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.

Bill Gates, the co-founder of Microsoft and world's richest man, said in an interview Friday that robots that steal human jobs should pay their fair share of taxes. "Right now, the human worker who does, say, $50,000 worth of work in a factory, that income is taxed and you get income tax, Social Security tax, all those things," he said. "If a robot comes in to do the same thing, you'd think that we'd tax the robot at a similar level." Gates made the remark during an interview with Quartz. He said robot taxes could help fund projects like caring for the elderly or working with children in school.

A panel of esteemed cryptographers at RSA 2017 expressed doubt over artificial intelligence's applicability in the cybersecurity space, tossing cold water on what otherwise appeared to be a hot technology at the conference. "The real problem is that what AI and machine learning is great at is lots of data and dealing with it effectively and what we're dealing with, with the serious attacks are anomalous situations and AI does not look like it's going to be useful there," said Susan Landau, professor of cybersecurity policy and professor of computer science at Worcester Polytechnic Institute, during the session earlier this week. Adi Shamir, Borman professor of computer science at the Weizmann Institute in Israel, said that AI would likely be helpful in defending against attacks when they happen, but not sniffing out threats before they materialize. "I think that AI can be very helpful on the defensive side," said Shamir, who earlier this month was named a recipient of the 2017 Japan Prize. "I doubt it would be so helpful for new zero days because this requires more ingenuity and originality. But when you talk about finding deviations from normal behavior, I think that AI systems are going to be very useful... Ronald Rivest, a professor at the MIT Institute, said that he was "skeptical" that AI would have a significant impact on security. He did, however, acknowledge that AI played an important role in spreading fake news and propaganda during the U.S. election. "There are AI bots talking on chat rooms... adding misinformation and disinformation," said Rivest. "I can imagine 10 or 15 years from now, we're going to find ourselves competing to find the humans among just a sea of bots talking to each other intelligently." But Shamir had a different vision for AI as it becomes super intelligent in the next 15 years: I can foresee a situation which we'll give all of the available data about cybersecurity to this program and it will think for a long time and then say in a calm voice, 'In order to save the Internet I'll have to kill it,'" said Shamir, facetiously.

A fresh scrape from Glassdoor gives us a good idea about what applicants are asked during a data scientist interview at some of the top companies. Unfortunately for us, almost every company has their interviewees sign NDAs. Since Glassdoor allows anonymity, a few brave souls have given us some fantastic examples of what they were asked during the interview process at top companies like Facebook, Google, and Microsoft. If you find yourself unable to answer some of the questions below, consider checking out a course or a book on the subject. If you'd like to share your answer(s) to any of the questions, leave a comment and I'll add the top ones to the post.

A group of Silicon Valley roboticists who developed Shakey, a pioneer mobile robot project, gathered last night at the Computer History Museum in Mountain View, Calif., to dedicate the tall, wheeled machine as an IEEE Milestone. Joining the group were other robotics visionaries, IEEE officers and local IEEE section members, and fans of computing history. Shakey, developed at SRI International between 1966 and 1972, was honored as the world's first mobile, intelligent robot. "Stanford Research Institute's Artificial Intelligence Center developed the world's first mobile, intelligent robot, SHAKEY. It could perceive its surroundings, infer implicit facts from explicit ones, create plans, recover from errors in plan execution, and communicate using ordinary English. SHAKEY's software architecture, computer vision, and methods for navigation and planning proved seminal in robotics and in the design of web servers, automobiles, factories, video games, and Mars rovers."

Machine learning and artificial intelligence are terms that any carbon-based humanoid operating in 2017 has probably heard or discussed in one way or another. What was the stuff of science fiction two decades ago is now science, and it is present everywhere. Machine Learning – type of artificial intelligence enabling computers to learn without the explicit programming to do so. It is essentially the evolution of programming, whereby millions of formulae and functions are converged to work in synchronicity and analyse masses of data. Almost like adding a trend line to a data set, on top of a data set, on top of another data set, ad infinitum.

Obviously, it's artificial intelligence, but it is an intelligence we can use to help us determine what's useful in all of the big data being collected through the Internet of Things (IoT). And we can teach it to tell us what's important to investigate there, too. AI won't replace humans, but it will give us a huge boost. Paul Muller, vice president of strategic marketing at Hewlett Packard Enterprise, has a conversation with technology analyst Theo Priestley about AI, including what it is, how to use it, fears about it, and how it can help us long term. They talk about the myths around AI--such as it's a human replacement vehicle rather than a way to augment our work, or it's just software rather than hardware and software--and how artificial intelligence doesn't mean it's not intelligent and doesn't practice self-preservation.

Soaring over the world's highest buildings and racing a solar-powered car across the desert may not be on your vacation itinerary this summer, but you can still see what such experiences might be like, thanks to the spectacular new IMAX film, "Dream Big: Engineering Our World." The movie takes audiences from Dubai to Australia, while sharing stirring stories about the impact engineers have on society. Narrated by Academy Award winner Jeff Bridges, it celebrates the inspiring work and people behind these engineering feats. Featured footage includes the Great Wall of China and the Golden Gate Bridge, not to mention an underwater robot developed by a team of students from an underprivileged high school in Phoenix. Made up primarily of Mexican immigrants, the robotics team entered its creation in a competition and succeeded against all odds.

You can read the full interview about the future of recruitment and how AI and Machine Learning will have an impact on the HR industry below in Textkernel's blog post. There's been a lot of talk about AI and Machine Learning in the staffing and recruiting industry, but let's define the problem space. If you think about the entire lifecycle of the recruiting process, in the next 5-10 years where will machines really make a difference and where will humans continue to be able to add value? Will recruiters have jobs in 2025? To me, it is without a doubt by now, that AI and Machine Learning will have a huge impact on all areas of business.

I spent most of last week in the Midtown Hilton in New York City attending "Legaltech 2017," or "Legalweek: The Experience," or some sort of variation of the two. For the most part, it pretty much had the same feel as every other Legaltech I've attended. But I agree with my fellow Above the Law tech columnist, Bob Ambrogi, that ALM deserves kudos for trying to change the focus a bit. It may take a year or two of experimentation to get it right, but at least they're trying. This year, one of the topics that popped up over and over throughout the conference was artificial intelligence and its potential impact on the practice of law.