While at the park, the service members had the chance to explore attractions and participate in Disneyland's daily flag ceremony. Disney's Imagineers are working on a new animatronic of iconic American visionary Walt Disney, but some members of his family have opposing views about whether it celebrates his legacy or dehumanizes him. Disney's Main Street Opera House plans to unveil a new theme park attraction called Walt Disney – A Magical Life, featuring an audio-animatronic of the company's founder. But Joanna Miller, one of Disney's grandchildren, slammed the idea of an animatronic as "dehumanizing" in a viral Facebook post. Among her claims, she suggested that her grandfather had told early Imagineer Sam McKim he never wanted to be commemorated with an animatronic.

Automated Machine Learning (AutoML) is an upcoming field in machine learning (ML) that searches the candidate model space for a given task, dataset and an evaluation metric and returns the best performing model on the supplied dataset as per the given metric. AutoML not only reduces the manpower and expertise needed to develop ML models but also decreases the time-to-market for ML models substantially. We have designed an enterprise-scale AutoML framework called WALTS to meet the rising demand of employing ML in retail or any other business of interest, and thus help democratize ML within our organization. In this blog, we elaborate on how we explore models from a pool of candidates and underline how it has helped us with a business use-case. To give an overview of the AutoML process, its current landscape, and showcase the benefits of WALTS, we will be covering: · What is AutoML?

Cyber criminals are constantly seeking new ways to perpetrate a breach but thanks to artificial intelligence (AI) and its subset machine learning, it's becoming possible to fight off these attacks automatically. The secret is in machine learning's ability to monitor network traffic and learn what's normal within a system, using this information to flag up any suspicious activity. As the technology's name suggests, it's able to use the vast amounts of security data collected by businesses every day to become more effective over time. At the moment, when the machine spots an anomaly, it sends an alert to a human – usually a security analyst – to decide if an action needs to be taken. But some machine learning systems are already able to respond themselves, by restricting access for certain users, for example.

Cyber criminals are constantly seeking new ways to perpetrate a breach but thanks to artificial intelligence (AI) and its subset machine learning, it's becoming possible to fight off these attacks automatically. The secret is in machine learning's ability to monitor network traffic and learn what's normal within a system, using this information to flag up any suspicious activity. As the technology's name suggests, it's able to use the vast amounts of security data collected by businesses every day to become more effective over time. At the moment, when the machine spots an anomaly, it sends an alert to a human – usually a security analyst – to decide if an action needs to be taken. But some machine learning systems are already able to respond themselves, by restricting access for certain users, for example.

Building an experimental city from scratch was always going to be a complex, expensive, problematic task--even Walt would have struggled to pull this one off. But the Walt Disney Co. seems to have abandoned all efforts at serious futurology. Since the abandonment of the "2055" project, the original Tomorrowland--home of Walt's starry-eyed vision of space, atoms, and transit--has displayed little of Walt's futuristic spirit. The park's current "Googie" architecture is a relic from the past, "a space age look that was enormously popular during the 1950s," according to the Disney website. The rides are a mixture of vintage classics (Autopia, the monorail, and the submarine still run) and Pixar/Star Wars–themed attractions that are more science-fiction than science-future.

Apple had a significant head start over Google when buying Siri, but Apple has fallen way behind in the AI race as far as usability. Walt's column this week argues that Siri seems dumb, and might be a major problem for Apple in the coming tech war. This week on Ctrl-Walt-Delete, Walt and Nilay discuss Apple's artificial intelligence strategy and how it's just not up to speed with what customers are doing today. We love your feedback on the topics of the show and suggestions on how to make our show better and more fun -- you can tweet at Walt at @waltmossberg and Nilay at @reckless. And of course, we'd love it if you subscribed in iTunes (here's the direct RSS feed, if you like), along with The Verge's other great podcasts like What's Tech, and The Vergecast.

Google employees, squeezed onto metal risers and standing in the back of a meeting room, erupted in cheers as newly arrived executive Andrew Conrad announced they would try to turn science fiction into reality: The tech giant had formed a biotech venture to create a futuristic device like Star Trek's iconic "Tricorder" diagnostic wizard -- and use it to cure cancer. Conrad, recalled an employee who was present, displayed images on the room's big screens showing nanoparticles tracking down cancer cells in the bloodstream and flashing signals to a Fitbit-style wristband. He promised a working prototype of the cancer early-detection device within six months. That was three years ago. Recently departed employees said the prototype didn't work as hoped, and the Tricorder project is floundering. Tricorder is not the only misfire for Google's ambitious and extravagantly funded biotech venture, now named Verily Life Sciences. It has announced three signature projects meant to transform medicine, and a STAT examination found that all of them are plagued by serious, if not fatal, scientific shortcomings, even as Verily has vigorously promoted their promise.

Google employees, squeezed onto metal risers and standing in the back of a meeting room, erupted in cheers as newly arrived executive Andrew Conrad announced they would try to turn science fiction into reality: The tech giant had formed a biotech venture to create a futuristic device like Star Trek's iconic "Tricorder" diagnostic wizard -- and use it to cure cancer. Conrad, recalled an employee who was present, displayed images on the room's big screens showing nanoparticles tracking down cancer cells in the bloodstream and flashing signals to a Fitbit-style wristband. He promised a working prototype of the cancer early-detection device within six months. That was three years ago. Recently departed employees said the prototype didn't work as hoped, and the Tricorder project is floundering. Tricorder is not the only misfire for Google's ambitious and extravagantly funded biotech venture, now named Verily Life Sciences. It has announced three signature projects meant to transform medicine, and a STAT examination found that all of them are plagued by serious, if not fatal, scientific shortcomings, even as Verily has vigorously promoted their promise.

Two years ago, a lawyer in Indiana sent me a check for seventy-eight thousand dollars. The money was from my uncle Walt, who had died six months earlier. I hadn't been expecting any money from Walt, still less counting on it. So I thought I should earmark my inheritance for something special, to honor Walt's memory. It happened that my longtime girlfriend, a native Californian, had promised to join me on a big vacation. She'd been feeling grateful to me for understanding why she had to return full time to Santa Cruz and look after her mother, who was ninety-four and losing her short-term memory. She'd said to me, impulsively, "I will take a trip with you anywhere in the world you've always wanted to go." To this I'd replied, for reasons I'm at a loss to reconstruct, "Antarctica?" Her eyes widened in a way that I should have paid closer attention to. But a promise was a promise. Hoping to make Antarctica more palatable to my temperate Californian, I decided to spend Walt's money on the most deluxe of bookings--a three-week Lindblad National Geographic expedition to Antarctica, South Georgia island, and the Falklands. I paid a deposit, and the Californian and I proceeded to joke, uneasily, when the topic arose, about the nasty cold weather and the heaving South Polar seas to which she'd consented to subject herself. I kept reassuring her that as soon as she saw a penguin she'd be happy she'd made the trip. But when it came time to pay the balance, she asked if we might postpone by a year. Her mother's situation was unstable, and she was loath to put herself so irretrievably far from home. By this point, I, too, had developed a vague aversion to the trip, an inability to recall why I'd proposed Antarctica in the first place. The idea of "seeing it before it melts" was dismal and self-cancelling: why not just wait for it to melt and cross itself off the list of travel destinations? I was also put off by the seventh continent's status as a trophy, too remote and expensive for the common tourist to set foot on. It was true that there were extraordinary birds to be seen, not just penguins but oddities like the snowy sheathbill and the world's southernmost-breeding songbird, the South Georgia pipit. But the number of Antarctic species is fairly small, and I'd already reconciled myself to never seeing every bird species in the world. The best reason I could think of for going to Antarctica was that it was absolutely not the kind of thing the Californian and I did; we'd learned that our ideal getaway lasts three days.

The rich expressivity provided by the cognitive event calculus (CEC) knowledge representation framework allows for reasoning over deeply nested beliefs, desires, intentions, and so on. I put CEC to the test by attempting to model the complex reasoning and deceptive planning used in an episode of the popular television show Breaking Bad. CEC is used to represent the knowledge used by reasoners coming up with plans like the ones devised by the fictional characters I describe. However, it becomes clear that a form of nonmonotonic reasoning is necessary—specifically so that an agent can reason about the nonmonotonic beliefs of another agent. I show how CEC can be augmented to have this ability, and then provide examples detailing how my proposed augmentation enables much of the reasoning used by agents such as the Breaking Bad characters. I close by discussing what sort of reasoning tool would be necessary to implement such nonmonotonic reasoning.