Summary: In this last article in our series on recommenders we look to the future to see how the rapidly emerging capabilities of Deep Learning can be used to enhance recommender performance. In our first article, "Understanding and Selecting Recommenders" we talked about the broader business considerations and issues for recommenders as a group. In our second article, "5 Types of Recommenders" we attempted to detail the most dominant styles of Recommenders. Our third article, "Recommenders: Packaged Solutions or Home Grown" focused on how to acquire different types of recommenders and how those sources differ. In this last article in our series on recommenders we look to the future to see how the rapidly emerging capabilities of Deep Learning can be used to enhance performance.

On May 11, 1997, a computer showed that it could outclass a human in that most human of pursuits: playing a game. The human was World Chess Champion Garry Kasparov, and the computer was IBM's Deep Blue, which had begun life at Carnegie Mellon University as a system called ChipTest. One of Deep Blue's creators, Murray Campbell, talked to the IDG News Service about the other things computers have learned to do as well as, or better than, humans, and what that means for our future. What follows is an edited version of that conversation. IDGNS: Is it true that you and Deep Blue joined IBM at the same time?

Garry Kasparov, one of the greatest chess players of all time, is famous for his pair of faceoffs against the IBM supercomputer Deep Blue. Kasparov won the first match against the computer, 4-2, in 1996, but lost in the rematch, 3½-2½, in 1997. He recently published a book, "Deep Thinking," about the experience. Business Insider recently spoke with Kasparov about Deep Blue, his thoughts on AI, and machine advancements over the past 20 years -- and how he sees the interplay between machine intelligence and humanity. This interview has been edited for clarity and length. Garry Kasparov: AI as a concept is surrounded by mythology. Most of the things we mention we understand. You know, if we say "white," we all see it's white. If we talk about elements of computer science or some general items, we are in agreement.

I'd love to talk more about Flowcast, but I'm still not able to shake the image of you making a robotic submarine run by San Diego poolside (laughs). As a STEM enthusiast, I have been in awe of IBM Watson's capabilities. And I feel it's an honor to be talking to someone who has contributed to its capabilities. Now, let's come back to Flowcast. Can you share more information and shed more light on how Flowcast came about?

Microsoft executive Todd Holmdahl has led teams to invent profitable new computing hardware products before. His latest project is his first with a chance of hauling in a Nobel Prize in physics as well as new revenue if it succeeds. Holmdahl previously oversaw the hardware design of the Xbox and Xbox360 consoles, which rake in billions for Microsoft each year. Late last year he was appointed the leader of a swelling band of mathematicians, physicists, and engineers trying to add mighty computers powered by quantum physics to Microsoft's menu of cloud computing services. Holmdahl speaks about quantum computing like a tech executive would a new line of business, not a speculative physics or R&D project.

On January 22nd, two days after President Trump was inaugurated, he received a memo from his new Secretary of Defense, James Mattis, recommending that the United States launch a military strike in Yemen. In a forty-year career, Mattis, a retired Marine Corps general and a veteran of the wars in Afghanistan and Iraq, had cultivated a reputation for being both deeply thoughtful and extremely aggressive. By law and by custom, the position of Defense Secretary is reserved for civilians, but Mattis was still a marine at heart. He had been out of the military for only three years (the rule is seven), and his appointment required Congress to pass a waiver. For the first time in his professional life, he was going to the Pentagon in a suit and tie. Mattis urged Trump to launch the raid swiftly: the operation, which was aimed at one of the leaders of Al Qaeda in Yemen, required a moonless night, and the window for action was approaching. Under previous Administrations, such attacks entailed ...

Recently I was privileged to sit on the committee that selects the winner of the Karl Pearson Prize. KP was, of course, an early mathematical statistician, famous for many commonly-used statistical methods and tools including histograms, the correlation coefficient, the method of moments, p-values, the chi-squared test and principal components analysis. He is also infamous for his highly racist views, support for eugenics, anti-semitism and for refusing a knighthood. All that aside, the job of the committee was to select an English-language article or book published in the last 30 years that has made a stand-alone research contribution, and which has had major influence on one or more of statistical theory, statistical methodology, statistical practice and application. There were many excellent nominations, but we decided to award the 2017 prize to Rod Little and Don Rubin for their 1987 book "Statistical analysis with missing data".

By their own admission, Jack Ma is uncomfortable with Artificial Intelligence (AI) and Elon Musk is scared. But why? Contrary to popular perception AI is old. To be precise it's 51-years old, widely acknowledged to have been born at a conference at Dartmouth College in 1956. That conference was attended by a diverse group of people. Three of them presented the Logic Theorist, the world's first true artificial intelligence programme. Two of them were Allen Newell and Herbert Simon.

AI was very popular 30 years ago, then disappeared, and is now making a big come back because of new robotic technologies: driver-less cars, automated diagnostic, IoT (including vacuum cleaning and other household robots), automated companies with zero employee, soldier robots, and much more. Will AI replace data scientists? I think so, though data scientists will be initially replaced by "low intelligence" yet extremely stable and robust systems. There has been a lot of discussions about the automated statistician. I am myself developing data science techniques such as Jackknife regression that are simple, robust, suitable for black-box, machine-to-machine communications or other automated use, and easy to understand and pilot by the layman, just like a Google driver-less car can be "driven" by an 8 years old kid.

There are only a few industries in which automation isn't threatening some job roles. "While automation will eliminate very few occupations entirely in the next decade, it will affect portions of almost all jobs to a greater or lesser degree, depending on the type of work they entail," according to McKinsey Quarterly. Roles that require empathy, like therapists and psychologists, as well as jobs that are highly reliant on social and negotiation skills, like managerial positions, are less threatened by automation, according to The Future of Employment: How Susceptible Are Jobs to Computerisation? Those of us in roles that require creative thinking and original ideas -- like content creation -- are also deemed at less risk of having our jobs swiped from under our noses by something harder-working, "smarter," and cheaper to maintain. It's pretty tough to envision a machine generating great content ideas, not to mention creating that content -- content worth consuming.