If you get an invite to Stanley Kubrick's house, never refuse. I was fortunate enough to be invited by the University of Arts and the Kubrick family to an intimate evening in Harpenden to celebrate the launch of Thames & Hudson's book Artificial Intelligence: The Vision Behind The Film. I went to take a picture but was swiftly reprimanded. Oh, the irony of being in the house of one of cinema's greatest directors but unable to take a photo -- Kubrick may be dead, but the air of secrecy still lingers thick. I made my way down a grand, glass-floored corridor and entered his red walled library, packed full of medical tomes, history books, sci fi novels and a smattering of awards.

The following abbreviated edit is based on an original article by Ronald van Loon whch can be found here. It is based on a conversation with John Mellor from Adobe. Customer behavior and experience changes over time and there is a lot organizations can do to use that change to their own advantage. John Mellor, who holds a strategic position at Adobe, is of the view that customer experience is ever changing. The challenge this presents for businesses is to place their customer right at the center of their organizational chart.

According to Nathan Mondragon, finding the right employee is all about looking at the little things. Tens of thousands of little things, as it turns out. Mondragon is the head psychologist at Hirevue, a company that offers software that screens job candidates using algorithms and artificial intelligence (AI). Hirevue's flagship product, used by global giants such as Unilever and Goldman Sachs, asks candidates to answer standard interview questions in front of a camera. Meanwhile its software, like a team of hawk-eyed psychologists hiding behind a mirror, makes note of thousands of barely perceptible changes in posture, facial expression, vocal tone and word choice.

In this episode, Audrow Nash speaks with Monica Daley about learning from birds about legged locomotion. To do this, Daley analyzes the gaits of guineafowl in various experiments to understand the mechanical principles underlying gaits, such as energetic economy, mechanical limits, and how the birds avoid injury. She then tests her ideas about legged locomotion on legged robots with collaborators, including Jonathan Hurst from Oregon State University. Daley also speaks about her experience with interdisciplinary collaborations. Monica Daley earned an HBSc in Biology with a Chemistry minor at the University of Utah, where she was inspired to pursue an academic career through her research on human locomotor-ventilatory integration with Dennis Bramble and David Carrier.

All countries will feel the impact of automation, but at different speeds and in different ways. In this podcast, McKinsey Global Institute looks at its likely impact in China, Europe, and India. New technologies such as artificial intelligence and automation are reshaping the workplace globally. All countries will feel the impact in some way, shape, or form. In this episode for the McKinsey Global Institute's New World of Work podcast, MGI directors Jonathan Woetzel and Jacques Bughin and MGI partner Anu Madgavkar examine automation's likely impact in China, Europe, and India. I'm Peter Gumbel from the McKinsey Global Institute, and today we'll be taking a look at the quite different ways that new technologies like automation and artificial intelligence will affect work in different parts of the world. Specifically, we'll be looking at China, Europe, and India. These differences come about for a number of reasons that we explain in our new MGI report on the future of work, which is called Jobs lost, jobs gained: Workforce transitions in a time of automation. Among the reasons for these differences are different levels of economic development, different wage rates, and different potential for automation adoption in different economies. First, let's talk about China. Here to do so is Jonathan Woetzel, director of the McKinsey Global Institute, based in Shanghai. Jonathan, perhaps you can start by telling us where the Chinese workforce is at the moment.

Let's talk about information – what is it? What are the limits on the communication of information? We'll try to answer these questions by looking at the revolutionary work of one man – Claude E. Shannon, and the men and women who followed in his footsteps. Most importantly, we'll try to answer the question'is this important for machine learning and why?'. I hope that by the time you finish reading this post you too will be convinced that information theory can be very beneficial to anybody who's interested in developing machine learning systems. Let's start by discussing the general way the word "information" was understood before Shannon's work in 1948. Then we will be ready to understand the revolutionary nature of this work.

See the GTC session schedule. Major sponsors include Facebook, IBM, Cisco, Dell EMC, Google Cloud, Hewlett Packard Enterprise, Inspur, Lenovo and Supermicro. Show attendees can vote for the world's top AI startups at NVIDIA's Inception Awards Finale on Tuesday, March 27, from 4:30-5:30 pm Pacific time. NVIDIA Deep Learning Institute certified instructors will deliver more than 100 hours of training to thousands of data scientists, using the latest AI frameworks and software development kits. Two Global Impact Award winners will receive $200,000 in prizes for their pioneering work addressing important social and humanitarian problems using GPU computing.

Get out of the way, Tinder. There's a new dating app on the scene hoping to disrupt the way we find that one special person to eventually melt down in an IKEA with over the relative merits of a Kvikne wardrobe versus a Trysil. Described by Wired as "a sort of 23andMe meets Tinder meets monogamists," Pheramor's secret to success is your DNA. That's right--for a low-rate of $19.99 plus a $10 monthly membership fee, the Houston-based online dating startup promises to match local singles who are compatible not only socially but genetically. The app's matching algorithm analyzes 11 special "attraction genes" supposedly linked to our pheromones--olfactory signals that some believe trigger attraction.

KenSci, a company that has developed a machine learning risk prediction platform for healthcare, recently presented a paper on predicting end-of-life mortality and improving care. The paper, which tackles a tricky topic with predictions for the last six to 12 months of life for patients, was accepted by the Association for the Advancement of Artificial Intelligence. At stake is $205 billion in cost spent on care for the last year of an individual's life. As part of our ongoing series on data scientists and their approaches, we caught up with Ankur Teredesai, CTO of KenSci and one of the authors of the paper, which was recognized in the emerging technologies category. What data sets did you use to model?

Owura Kwadwo Hottish illustrates a window of Microsoft Word using colored chalk on a blackboard. He uses it to teach computer skills to students at the Betenase M/A Junior High School in Kumasi, Ghana. Owura Kwadwo Hottish illustrates a window of Microsoft Word using colored chalk on a blackboard. He uses it to teach computer skills to students at the Betenase M/A Junior High School in Kumasi, Ghana. Could you teach computer class without a computer?