This week, 16 teams of teens from around the world assembled in Mountain View to demonstrate the results of research projects at the Google Science Fair. You can view summaries of all the projects here. I've been attending these finals for several years now and am always impressed with how creatively the teens use the technologies of today. And this year was no exception: machine learning is hot in the tech world, and the teens are embracing it. A Silicon Valley girl from Cupertino, Calif., Cheerla was curious about the current state of breast cancer prediction, and discovered that prediction methods using digital mammograms are just 64 percent effective, typically simply considering the percentage of dense tissue in a breast.

Google has a plan to speed up robotic learning, and it involves getting robots to share their experiences and collectively improve their capabilities. Sergey Levine from the Google Brain team, along with collaborators from Alphabet subsidiaries DeepMind and X, published a blog post on Monday describing an approach for "general-purpose skill learning across multiple robots." Teaching robots how to do even the most basic tasks in real-world settings like homes and offices has vexed roboticists for decades. To tackle this challenge, the Google researchers decided to combine two recent technology advances. The first is cloud robotics, a concept that envisions robots sharing data and skills with each other through an online repository.

Artificial intelligence can do remarkable things, like recognize faces on social networks, instantly translate speech from one language to another, and identify commands barked into a smartphone. But it also can do stupid things, like label an African-American couple "gorillas." The artificial intelligence underpinning Google Photos did just that last year. The platform uses deep neural networks to identify images in your photo collection. These networks of hardware and software, modeled after the network of neurons in your brain, learn to recognize objects, animals, and faces by analyzing many millions of pre-labeled photos.

Line is ramping up incentives for third party developers to make chatbots, and thereby expand the utility of its platform, as the great scramble of messaging giants applying AI to keep users engaged continues. The South Korean company behind the platform announced its plans to open up to chatbots back in March, going on to allow the first developers to start building bots in April, on a first come first serve basis, with an initial limit of 10,000 bots. It's now taken the stabilizer wheels off its chatbot developer initiative, launching a new Messaging API at a conference in Japan which it's touting as simper than the prior bot API. The mobile messaging giant, which competes with the likes of WhatsApp and WeChat and has 218 million monthly active users, is most popular in Asia, with two-thirds of Line users in Japan, Taiwan, Thailand and Indonesia. "These new message types sent by chatbots allow companies to realise a smoother Line-based contact point with users for their services and content," it said in a press release.

The way that we understand one another has been finely tuned over millions of years, to the point where it's hard to believe anything could outperform humans when it comes to understanding humans. I'm convinced though, that within the next five to ten years, that belief will gradually disappear, as machines get better and better at making sense of our emotions. This is the field of affective computing, or what I affectionately call, artificial emotional intelligence. The first signs of the shift to more emotionally intelligent software are already starting to appear on the market, and I'll touch on them in a moment. But first, I want to disclose a strong conviction: I believe emotional intelligence is absolutely essential to artificial intelligence.

Dean Pomerleau can still remember his first tussle with the black-box problem. The year was 1991, and he was making a pioneering attempt to do something that has now become commonplace in autonomous-vehicle research: teach a computer how to drive. This meant taking the wheel of a specially equipped Humvee military vehicle and guiding it through city streets, says Pomerleau, who was then a robotics graduate student at Carnegie Mellon University in Pittsburgh, Pennsylvania. With him in the Humvee was a computer that he had programmed to peer through a camera, interpret what was happening out on the road and memorize every move that he made in response. Eventually, Pomerleau hoped, the machine would make enough associations to steer on its own.

In future, machine learning could improve transport, security, healthcare and revolutionise industry. But despite its reach, this powerful technology remains mysterious to most. Our panel of speakers, chaired by Marcus du Sautoy, discussed what we mean by machine learning and discovered some of the exciting current and future uses of this technology. We had presentations from the Head of Microsoft Research Chris Bishop, robotics researcher Sabine Hauert and machine vision researcher Maja Pantic. Visitors were also be able to take part in an interactive exhibition where machine learning developers and researchers showcased examples of the technology in action.

"With deep learning in particular, you process all that data and you look for not the 100 percent conclusion, but you look for the 51 percent, the 60 percent opportunities that could send you in a new direction. And I think that's why it's so big," Cuban said. And while he thinks there is enough room for all of the major tech players like Facebook, Alphabet and Amazon to co-exist, the tough thing is that no one knows what the next big thing in technology is. Right now, executives are simply using their intuition to make moves in the space. "I could argue for precision medicine. I could argue that's why they are all acquiring artificial intelligence, deep learning companies, right, because they don't know where it will take them," Cuban said.

There is no question that artificial intelligence (A.I.) is in the process of radically transforming the workplace. As a 15 billion industry expected to grow to more than 70 billion by 2020, it's clear that in the future, A.I. will change almost everything in the way that we live and work. And the future is here already; employees in tech-savvy organizations are actively engaged with bots as part of their everyday jobs. Studies show that up to 45 percent of professional activities can be automated, including the work of the highest-paid occupations such as managers, senior executives, and even CEOs. Rather than fear it, today's top innovative companies are embracing the potential of automation to reduce routine and mundane work and allow employees to focus on higher level tasks that require unstructured creativity and emotional empathy.

But the condition is so rare that it's still far from fully understood. Though it's undeniably horrific for those experiencing it, Cotard's Syndrome presents a fascinating conundrum for those studying the disorder. The condition's central contradiction -- how can someone articulate the thought that they don't exist? A 2013 case study of a Cotard's sufferer showed low activity in the brain network associated with awareness of the body. It's only one example (as with much of Cotard's Syndrome research, because the condition is so rare), but unpacking how the brains of those with the syndrome work offers hints as to how normally-functioning brains develop a sense of existence.