The federal government spent millions of taxpayer dollars studying if women can pick Barbie out of a lineup, creating robot flowers, and building a computer that binge watches "Desperate Housewives," according to a new report on wasteful spending. Sen. Jeff Flake, R.-Ariz., on Tuesday released Wastebook: PORKémon Go, the latest chronicle of waste in a series started by former Oklahoma Sen. Tom Coburn, a Republican. The report covers 50 items and more than $5 billion in spending on outrageous government programs and frivolous projects. "Within mere days, the national debt will top $20 trillion, the largest amount ever owed by any nation in history, and the federal government's authority to borrow expires in March," Flake wrote in the introduction to the report. "But rather than making a long overdue resolution to be fiscally responsible, the promises from Washington are to spend even more."

From a nation defined by cricket, steam engines and horses to a celebrity-obsessed, football-mad country, Britain has undergone huge cultural changes between 1800 and 1950. The history of the country is well-documented in books, but a new analysis has used a different source, newspapers, to pinpoint exactly when these shifts in culture took place. A group of computer scientists have developed artificial intelligence software to analyse articles from 120 British newspapers between 1800 and 1950, and revealed key turning points in our culture. In 2011, a huge analysis of five million books taken from over 200 years provided the first clues to what insights can be gained from analysing words on such a huge scale. But the project was criticised for only counting words, and ignored the context they were based in.

UK ISPs will begin sending out emails to subscribers later this month, warning them of movie, TV and music piracy identified on their connection and pointing them to legal content sources. Providers confirmed to ISPreview the voluntary programme will kick off later this month, with Virgin Media offering more details on how it'll work. If the ISP can link the IP address with a subscriber account, it'll send an email expected to contain an itemisation of the media in question, as well as links to bona fide content sources and other educational, anti-piracy resources. Torrents are the main target here, since it's easy to identify IP addresses due to the peer-to-peer nature of the network (as long as there's no VPN at play). Tracking people that turn to other sources of illegal media, such as direct download sites, is more complex.

Director Denis Villeneuve's "Arrival" may be an alien movie, but you won't hear the sounds of warp speed, Martian death rays or beeping robots in it, say supervising sound editor Sylvain Bellemare and re-recording mixer Bernard Gariépy Strobl. "Denis really insisted on having a sound that was not electronic," Bellemare says of the film starring Amy Adams as a linguist trying to communicate with an alien species. "He wanted to do another type of science fiction. So he wanted to use an approach of [making] the sound really organic." Bellemare and Gariépy Strobl knew what they were in for, having worked together on several previous films, including Villeneuve's 2008 short "Next Floor."

Reality was more ambitious than rumor when AirTV made its big debut at CES 2017. Instead of a networked antenna tuner that feeds broadcast channels into the Sling TV app on other devices, AirTV wants to be the complete package. It's a $100 Android-based box with an optional antenna hookup, letting you stream Sling TV channels and free over-the-air broadcasts into a unified interface, which also includes recommendations from Netflix. The remote control strives for similar harmony, with TV power and volume controls, dedicated guide and previous channel buttons, voice search from Google, and shortcuts to Netflix and Google Play Movies & TV. As importantly, AirTV isn't just a product, but a new subsidiary of Dish Network, which also operates the Sling TV service.

In recent years, computers have gotten remarkably good at recognizing speech and images: Think of the dictation software on most cellphones, or the algorithms that automatically identify people in photos posted to Facebook. But recognition of natural sounds -- such as crowds cheering or waves crashing -- has lagged behind. That's because most automated recognition systems, whether they process audio or visual information, are the result of machine learning, in which computers search for patterns in huge compendia of training data. Usually, the training data has to be first annotated by hand, which is prohibitively expensive for all but the highest-demand applications. Sound recognition may be catching up, however, thanks to researchers at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL).

While both have gained a lot of attention this year, these techniques have been around for quite some time, but no more so than now, has it felt so promising. Over the past few years, there has been a monumental shift in technology and how it's being applied to everyday life. From robots to search engines, deep learning and machine learning are being raved about as the tech fuelling our new innovations, but many are left wondering what truly differentiates these two models. Broadly speaking, both machine learning and deep learning are forms of Artificial Intelligence, the intelligence exhibited by machines using cutting-edge techniques to perform cognitive functions that we associate with intuitive learning; however, each application is unique and offers an array of benefits to the end-user, whether it's solving unique problems for a particular business case, aiding in speech/facial recognition, speeding up web applications or protecting against breaches or hacks. While the concepts of machine learning and deep learning have been around as early as the 1960s, each model has changed drastically over the years, creating a greater divide between the two.

Mark your calendars: The original "Ghost in the Shell" movie will be hitting theaters for a limited run in February. Lionsgate and Funimation have announced that Mamoru Oshii's 1995 anime film will return to the big screen for a two-day engagement. The original Japanese version (with English subtitles) will be shown Feb. 7, while those who prefer the English-dubbed film will have to wait until Feb. 8. "'Ghost in the Shell' is truly a seminal work in anime cinema and it helped firmly establish a market for the genre with U.S. movie audiences," said Gen Fukunaga, Funimation founder and chief executive in a press release. "We're excited to be partnering with Lionsgate to bring the original'Ghost in the Shell' movie back to U.S. theaters." Based on the manga series by Masamune Shirow, "Ghost in the Shell" is set in a fictional Japanese city in a future where the line between techonlogy and biology is blurred.

"As our technologies change the world, the responsibility only grows deeper for each of us to take an active role in shaping it the way we want--not the other way around."-Ray It's what we teach; it's what we expect from the world and expect our students to understand. When I was a teenager growing up in a working class town, I can recall the horror stories of robots which would take over our jobs and that would make human workers redundant….literally. It turned out that the'robots' of the time were actually automated processes and machinery with little or no sign of the robots my over active imagination conjured up after years of watching sci-fi films. Jobs were not lost en masse in my little town and humans were not rendered redundant…well not by mechanical beasts anyway.

What major advances do you foresee in artificial intelligence in 2017? Despite the impressive progress that machine learning made in 2016, AI systems remain specialists: they cannot add new skills to their repertoires without erasing what they already know. This is the problem of catastrophic forgetting. An AI trained to recognise faces in photographs, for example, would not apply well to another visual task, such as recognising street signs. Each system would need to be trained for its own limited task.