One day in 1992, Lawrence Mattis opened up his mail to find an unsolicited screenplay from two unknown writers. It was a dark, nasty, almost defiantly uncommercial tale of cannibalism and class warfare--the type of story that few execs in Hollywood would want to tell. Yet it was exactly the kind of movie Mattis was looking for. Only a few years earlier, Mattis, in his late twenties, had abandoned a promising legal career to start a talent company, Circle of Confusion, with the aim of discovering new writers to represent. He'd set up shop in New York City, despite being told repeatedly that his best hope for finding talent was to be in Los Angeles. Before that strange script showed up, Mattis was starting to wonder if those naysayers had been right. "I'd only sold a few options that paid about five hundred dollars each," Mattis says. "I was starting to think about going back to law. Then I get this letter from these two kids, saying'Could you please read our script?'" The screenplay, titled Carnivore, was a horror tale set in a soup kitchen, where the bodies of the rich are used to feed the poor. "It was funny, it was visceral, and it made it clear that whoever wrote it really knew movies," Mattis says. Its writers were Lilly and Lana Wachowski, two self-described "schmoes from Chicago" who, in later years, would be referred to by many colleagues and admirers simply as "the Wachowskis." By the time they contacted Mattis, the Wachowskis had been collaborating for years, having spent their childhood creating radio plays, comic books, and their own role-playing game. They'd been raised in a middle-class neighborhood on Chicago's South Side by their mother, a nurse and artist, and their father, a businessman. Growing up, their parents had encouraged them to discover art, especially film.

BEIJING - People.cn, the online unit of China's influential People's Daily, is boosting its numbers of human internet censors backed by artificial intelligence to help firms vet content on apps and adverts, capitalizing on its unmatched Communist Party lineage. Demand for online censoring services provided by the Shanghai-listed People.cn has soared since last year after China tightened its already strict online censorship rules. As a unit of the People's Daily -- the ruling Communist Party's mouthpiece -- it is seen by clients as the go-to online censor. Investors concur, lifting shares in People.cn "The biggest advantage of People.cn is its precise grasp of policy trends," said An Fushuang, an independent analyst based in Shenzhen.

Spotify has launched a new offer that finally allows you to share your subscription with the most important person in your life. The feature – named "Premium Duo" – allows people to buy a cheaper subscription between two people, letting them sign up with their partner or someone else important in their life. As well as offering a way of getting a Spotify subscription more cheaply, the new deal gives people extra features that aren't usually available on the service. We'll tell you what's true. You can form your own view.

While many methods for interpreting machine learning models have been proposed, they are frequently ad hoc, difficult to evaluate, and come with no statistical guarantees on the error rate. This is especially problematic in scientific domains, where interpretations must be accurate and reliable. In this paper, we cast black box model interpretation as a hypothesis testing problem. The task is to discover "important" features by testing whether the model prediction is significantly different from what would be expected if the features were replaced with randomly-sampled counterfactuals. We derive a multiple hypothesis testing framework for finding important features that enables control over the false discovery rate. We propose two testing methods, as well as analogs of one-sided and two-sided tests. In simulation, the methods have high power and compare favorably against existing interpretability methods. When applied to vision and language models, the framework selects features that intuitively explain model predictions.

Media reports are attempting to suggest that the strange "Momo" character has returned, despite it not actually existing. A flurry of reports over the last day or so have focused on suggestions that the character is appearing in "baby shark" videos in an attempt to terrify the children watching them, and convince them to engage in dangerous behaviour. While it may be true that there the image has appeared in one or some of those videos, they have probably been watched by very few people. What's more, the character does not actually exist – the model that the infamous picture shows was destroyed after it rotted – which means the stories actually only refer to somebody inserting the horrifying image into videos rather than anything supernatural. We'll tell you what's true.

This audio conversation digs into research on the emerging job categories spurred by AI and what leaders should be thinking about next.

Recent, extremely successful methods in deep learning, such as convolutional neural networks (CNNs) have originated in machine learning for images. When applied to music signals and related music information retrieval (MIR) problems, researchers often apply standard FFT-based signal processing methods in order to create an image from the raw audio data. The impact of this basic signal processing step on the final outcome of the MIR task has not been widely studied and is not well understood. In this contribution, we study Gabor Scattering and a new representation, namely Mel Scattering. Furthermore, we suggest an alternative enhancement of the loss function that uses transformed representations of the output data to incorporate additional available information. We show how applying various different signal analysis methods can lead to useful invariances and improve the overall performance in MIR problems by reducing the amount of necessary training data or the necessity of augmentation.

Apple unveiled a host of new subscription services at a star-studded event in Cupertino, California, on Monday morning. The event marked the debut of a new era for a company that built its brand on hardware and software; just last week, Apple announced new products with little fanfare, saving its firepower for Monday's celebration of services, from its attempt to take on Netflix to a new Apple credit card. Steven Spielberg, Reese Witherspoon, Jennifer Aniston, Steve Carrell, Kumail Nanjiani, and Big Bird were on hand to promote new creative projects that will be released through Apple's new subscription television service, Apple TV . Spielberg's Amazing Stories will resurrect the 93-year-old brand of a science fiction magazine that inspired the director as a child. Witherspoon and Aniston announced The Morning Show, described by Aniston as "an honest look at the complex relationship between women and men in the workplace".

Apple has unveiled a complete update to its news offering, known as News, which allows people to pay to subscribe to magazines as well as newspapers. The company suggested that the new service is the best way of reading magazines online, as well as offering a way for news organisations to sell premium subscriptions. People will pay just $9.99 per month and get access to all of the magazines and news organisations available through the app. Subscribing to the various outlets included in the service would cost over $8,000 per month, it said. We'll tell you what's true.