This article is a part of an evolving theme. Here, I explain the basics of Deep Learning and how Deep learning algorithms could apply to IoT and Smart city domains. Specifically, as I discuss below, I am interested in complementing Deep learning algorithms using IoT datasets. I elaborate these ideas in the Data Science for Internet of Things program which enables you to work towards being a Data Scientist for the Internet of Things (modelled on the course I teach at Oxford University and UPM – Madrid).

Here I offer a few off-the-beaten-path interesting problems that you won't find in textbooks, data science camps, or in college classes. These problems range from applied maths, to statistics and computer science, and are aimed at getting the novice interested in a few core subjects that most data scientists master. The problems are described in simple English and don't require math / stats / probability knowledge beyond high school level. My goal is to attract people interested in data science, but who are somewhat concerned by the depth and volume of (in my opinion) unnecessary mathematics included in many curricula. I believe that successful data science can be engineered and deployed by scientists coming from other disciplines, who do not necessarily have a deep analytical background yet are familiar with data.

In the summer of 1967, Secretary of Defense Robert McNamara commissioned a group of thirty-six scholars to write a secret history of the Vietnam War. The project took a year and a half, ran to seven thousand pages, and filled forty-seven volumes. Only a handful of copies were made, and most were kept under lock and key in and around the Beltway. One set, however, ended up at the RAND Corporation, in Santa Monica, where it was read, from start to finish, by a young analyst there named Daniel Ellsberg. Ellsberg was dismayed by what he learned. For a generation, the U.S. government had been lying to the American people about the Vietnam War. He put the first of the volumes in his briefcase, praying that the security guards at RAND would not stop him, and made his way to a small advertising agency in West Hollywood, where a friend told him there was a Xerox machine he could use. "It was a big one, advanced for its time, but very slow by today's standards," Ellsberg writes in his 2002 autobiography, "Secrets: A Memoir of Vietnam and the Pentagon Papers": It could do only one page at a time, and it took several seconds to do each page. I tried pressing the book down on the glass to do two pages at a time, but the middle section was faint and uneven. Fortunately the books were bound with metal tapes through holes so they could be taken apart. . . . The machine didn't collate, and the bar had to come back and travel just as slowly for each copy.

At the start of December, Stephen Hawking issued a stark warning: Automation and AI are going to destroy middle-class jobs, risking massive political upheaval. "The automation of factories has already decimated jobs in traditional manufacturing," the famed physicist wrote, "and the rise of artificial intelligence is likely to extend this job destruction deep into the middle classes." It's an issue that Shahar Waiser knows about first-hand: In the coming years and decades, almost his entire workforce will become obsolete. The Russian-born entrepreneur is the CEO of Gett -- a taxi app used by thousands of cabbies in more than 100 cities around the world. And all of their jobs are at risk of being replaced by self-driving cars.

In the summer of 1967, Secretary of Defense Robert McNamara commissioned a group of thirty-six scholars to write a secret history of the Vietnam War. The project took a year and a half, ran to seven thousand pages, and filled forty-seven volumes. Only a handful of copies were made, and most were kept under lock and key in and around the Beltway. One set, however, ended up at the RAND Corporation, in Santa Monica, where it was read, from start to finish, by a young analyst there named Daniel Ellsberg. Ellsberg was dismayed by what he learned. For a generation, the U.S. government had been lying to the American people about the Vietnam War. He put the first of the volumes in his briefcase, praying that the security guards at RAND would not stop him, and made his way to a small advertising agency in West Hollywood, where a friend told him there was a Xerox machine he could use. "It was a big one, advanced for its time, but very slow by today's standards," Ellsberg writes in his 2002 autobiography, "Secrets: A Memoir of Vietnam and the Pentagon Papers": It could do only one page at a time, and it took several seconds to do each page. I tried pressing the book down on the glass to do two pages at a time, but the middle section was faint and uneven. Fortunately the books were bound with metal tapes through holes so they could be taken apart. . . . The machine didn't collate, and the bar had to come back and travel just as slowly for each copy.

In my opinion, two innovations have reached clear "tipping points" in 2014 and herald the beginning of new times in the field of money and personal transportation. Software companies are taking leadership positions in established economies and will transform them in a durable manner. In less than a century, money will have gone from physical to plastic to digital. Driving will evolve from an art, mechanically operated by humans, to a science programmed by machine learning algorithms. Digital currency is pushing our economy squarely into the influence of the algorithm. In the case of driving, a new era is emerging – the era of machine learning. It's now become quite realistic that machines are able to execute functions at similar, if not higher, levels than humans. I'd like to propose we append Andreessen memo: "Software might eat the world, Marc, but, algorithms will end up digesting it." And since "money fuels our economy", I couldn't resist lumping both predictions in the same piece and proposing an appendix to Marc's memo.

About 20 percent of youth in the United States live with a mental health condition, according to the National Institute of Mental Health. The good news is that mental health professionals have smarter tools than ever before, with artificial intelligence-related technology coming to the forefront to help diagnose patients, often with much greater accuracy than humans. A new study published in the journal Suicide and Life-Threatening Behavior, for example, showed that machine learning is up to 93 percent accurate in identifying a suicidal person. The research, led by John Pestian, a professor at Cincinnati Children's Hospital Medical Center, involved 379 teenage patients from three area hospitals. Each patient completed standardized behavioral rating scales and participated in a semi-structured interview, answering five open-ended questions such as "Are you angry?" to stimulate conversation, according to a press release from the university.

This was the title of a very popular book published in 2012, featuring several job interview questions (brain teasers) asked by Google's hiring managers to candidates. They apparently dropped all these questions, as they found out that they were not good indicators of career success. I had one phone interview with Google long ago, and was rejected right away. The interviewer was just focused on very technical details, and spent all her time arguing about Lasso regression, and was clearly looking for a specialist, dismissing people with a broad range of skills and non-standard approach to solving tech problems. Big companies do not value things like intuition, innovation, vision or a disruptive mindset (despite claiming the contrary), and for good reasons.

I remember meeting Gerd Leonhard [Futurist, Author and a raft of other titles] for the first time in a particularly crowded Benugo in Covent Garden. The meeting came after several near misses and during one of his gigs in London and we decided just to wing it. The fries were unmemorable but the conversation probably set me on the path I find myself travelling today. I have quizzed him on his latest book "Technology Vs. Humanity" [Amazon] in which he poses some interesting questions about the future of the human race and technology but essentially asks; "Are you on team human, or not...? We are at a pivot point in human history [and you need to choose]."

When the U.S. Department of Veterans Affairs awarded a five-year contract to Flow Health for advancing artificial intelligence, the startup stepped into the national health IT spotlight. Healthcare IT News caught up with Flow Health CEO Alex Mishkin to discuss his vision for AI, cognitive computing and machine learning, what a federal contract means to a young company and what the incoming Trump administration might mean for innovation in America. Q: What does winning a VA contract mean to an emerging company like Flow Health? A: The VA approached us and said'imagine what you can help us do with our data.' It's super exciting to be working with the largest integrated clinical data set in the United States: 22 million unique people, spanning 20 years so it's a rich longitudinal data.