PewDiePie has conceded his hard-fought battle with T-Series with a music video of his own. The YouTube star whose real name is Felix Kjellberg has finally stopped being the site's biggest channel – a slot he held for years – after a challenge from T-Series, which hosts Indian music videos and has grown quickly. Over recent months, the two channels have fought to get control of the top spot, with PewDiePie's fans using a whole host of publicity techniques in an attempt to keep him as its most popular channel. We'll tell you what's true. You can form your own view. But T-Series kept outpacing its growth and has now settled as YouTube's biggest channel.

Potential driverless cars will be making their way around London as part of a test of a service for autonomous vehicles. The cars – with safety drivers ready to take over, for now – will make their way around two London boroughs to see how the scheme run by a British artificial intelligence company could work. Autonomous technology firm FiveAI has started testing five self-driving cars in Bromley and Croydon as the latest step in its plans to eventually roll out an autonomous, car-sharing service in London. We'll tell you what's true. You can form your own view.

The history of AI is often told as the story of machines getting smarter over time. What's lost is the human element in the narrative, how intelligent machines are designed, trained, and powered by human minds and bodies. In this six-part series, we explore that human history of AI--how innovators, thinkers, workers, and sometimes hucksters have created algorithms that can replicate human thought and behavior (or at least appear to). While it can be exciting to be swept up by the idea of super-intelligent computers that have no need for human input, the true history of smart machines shows that our AI is only as good as we are. In 1950, at the dawn of the digital age, Alan Turing published what was to be become his most well-known article, "Computing Machinery and Intelligence," in which he poses the question, "Can machines think?"

Businesses and nonprofits are finding novel ways to employ artificial intelligence in the developing world, using the tools to improve agriculture yields, infant health care, and entrepreneur earnings, according to speakers at MIT Technology Review's EmTech Digital conference in San Francisco on Tuesday. Solomon Assefa, who oversees IBM's research labs in Kenya and South Africa, said the company has been using AI to more accurately predict crop yields in specific regions, based on shifting weather patterns, soil moisture, and other conditions. This insight into growing conditions has helped local farmers raise financing to expand their operations, or make better decisions about the right seeds, appropriate fertilizer, and ideal times to plant and harvest. Separately, the tech giant's research lab has partnered with a startup, Hello Tractor, that links farmers in need of tractors with owners looking to lease equipment. By forecasting demand for the vehicles, IBM has also helped owners raise money to expand their fleet, boosting their profits, Assefa said.

This is the pilot of a series of EdTech articles with the focus on AR and AI. This article covers AR and AI from a birds-eye view. We will dive deeper into the specific application areas in the upcoming articles. Welcome to 21st century learning! Gone are the days when you missed a class in your school, it was difficult to catch up with the current lessons.

In one of Aesop's fables, a thirsty crow finds a pitcher with a small amount of water beyond the reach of its beak. After failing to push the pitcher over, the crow drops pebbles in one by one until the water level rises, allowing the bird to have a drink. For Aesop, the fable showed the superiority of intelligence over brute strength. Two and a half millennia later, we might get to see whether AI could pass Aesop's ancient intelligence test. In June, researchers will train algorithms to master a suite of tasks that have traditionally been used to test animal cognition.

Southwest Airlines was among the airlines affected by a contractor's computer outage Monday, forcing hundreds of flights to be delayed. Southwest Airlines was among the airlines affected by a contractor's computer outage Monday, forcing hundreds of flights to be delayed. Thousands of travelers on some of the largest U.S. airlines endured long waits Monday morning, as their flights were delayed owing to a contractor's computer outage. The technical problem forced Southwest Airlines, which had some of the biggest issues, to shut down all U.S. flights for about 40 minutes Monday. Eastern Time, more than 600 Southwest flights today had been delayed," NPR's David Schaper reports.

Money laundering accounts for up to 5% of global GDP - or $2tn (£1.5tn) - every year, says the United Nations Office on Drugs and Crime. So banks and law enforcement agencies are turning to artificial intelligence (AI) to help combat the growing problem. Money laundering, so-called after gangster Al Capone's practice of hiding criminal proceeds in cash-only laundromats in the 1920s, is a huge and growing problem. "Dirty" money is "cleaned" by passing it through layers of seemingly legitimate banks and businesses and using it to buy properties, businesses, expensive cars, works of art - anything that can be sold on for new cash. And one of the ways criminals do this is called "smurfing".

The countdown has begun to the launch of one of the world's boldest attempts to tackle air pollution. From next Monday, thousands of drivers face paying a new charge to enter central London. The aim is to deter the dirtiest vehicles in an effort to reduce diseases and premature deaths. The initiative comes as scientists say the impacts of air pollution are more serious than previously thought. The mayor of London, Sadiq Khan, told the BBC that the threat of air pollution, which is mostly invisible to the naked eye, is "a public health emergency".

Most artificial networks today rely on dense representations, whereas biological networks rely on sparse representations. In this paper we show how sparse representations can be more robust to noise and interference, as long as the underlying dimensionality is sufficiently high. A key intuition that we develop is that the ratio of the operable volume around a sparse vector divided by the volume of the representational space decreases exponentially with dimensionality. We then analyze computationally efficient sparse networks containing both sparse weights and activations. Simulations on MNIST and the Google Speech Command Dataset show that such networks demonstrate significantly improved robustness and stability compared to dense networks, while maintaining competitive accuracy. We discuss the potential benefits of sparsity on accuracy, noise robustness, hyperparameter tuning, learning speed, computational efficiency, and power requirements.