By tradition, reporters work through those reports each quarter to extract financial data for business news articles. There are two fundamental limitations of this approach. First, there are currently more than 4,000 publicly traded companies in the U.S. and almost 40,000 around the world. With its existing resources, even an organization as big as the Associated Press (AP) could only produce about 300 such stories in a timely fashion per quarter – and that left thousands of potential company earnings stories unwritten. Second, even though these stories are relatively easy to write, they involve a lot of repetitive work copying and pasting numbers – something even trained reporters find difficult to do with perfect accuracy.

Whether it's guessing which songs you want to listen to or which ads you should be shown, modern AI is increasingly focused on predicting the future. But there's an enormous gulf between those kind of applications and looking at a scene and guessing what will happen next. That's what researchers at the Massachusetts Institute of Technology have done, with a new paper revealing not just their ability to look at still images and guess what will happen next -- something we've covered in the past -- but to actually generate video of it. "What we're interested in is teaching machines what can happen in a particular setting," Carl Vondrick, a Ph.D. student in computer science, told Digital Trends. "For example, we wanted a machine to recognize what happens on a beach. We want it to know that waves are going to crash, people are going to play in the water -- these are all things it's very difficult to teach a machine. The reason is that it would be very time-consuming for a person to sit down and write rules to explain everything that can happen in any given scenario. What we wanted to do was to teach them from watching massive amounts of video instead."

Alicia Vikander in "Ex Machina," a sci-fi thriller about an eccentric inventor who designs artificial intelligence. Artificial intelligence (AI) is all about getting a machine to mimic a human in every way: thought, speech, movement. That's why one of the tests for AI is the Turing test: whether a robot can fool a human into thinking it is conversing with another of its own species. An integral part of accomplishing this is making the AI recognize human emotions. So one research lab is working on the next iteration of virtual assistants, those that can recognize and react to emotional cues.

Tyrell Wellick (Martin Wallstrom) made a few appearances in "Mr. Robot" Season 2, and they say nothing about what exactly his participation in the 9/5 hack and Elliot's (Rami Malek) grand plan. He finally appeared in episode 11, much to Elliot's shock. The fsociety founder was already clinging on to the idea that he killed Tyrell, so seeing him has made him question whether he was just making the image of the former E Corp executive up. Whether the Tyrell Elliot saw was real or not has yet to be determined, but Wallstrom is confident that his character's return -- as an illusion or the real thing -- will bring "something exciting."

Slate and Future Tense are discussing Mr. Robot and the technological world it portrays throughout the show's second season. You can follow this conversation on Future Tense, and Slate Plus members can also listen to Hacking Mr. Robot, a members-only podcast series featuring Lily Newman and Fred Kaplan. In this episode of Hacking Mr. Robot, Fred and Lily discuss Episode 11. Fred Kaplan is the author of Dark Territory: The Secret History of Cyber War.

Large volume of networked streaming event data are becoming increasingly available in a wide variety of applications, such as social network analysis, Internet traffic monitoring and healthcare analytics. Streaming event data are discrete observation occurred in continuous time, and the precise time interval between two events carries a great deal of information about the dynamics of the underlying systems. How to promptly detect changes in these dynamic systems using these streaming event data? In this paper, we propose a novel change-point detection framework for multi-dimensional event data over networks. We cast the problem into sequential hypothesis test, and derive the likelihood ratios for point processes, which are computed efficiently via an EM-like algorithm that is parameter-free and can be computed in a distributed fashion. We derive a highly accurate theoretical characterization of the false-alarm-rate, and show that it can achieve weak signal detection by aggregating local statistics over time and networks. Finally, we demonstrate the good performance of our algorithm on numerical examples and real-world datasets from twitter and Memetracker.

This article was originally published on TechRepublic. Remember the famous scene in Stanley Kubrick's 1968 2001: A Space Odyssey, when Hal 9000--the intelligent-turned-malevolent computer--regresses to his "childhood" and sings "Daisy Bell" as he's decommissioned by astronaut Dave Bowman? Its inspiration was a real-life Bell Labs demonstration of speech synthesis on an IBM 704 mainframe in 1961, witnessed by Arthur C Clark, who later incorporated it into his 2001 novel and screenplay. Although Bell Labs' involvement in the field stretches back to the 1930s with Homer Dudley's keyboard-and-footpedal-driven Voder speech synthesis device, it's undoubtedly the classic Kubrick/Clarke movie that cemented the ideas of artificial intelligence (AI) and conversing with computers into the public mind. Depending on how old you are, we're now familiar with computerised voices, thanks to devices like Texas Instruments' popular 1978 Speak & Spell educational toy, Stephen Hawking's speech synthesiser (memorably sampled in the Pink Floyd song Keep Talking), GPS navigational systems in your car, and any number of public information and call handling systems. More recently, the combination of automatic speech recognition (ASR), natural-language understanding (NLU) and text-to-speech (TTS) has come to mainstream attention in virtual assistants such as Apple's Siri, Google Now, Microsoft's Cortana, and Amazon's Alexa. To get a handle on how speech technologies work, we clearly need to know something about the mechanics of human speech and the structure of language. When we speak, air from the lungs passes through the vocal tract to produce "voiced" or "unvoiced" sounds (depending on whether the vocal cords are vibrating or not) that may then be modulated by the tongue, teeth and lips.

Lady Gaga has announced a new album, and her first under her birth name -- well, almost. The singer debuted the news of a new record on Zane Lowe's Beats 1 show Thursday. The LP, titled "Joanne," will come out Oct. 21 on Streamline/Interscope. The record is titled after Gaga's middle name (she was born Stefani Joanne Angelina Germanotta), one she shares with her late aunt. Her family also owns the NYC Italian restaurant Joanne Trattoria.

When you get the opportunity to ask world-renowned astrophysicist Neil deGrasse Tyson absolutely anything about the known universe, odds are you'd have some pretty important questions. Like, we don't know, are we alone in the universe? Are alternate realities a thing? And comedian Andy Samberg got this chance last night when he was a fellow guest on The Late Late Show with James Corden. Okay, in Samberg's defense, he also asked about all that other stuff (and also in his defense, we kind of want to know about the robot sex thing too, now that it's out there…).

Long gone are the days of swapping tapes with friends after school, reading about the latest bands in your weekly magazine or tuning into Top of the Pops at the end of the week for the chart countdown. The excitement of discovering new music has definitely declined as the digitalisation of music has taken over the industry. Spotify is the most popular music streaming service out there and as such, harnesses the most valuable asset a company like this can have – data. Used by over 100 million people, with 30million of those paid subscribers and 55% of those linking their accounts to social media. Around 5million playlists are created or edited daily and in 2015 Spotify users streamed over 20bn hours of music.