A Markov chain is a random process with the property that the next state depends only on the current state. For example: If you have the choice of red or blue twice the process would be Markovian if each time you chose the decision had nothing to do with your choice previously (see diagram below). How can Markov Chains help us? To start with we need to define some basic terminology. The changes of state within the system are called transitions, and the probabilities associated with various state-changes are called transition probabilities.

Have you ever wanted to order a bucket of fried chicken without having to speak to a single a human being? Now you can! KFC, in partnership with Chinese search engine giant Baidu, has just opened the world's first human-free fast food restaurant in Shanghai, reports SoHu. The intelligent robot concept store, Original (pronounced, "Original Plus"), looks unlike any KFC you've ever seen. The interior is designed in a traditional Chinese garden style with bamboo, flowers, and jade accents. Customers enter through a big circular doorway.

Taking inspiration from the way humans seem to learn, scientists have created AI software capable of picking up new knowledge in a far more efficient and sophisticated way. The new AI program can recognize a handwritten character about as accurately as a human can, after seeing just a single example. The best existing machine-learning algorithms, which employ a technique called deep learning, need to see many thousands of examples of a handwritten character in order to learn the difference between an A and a Z. The software was developed by Brenden Lake, a researcher at New York University, together with Ruslan Salakhutdinov, an assistant professor of computer science at the University of Toronto, and Joshua Tenenbaum, a professor in the Department of Brain and Cognitive Sciences at MIT. Details of the program, and the ideas behind it, are published today in the journal Science.

Whitewater river guide Bret Wojciak has long been a fan of the West's dramatic, cathedral-like canyons and the wild rivers that flow through many of them. In the last five years or so, he has found another reason to cherish these deep gorges: They force people to disconnect from their computers, tablets and cellphones, at least for a few days. The results can be remarkable, Wojciak told me on a recent Don Hatch River Expeditions rafting and kayaking trip down the Green River on the Colorado-Utah border. For three days, my family and a dozen other fortunate souls paddled rafts through Dinosaur National Monument in the 1,500-foot-deep, rainbow-striped Gates of Lodore canyon, bounced over moderate Class III rapids, hiked up trails to see native petroglyphs and dined on delicious campfire meals. Always a bit of an outlier, I tagged along in a hard-shell kayak and had the freedom to run rapids and surf waves on my own.

By rights, Yuval Noah Harari should be an anonymous academic buried in an obscure university department somewhere toiling away on his somewhat dusty discipline – medieval military history. He's a professor of history at the Hebrew University of Jerusalem and there is almost nothing in his background to suggest that he would write a book that has become one of the most talked about non-fiction bestsellers of the year – Sapiens. Or that he'd join the globetrotting TED-ocracy: the academic superstars who travel the world delivering keynotes on zeitgeisty topics, in Harari's case, the not inconsiderable subject of the history of the whole of mankind. When I meet him, he's just been the star turn at Penguin Random House's global sales conference. In May, he packed out Hay. Earlier this month, he delivered a TED talk.

The possible applications of AI for financial services can seem nearly limitless. Advances in AI and data analytics mean much greater quantities of data can be used to inform decision-making and asset price predictions, such as market events (product releases and recalls, regulatory approvals, acquisitions) and unstructured data (images, video footage and live streaming). AI tools are getting better at understanding context, too.

Human communication goes beyond words. It is complex, rich in nuances and frequently includes non-verbal signs. Yet despite our technological limitations it is not impossible for some aspects of communication to be emulated by a machine with surprising effect. This has been part of the challenge in developing Harlie (Human and Robot Language Interaction Experiment), a smartphone chatbot app developed by researchers at the CSIRO and University of Queensland. It's primarily aimed at people who may have trouble conversing including those with neurological conditions such as Parkinson's disease and dementia, or even autism. The following dialog excerpt took place between a human who has autism, let's call him Chris (pseudonym), and Harlie.

From New York National Guard: "A bomb disposal robot, piloted from a distance, examines a downed drone with explosive material tethered to it during training." Looks like that bomb was ... frozen in its tracks. Look, I'm not going to beat the lede from the New York National Guard, so here it is in full: The remote-controlled robot bumped across the divots of the grassy field until it reached the downed toy drone. Its camera gazed up and down as it examined the explosive device nearby, in a Taylor Swift lunch box tethered to the drone. The drone, the robot, the lunch box, and the explosive device were part of an exercise called Raven's Challenge.

It happens hundreds of times a day: We press snooze on the alarm clock, we pick a shirt out of the closet, we reach for a beer in the fridge. In each case, we conceive of ourselves as free agents, consciously guiding our bodies in purposeful ways. But what does science have to say about the true source of this experience? In a classic paper published almost 20 years ago, the psychologists Dan Wegner and Thalia Wheatley made a revolutionary proposal: The experience of intentionally willing an action, they suggested, is often nothing more than a post hoc causal inference that our thoughts caused some behavior. The feeling itself, however, plays no causal role in producing that behavior. This could sometimes lead us to think we made a choice when we actually didn't or think we made a different choice than we actually did.

Using its Xbox Kinect 3D sensor, a camera, a laptop and a laser pointer, Watch-Bot observed a week's worth of human activity in a kitchen and an office. During that time, it collected 458 videos -- about half of which included someone human deliberately "forgetting" to do something. The team then made Watch-Bot analyze the videos and use its unsupervised learning algorithm to determine which human actions were intentional and which ones -- like leaving the milk out on the counter -- were accidental. Using probabilistic learning models, Watch-Bot was able to independently figure out which actions the humans were forgetting. When Watch-Bot does notice the clumsy human in the room forgot to do something, it quietly highlights that item with the laser pointer until it is put away or dealt with.