Google has long been the king of search, building its empire by answering questions, giving directions and helping people settle arguments with its vast database of facts. But as more people search using their voice -- verbally asking questions to Apple's Siri or Amazon Echo's Alexa -- instead of typing queries into their phones or laptops, the tech giant risks ceding some of that dominance. To future-proof itself from competitors, the Mountain View, Calif., tech firm announced Tuesday a new slate of electronic devices that support voice search and come pre-installed with Google Assistant, the company's artificial intelligence bot that can do a wide variety of things, including answering queries and making restaurant reservations. "[Google Assistant] will be a two-way conversation, a natural dialogue between our users and Google," said Sundar Pichai, Google's chief executive. "It will be universal, available when users need it to help them. Our goal is to build a personal Google for each and every user."

In his experiment, Queen Mary University of London researcher Sylvian Alem attached a fake flower sprinkled with sugar water to the end of a string, placed it under a transparent sheet of glass and then put a group of bumblebees to a test. If he trained them to pull the string to get the sugar water, would they be able to learn it – and spread the skill to their colony? When Lars Chittka, a professor at the university, saw the experiment, he was surprised. "What I like about the work," Dr. Chittka said in a press release, "in addition to the experimental and intellectual challenges and insights, is the sheer absurdity of seeing bees solving a string-pulling puzzle. When lead author Sylvain Alem first showed me a bee successfully pulling on the string, I just couldn't believe what I was seeing. And even now, looking at the videos still makes me laugh."

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.