year ahead, according to a survey released yesterday. The survey, performed by Silicon Valley Bank, polled 122 founders, execs and investors in health care technology companies about the biggest challenges, most promising technology and biggest growth sectors in the industry for the coming year. The most promising technology for the sector was utilization of big data, with 46% of of responders ranking it number 1, closely followed by artificial intelligence, with 35%. Consumer, patient and client adoption was the biggest challenge reported by polled individual for the industry, with 37% placing it at the top. Thirty four percent reported that regulation was the largest challenge for the sector.

Nvidia Corp. debuted two new graphics processors for systems and computers that use artificial intelligence putting them directly in competition with chips giant Intel. Nvidia hopes that its two new graphic processors would be utilized by tech companies in their products that run artificial intelligence software like smartphones, driverless cars and smart homes, according to a report by Bloomberg. The chipmaker, which is the leading company in video gaming graphics, released a new set of graphics chips for running software that "makes split-second decisions needed when everything from phones to cars to internet search engines respond to inputs such as speech, images and moving objects," according to Bloomberg. Nvidia calls the new chip "Tesla P4 chip" and is designed for servers used in massive data centers that process big data like Facebook, "The Division" and "World of Warcraft." Based on Nvidia's Pascal design, the P4 is more than three times as efficient at processing images than its predecessor and 40 times more efficient than Intel server chips, as stated in Nvidia's report.

Earth's oceans are having a rough time right now. They're oily, hot, acidic, full of dead fish--and their levels are rising. But even though these things are true, it can be hard to grok (or muster up the will to care about) the oceans' subtle changes over decades. Every time you go to the beach, everything's still as blue and salty and vast as it ever was. But these changes directly impact human life (just ask the Marshall Islands). So to make the ocean's plight more relatable, a Swedish sustainability group is putting out a message that will hit you where it counts: right in the nerd.

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

Soulless computer algorithms are already churning out weather bulletins, sports reports, rap lyrics and even passable Chinese poetry. But it seems machines have now taken another step towards replacing human enterprise by generating their own reviews of serious academic journal papers that are able to impress even experienced academics. Using automatic text generation software, computer scientists at Italy's University of Trieste created a series of fake peer reviews of genuine journal papers and asked academics of different levels of seniority to say whether they agreed with their recommendations to accept for publication or not. In a quarter of cases, academics said they agreed with the fake review's conclusions, even though they were entirely made up of computer-generated gobbledegook – or, rather, sentences picked at random from a selection of peer reviews taken from subjects as diverse as brain science, ecology and ornithology. "Sentences like'it would be good if you can also talk about the importance of establishing some good shared benchmarks' or'it would be useful to identify key assumptions in the modelling' are probably well suited to almost any review," explained Eric Medvet, assistant professor at Trieste's department of engineering and architecture, who conducted the experiment with colleagues at his university's Machine Learning Lab.

As enterprise enters further into the digital age, data has become the strategic asset that knowledge workers, small or large, rely on to guide their decisions. However, managing such large volumes of data has exposed some unprecedented challenges for the enterprises. Enterprises have learned that the data that they hold, comes in a variety of formats, resides in different and distributed systems and is specific to the organization and its domain. Setting these challenges as the backdrop, IBM's Watson division has built solutions that not only allow for data connectivity but also the analysis of unstructured data and its customization to an enterprise domain. IBM Watson Explorer is Watson's flagship product for text analytics and discovery.

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This is the first question that I hear every Thursday morning. It doesn't come from any of my co-workers. It comes from standup-bot, our friendly digital task-master that we built using Slack's Howdy. We use the bot to collect weekly status reports on product development and billable work. It saves us time when compared to traditional update meetings.

You've trained a classifier and it's performing well on the validation set – but does the model exhibit sound judgement or is it making decisions based on spurious criteria? Can we trust the model in the real world? And can we trust a prediction (classification) it makes well enough to act on it? Can we explain why the model made the decision it did, even if the inner workings of the model are not easily understandable by humans? These are the questions that Ribeiro et al. pose in this paper, and they answer them by building LIME – an algorithm to explain the predictions of any classifier, and SP-LIME, a method for building trust in the predictions of a model overall.

Microsoft is also teaming with the Knight Cancer Institute on AI that would personalize those drug mixes on a patient-by-patient basis. They're primarily focused on acute myeloid leukemia, where you might end up battling multiple leukemias at once -- machine learning could identify just what you're dealing with and treat it accordingly. Another effort would lean heavily on computer vision to understand how a tumor is reacting to treatments. Human doctors can easily identify tumors, Microsoft notes, but they can't always tell how tumors are changing or how they're affecting the health of nearby cells. The Redmond crew is even more ambitious than that.