artificial intelligence News for September 13 2017

How Food-Bots Are Changing How We Eat | Robots & Us | WIRED

Artificial intelligence and advanced automation are everywhere including our farm fields and kitchens. How will robots change the way we grow, harvest and …

Cornell CIS and Adobe collaboration creates artificial intelligence photo tool

If you’re a fan of making your photo into a Monet or Warhol, there’s now a way to make changes to a photograph by transferring the style and other elements from another photograph. Computer science professor Kavita Bala, doctoral student Fujun Luan, and Adobe collaborators Sylvian Paris and Eli Shechtman have released a paper detailing their new Deep Photo Style Transfer. The paper explains how the researchers have augmented style transfer, transposing the look of one photo onto another using neural networks to make sure the details of the original image are preserved. “What motivated us is the idea that style could be imprinted on a photograph but it is still intrinsically the same photo. This turned out to be incredibly hard. The key insight finally was about preserving boundaries and edges while still transferring the style.” Explore further: Monet’s worlds translated into realistic photos in Berkeley effort.
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1000+ images about Artificial Intelligence Technology on …

Artificial intelligence (AI) is the intelligence exhibited by machines or software. #AI | See more about Ibm, Technology and Stephen hawking.
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Artificial Intelligence | LinkedIn

View 319705 Artificial Intelligence posts, presentations, experts, and more. Get the professional knowledge you need on LinkedIn.
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Artificial Intelligence Is the New Business Intelligence

BI provides data and analytics to help business leaders make more informed decisions. Descriptive analytics summarizes business operations data and tells you what took place. As data volume grows, BI systems also start to incorporate simple predictive analytics. Simply defined, predictive analytics use your existing data to infer data that you don’t, or can’t, have. The most common use case of predictive analytics is to forecast the future trajectory of your data, though it’s important to realize that forecasting is not the only use case. The most powerful predictive analytics can also be used for data analyses that are not time-dependent. Sentiment analysis is a kind of predictive analytics that analyzes text data, such as social media conversations, and infers how the consumers feel about a product or a brand. Prescriptive analytics in advanced BI can recommend actions to optimize business processes, marketing effectiveness, ad targeting and many other business operations. Regardless of what the analytics might suggest, it is the human decision makers who invariably make the final decisions on what to do. This is often a good idea because traditional analytics are no match for human intelligence. Traditional business analytics are not accurate enough because of the tradeoffs among three types of constraints imposed by the data, the models and the sheer amounts of computing power. If AI is simply the automation of optimal decisions, we can get those same results from automating our familiar prescriptive analytics to select optimal solutions. As you can see, AI is merely the automation of an optimal sequence of decisions from prescriptive analytics. The intelligence of AI comes from the fact that it can leverage real-time feedback data to improve the models in prescriptive analytics so the next prescribed decision will always be better than the previous.
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How to Regulate Artificial Intelligence

The technology entrepreneur Elon Musk recently urged the nation’s governors to regulate artificial intelligence “Before it’s too late.” Mr. Musk insists that artificial intelligence represents an “Existential threat to humanity,” an alarmist view that confuses A.I. science with science fiction. Even A.I. researchers like me recognize that there are valid concerns about its impact on weapons, jobs and privacy. It’s natural to ask whether we should develop A.I. at all. Shouldn’t we take steps to at least slow down progress on A.I., in the interest of caution? The problem is that if we do so, then nations like China will overtake us. The A.I. horse has left the barn, and our best bet is to attempt to steer it. These three laws are elegant but ambiguous: What, exactly, constitutes harm when it comes to A.I.? I suggest a more concrete basis for avoiding A.I. harm, based on three rules of my own. First, an A.I. system must be subject to the full gamut of laws that apply to its human operator. This rule would cover private, corporate and government systems. We don’t want A.I. to engage in cyberbullying, stock manipulation or terrorist threats; we don’t want the F.B.I. to release A.I. systems that entrap people into committing crimes. We don’t want autonomous vehicles that drive through red lights, or worse, A.I. weapons that violate international treaties.
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3 Top Artificial Intelligence Stocks in Healthcare

A compounded annual growth rate of 40%: That’s how fast Accenture projects the market for artificial intelligence in healthcare will expand through 2021. It’s not just hype – and definitely not when it comes to the healthcare industry. Here’s why Google parent Alphabet, International Business Machines, and NVIDIA stand out as three of the top artificial intelligence stocks in healthcare. There’s a lot more that the company hopes to be able to accomplish with AI in healthcare. Alphabet’s AI initiatives in healthcare could become an increasingly important source of revenue in the future. IBM doesn’t use the phrase “Artificial intelligence” very often with its healthcare focus. Instead, the company prefers the term “Cognitive health.” Whatever it’s called Big Blue is a major player in advancing AI in the healthcare industry. IBM Watson is being used in healthcare in several ways. It’s helping government agencies deliver healthcare services more effectively. Is NVIDIA a stock to buy for healthcare AI? Absolutely. The company has developed an AI computing platform designed for the healthcare community. NVIDIA DGX for Healthcare processes huge amounts of data, which allows researchers to develop more effective cancer drugs, conduct faster genomic analysis, and obtain more precise imaging results. Many organizations currently use NVIDIA’s products to implement powerful AI solutions for healthcare. There could be some volatility in the months ahead, but the potential for AI in healthcare and practically every other industry is large enough that NVIDIA should continue to be a winner over the long run.
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