When I first started out learning about machine learning algorithms, it turned out to be quite a task to gain an intuition of what the algorithms are doing. Not just because it was difficult to understand all the mathematical theory and notations, but it was also plain boring. When I turned to online tutorials for answers, I could again only see equations or high level explanations without going through the detail in a majority of the cases. It was then that one of my data science colleagues introduced me to the concept of working out an algorithm in an excel sheet. And that worked wonders for me.

After AlphaGo's historic victory against South Korean grandmaster Lee Sedol in March 2016, Go teacher Jianlun Qian felt a sense of impending crisis. He fretted about the demise of the game brought about by AI. Now that a more powerful AlphaGo has beaten the world's number one player, though, Qian feels differently. At DeepMind's Go summit in Wuzhen last week, Qian, a teacher at the local Go association's training center, contemplated a very different future in which humans and AI can complement each other. "I'm indifferent to the results now," said Qian, who coaches about 50 preschoolers in Wuzhen.

If you find yourself daydreaming during class, Nestor could help you regain your focus and help professors improve the least-engaging parts of their lecture. You may have gotten away with staring off into space during your college years, but alas, the students of tomorrow may not be so lucky. In fact, some business students of today will soon find that their attention spans (or lack thereof) are being closely monitored. It's all thanks to a combination of artificial intelligence and facial analysis, which researchers are using to detect whether or not students are actually paying attention in lectures. This combination forms a new kind of software called Nestor, and at its September launch, will be used in two online courses at the ESG business school. It's the brainchild of LCA Learning, and it may just change the way we take classes.

On today's episode, Mitch Jeserich hosts George Zarkadakis. Zarkadakis, a PhD in Artificial Intelligence, is the author of several novels including his latest book In Our Own Image: Savior or Destroyer? The book explains AI's history, technology, its potential; its manifestations in intelligent machines; its connections to neurology and consciousness, as well as -- perhaps most tellingly -- what AI reveals about us as human beings. Mitch also talks to Bart Ehrman about his book Forged: Writing in the Name of God – Why the Bible's Authors Are Not Who We Think They Are. Ehrman is a professor of religious studies at the University of North Carolina at Chapel Hill.

Jobs requiring STEM skills (science, technology, engineering and math) are among the best in the U.S. Here's why. Would-be technology workers learn coding and programming skills at Galvanize, one of a number of new boot camps that help teach skills suited to the tech booming tech economy that has a particular need for data science experts. SAN FRANCISCO -- The nation's best jobs boast salaries that average $100,000 and up, offer generous company benefits, and promise to have recruiting suitors fighting for your hand. But they are highly technical roles carrying job descriptions like DevOps engineer and analytics manager that demand an alphabet soup of computer skills as well as incessant on-the-job learning. So do you have to be a math genius with a spare PhD in physics to get one of these great gigs?

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.

Roughly speaking, my machine learning journey began on Kaggle. "Regression models predict continuous-valued real numbers; classification models predict'red,' 'green,' 'blue.' Typically, the former employs the mean squared error or mean absolute error; the latter, the cross-entropy loss. Stochastic gradient descent updates the model's parameters to drive these losses down." Furthermore, to fit these models, just import sklearn. A dexterity with the above is often sufficient for -- at least from a technical stance -- both employment and impact as a data scientist. In industry, commonplace prediction and inference problems -- binary churn, credit scoring, product recommendation and A/B testing, for example -- are easily matched with an off-the-shelf algorithm plus proficient data scientist for a measurable boost to the company's bottom line. In a vacuum I think this is fine: the winning driver does not need to know how to build the car.

That's because, to paraphrase Amazon's Jeff Bezos, artificial intelligence (AI) is "not just in the first inning of a long baseball game, but at the stage where the very first batter comes up." Look around, and you will find AI everywhere--in self driving cars, Siri on your phone, online customer support, movie recommendations on Netflix, fraud detection for your credit cards, etc. To be sure, there's more to come. Featuring 30 lectures, MIT's course "introduces students to the basic knowledge representation, problem solving, and learning methods of artificial intelligence." It includes interactive demonstrations designed to "help students gain intuition about how artificial intelligence methods work under a variety of circumstances."

Might such things as school examinations, tests, marked classwork and progress checks soon all be a thing of the past? They will be if Rose Luckin, Professor of Learner-Centred Design at the Knowledge Lab at the University College, London (UCL) Institute of Education, has her way. She argues that the way school pupils are assessed today is unsatisfactory. "Decades of research have shown that knowledge and understanding cannot be rigorously evaluated through a series of 90-minute exams. The prevailing exam paradigm is stressful, unpleasant, can turn students away from education, and requires that both students and teachers take time away from learning. And yet we persist in relying on these blunt instruments, sending students off to universities and the workplace ill-equipped for their futures," writes Professor Luckin in a research paper.