In this new episode of Real Smart Marketing, we've posed this big question to four of our favorite influencers. If you're like me, the first thing that comes to mind when you hear "AI" might be this: But as I've come to realize, what we're talking about is a lot less creepy. AI is changing the face of SEO, but not like that. We're talking about algorithms that enable machines to make connections, "learn" to process data, and apply its learning in future tasks. Basically, improvements in artificial intelligence like deep learning and natural language processing mean that search engines are becoming smarter and more human-friendly.

The World Health Organization has estimated that there is a global shortfall of approximately 4.3 million doctors and nurses, with poorer countries disproportionately impacted. In the U.S., these shortages are less acute; instead, the country struggles with ever-increasing health care costs, which often translate into limits on the time a patient is able to spend with a doctor. One study estimated that U.S. doctors spend on average just 13 to 16 minutes with each patient. So against this backdrop of a global shortage in doctors and nurses, and cost-driven strains in patient care, let's take a look at some of the ways AI systems are being evaluated for use in medical care. In August 2016, an artificially intelligent supercomputer did in 10 minutes what would have taken human doctors weeks to achieve.

Maybe you would like to turn on the radio with a shimmy. Or shut off your desk lamp when you close your book at night. Neither of these is a particularly complicated interaction, yet each would involve developing all sorts of special hardware and software to make happen. Unless you have an Objectifier. Developed by Bjørn Karmann as a graduate project at the Copenhagen Institute of Interaction Design, the Objectifier is a camera that plugs into any electronic device.

So far the names of 34 of the 100 speakers are listed on the conference website. The name that is likely to be the most familiar to IProgrammer readers is that of Geoffrey Hinton, now a Google VP and the person who we consider as the principal pioneer of deep neural networks. Yoshua Bengio, head of MILA (Montreal Institute for Learning Algorithms) which was a recent recipient of extended Google funding is another many will recognize. If our recent //No Comment coverage of the cleverhans adversarial learning library project attracted your attention all three co-authors of the paper are among the speakers. At the moment one third of the speakers at the conference are women and it is to be hoped this balance is maintained as more names are revealed.

AI is the next big thing, and I am not just referring here to my company brand. I mean artificial intelligence, or so-called machine learning technologies. The impact for financial advice and advisers is positive, profound and, I have to say, imminent. When global information technology research firm Gartner issued its strategic technology predictions for 2017 last October, artificial intelligence and advanced machine learning themes led the top 10. As Gartner's report states: "During the next 10 years, virtually every app, application and service will incorporate some level of AI in much the same way as consumer appliances have incorporated microprocessors. "Some of these apps will be obvious intelligent apps that could not exist without AI and machine learning.

The dirty little secret is out about artificial intelligence. This one is more insidious. Data scientists, AI experts and others have long suspected it would be a problem. But it's only within the last couple of years, as AI or some version of machine learning has become nearly ubiquitous in our lives, that the issue has come to the forefront. Name an -ism, and more likely than not, the results produced by our machines have a bias in one or more ways.

Neural networks are an exciting field of software development used to calculate outputs from input data. While the idea seems simple enough, the implications of such networks are staggering--think optical character recognition, speech recognition, and regression analysis. With Neural Networks Using C# Succinctly by James McCaffrey, you'll learn how to create your own neural network to solve classification problems, or problems where the outcomes can only be one of several values. Learn about encoding and normalizing data, activation functions and how to choose the right one, and ultimately how to train a neural network to find weights and bias values that provide accurate predictions.

Artificial intelligence has been the focus of technology research for quite a while now. Recent years have seen considerable development in machine learning, narrow AI and deep learning. What does 2017 promise us in the realm of AI? How closer could we come to the glorious liberating visions of AI that have been the subject of fantasy and disbelief in previous decades? In this article, we talk about the five most significant developments that might be realized in 2017. With easy commercialization, greater investment in research and applicability across sectors, this could be the year when AI finally takes off as a commercially viable part of the technology industry.

As Friday night became Saturday morning, Dong Kim sounded defeated. Kim is a high-stakes poker player who specializes in no-limit Texas Hold'Em. The 28-year-old Korean-American typically matches wits with other top players on high-stakes internet sites or at the big Las Vegas casinos. But this month, he's in Pittsburgh, playing poker against an artificially intelligent machine designed by two computer scientists at Carnegie Mellon. No computer has ever beaten the top players at no-limit Texas Hold'Em, a particularly complex game of cards that serves as the main event at the World Series of Poker. Nearly two years ago, Kim was among the players who defeated an earlier incarnation of the AI at the same casino.