Many rumours have been circulating for Apple's'Show Time' event on 25 March, chief among them a new TV service and a premium news subscription. But a new report now suggests there may also be a gaming subscription service, allowing people to pay one flat fee and get access to a variety of games on their iPhones and iPads. The service could rival Google's recently announced Stadia platform as well as being part of Apple's broader plan to make more money from streaming service subscriptions. We'll tell you what's true. You can form your own view.

Apple is about to take to the stage in California and unveil its latest product. But there will be no moment where a laptop is pulled from an envelope or a phone from a pocket. Because its latest product in one sense doesn't exist physically at all. Instead, its newest announcements will be a series of services: subscription platforms that allow people to sign up to watch TV and films, read the news, and play games. We'll tell you what's true. You can form your own view.

Apple has unveiled the "Apple Card", a fully titanium credit card that is powered by the iPhone. The new release is an extension of Apple Pay, the company said, which allows people to pay with their device by adding their card into it. But they now be able to be issued their credit card by Apple, too. Though the card is largely virtual, people getting one will also be sent a physical card. It will be made of titanium and have the owner's name etched into it, Apple said.

Yes, Apple's new video-streaming service is set to be unveiled later today -- we'll be reporting from the frontlines in San Francisco at 10am PT/1pm ET. Meanwhile, you might have missed Honda's plans for EV cycles, Overwatch changes are actually having an effect on toxic players and we put NVIDIA's RTX ray tracing to the test. Honda is having fun with electric cars, but what about the motorcycle? Don't worry, you'll get your fix soon. The automaker has unveiled prototypes for both the CR Electric dirt bike (above) and the Benly Electric delivery scooter, offering a peek at how it will approach emissions-free transportation on two wheels.

GITAI is a robotics startup with offices in Japan and the United States that's developing tech to put humanoid telepresence robots in space to take over for astronauts. Today, GITAI is announcing a joint research agreement with JAXA (the Japanese Aerospace Exploration Agency) to see what it takes for robots to be useful in orbit, with the goal of substantially reducing the amount of money spent sending food and air up to those demanding humans on the International Space Station. It's also worth noting that GITAI has some new hires, including folks from the famous (and somewhat mysterious) Japanese bipedal robot company SCHAFT. A quick reminder about SCHAFT: The company was founded by members of the JSK Laboratory at the University of Tokyo in order to build a robot to compete in the DARPA Robotics Challenge Trials in 2013. SCHAFT won the DRC Trials by a substantial margin, scoring 27 points out of a possible 32, 7 more points than the second place team (IHMC).

The success of Jordan Peele's 2017 film, "Get Out," bought him time, he said, in a recent interview with Le Monde--for his new film, "Us," he had twice as many shoot days. The expanded time frame allowed him to produce a work of expanded ambition: "Us" bounces back and forth between 1986 and the present day, and its action, compared to "Get Out," has a vast range--geographical, dramatic, and intellectual. The movie's imaginative spectrum is enormous, four-dimensionally so: it delves deep into a literal underground world that lends the hallucinatory concept of the "sunken place" from "Get Out" a physical embodiment. And it captures the transformative, radical power of a political conscience, of an idea long held in secret, as it ripens and develops over decades' worth of time. "Us" is nothing short of a colossal achievement.

Fuzzy systems have achieved great success in numerous applications. However, there are still many challenges in designing an optimal fuzzy system, e.g., how to efficiently train its parameters, how to improve its performance without adding too many parameters, how to balance the trade-off between cooperations and competitions among the rules, how to overcome the curse of dimensionality, etc. Literature has shown that by making appropriate connections between fuzzy systems and other machine learning approaches, good practices from other domains may be used to improve the fuzzy systems, and vice versa. This paper gives an overview on the functional equivalence between Takagi-Sugeno-Kang fuzzy systems and four classic machine learning approaches -- neural networks, mixture of experts, classification and regression trees, and stacking ensemble regression -- for regression problems. We also point out some promising new research directions, inspired by the functional equivalence, that could lead to solutions to the aforementioned problems. To our knowledge, this is so far the most comprehensive overview on the connections between fuzzy systems and other popular machine learning approaches, and hopefully will stimulate more hybridization between different machine learning algorithms.

The area of formal ethics is experiencing a shift from a unique or standard approach to normative reasoning, as exemplified by so-called standard deontic logic, to a variety of application-specific theories. However, the adequate handling of normative concepts such as obligation, permission, prohibition, and moral commitment is challenging, as illustrated by the notorious paradoxes of deontic logic. In this article we introduce an approach to design and evaluate theories of normative reasoning. In particular, we present a formal framework based on higher-order logic, a design methodology, and we discuss tool support. Moreover, we illustrate the approach using an example of an implementation, we demonstrate different ways of using it, and we discuss how the design of normative theories is now made accessible to non-specialist users and developers.

Explaining complex or seemingly simple machine learning models is a practical and ethical question, as well as a legal issue. Can I trust the model? Is it biased? Can I explain it to others? We want to explain individual predictions from a complex machine learning model by learning simple, interpretable explanations. Of existing work on interpreting complex models, Shapley values is the only method with a solid theoretical foundation. Kernel SHAP is a computationally efficient approximation to Shapley values in higher dimensions. Like most other existing methods, this approach assumes independent features, which may give very wrong explanations. This is the case even if a simple linear model is used for predictions. We extend the Kernel SHAP method to handle dependent features. We provide several examples of linear and non-linear models with linear and non-linear feature dependence, where our method gives more accurate approximations to the true Shapley values. We also propose a method for aggregating individual Shapley values, such that the prediction can be explained by groups of dependent variables.

Though Elon Musk has famously warned humanity about the dangers of artificial intelligence, his shareholders might be well-served by having an algorithm on Tesla's board of directors. In recent years, Tesla has become a cautionary tale for how difficult it is for part-time directors to oversee charismatic, strong-willed CEOs--especially ones who are the founding visionaries of their companies. Given how Elon Musk has landed the company in hot water with the Securities and Exchange Commission with his erratic tweets and mocking disregard for the regulatory regime dictating the proper behavior of a publicly traded company, it's little wonder that Tesla's board has been accused of being "asleep at the wheel." Perhaps their seeming unwillingness to rein him in is due to the Tesla directors' personal loyalty to Musk. Or maybe they simply don't want to spend the time to "preapprove" Musk's tweets about the company, especially with the less conventional hours and fast pace the CEO keeps.