The robo-chef can learn how to create the perfect dish, simply from watching cooking videos. Researchers at the University of Cambridge have programmed a machine to make a meal by following how a human makes it. Using sophisticated AI, the robot is able to work out from every frame which objects it is looking at – such as a vegetable, hand, or knife – and how it is being used. Over time, it is then able to identify which ingredients work best together – and even point out when the human chef may have used the wrong amount. Robotic chefs have been featured in science fiction for decades, but in reality, cooking is a challenging problem for a robot.

Some say allowing artificial intelligence (AI) to determine guilt or innocence in a courtroom is a step too far. But for those who are sceptical about the neutrality of human judgment, or have witnessed an unfair justice system in action, AI and legal robots could be the answer to providing a fair and impartial jury. We already automate so much else in society, so why not extend this smart automation to juries? After all, lawyers rely on technology to scan documents for keywords or evaluate collected data. And people can now use legal chatbots to determine if it's worthwhile to pursue their case in court.

If you share your home with a dog or a cat, look at it carefully and you will get a good overview of everything we don't know how to do in artificial intelligence. "But my cat does nothing all day except sleep, eat and wash herself," you may think. And yet your cat knows how to walk, run, jump and land on her feet, hear, see, watch, learn, play, hide, be happy, be sad, be afraid, dream, hunt, eat, fight, flee, reproduce, educate her kittens – and the list is still very long. Each of these actions requires processes that are not directly intelligence in the most common sense but are related to cognition and animal intelligence. All animals have their own cognition, from the spider that weaves its web to the guide dogs that help people find their way.

HMD Global, the home of Nokia phones, has announced the new Nokia 2.2, delivering sophisticated AI powered low light imaging and Google Assistant at the press of a button, all at a truly astonishing price. Nokia 2.2 is the first 2 series Nokia smartphone to be part of the Android One programme, delivering the latest full Android experience on a modern 5.7" screen with a discreet selfie-notch. Shipping with Android 9 Pie, Nokia 2.2 is Android Q ready and will receive two years of OS upgrades and three years of monthly security updates, ensuring access to all the latest innovations from Android. Sanmeet Singh Kochhar, General Manager - Middle East, HMD Global, said: "We continue to offer secure and innovative smartphone experience that keeps getting better with two years of OS updates and three years of monthly security updates. And today, we've brought the pinnacle of AI experiences to more people than ever before with the Nokia 2.2, which joins our Android One family.

Reinforcement learning is an increasingly popular machine learning technique that is particularly well suited for addressing problems within dynamic and adaptive environments. When paired with simulations, reinforcement learning is a powerful tool for training AI models that can help increase automation or optimize operational efficiency of sophisticated systems such as robotics, manufacturing, and supply chain logistics. However, moving from the games commonly used to demonstrate these techniques into real-world applications isn't always straightforward. Structuring solutions to move beyond purely data-driven training introduces all sorts of new complexity, requiring you to consider things like how to use simulations to target your learning objectives, what kinds of simulations are applicable, how to deal with long-running simulations, how to incorporate ongoing training refinement once deployed, how to account for scaling and performance, and ultimately how to bridge from simulation to the real world. I was recently able to talk about how to effectively leverage reinforcement learning in real-world use cases at the O'Reilly AI conference in San Francisco.

Gaming has a lot in common with everyone's favourite heiress, at least in the public consciousness: it's pretty, but dumb. And now that Microsoft, Nintendo and Sony have released their latest games consoles, that statement becomes all the more pertinent - next-gen games look great, but they play like something that could have been made a decade ago. While visual fidelity has advanced exponentially over time, the technology that governs how games play, react and adapt - the artificial intelligence, or AI - remains relatively rudimentary. A handful of developers are striving to change this. The British designer Peter Molyneux, recently awarded an OBE, has spent his career trying to inject sentience and reactivity into games - and with his upcoming title, Fable 2, he thinks he's made significant progress.

Some things in life cannot be offset by a mere net gain in intelligence. The last few years have seen the widespread recognition that sophisticated AI is under development. Bill Gates, Stephen Hawking, and others warn of the rise of "superintelligent" machines: AIs that outthink the smartest humans in every domain, including common sense reasoning and social skills. Superintelligence could destroy us, they caution. In contrast, Ray Kurzweil, a Google director of engineering, depicts a technological utopia bringing about the end of disease, poverty and resource scarcity.

Some things in life cannot be offset by a mere net gain in intelligence. The last few years have seen the widespread recognition that sophisticated AI is under development. Bill Gates, Stephen Hawking, and others warn of the rise of "superintelligent" machines: AIs that outthink the smartest humans in every domain, including common sense reasoning and social skills. Superintelligence could destroy us, they caution. In contrast, Ray Kurzweil, Google's chief engineer, depicts a technological utopia bringing about the end of disease, poverty and resource scarcity.