Gone are the days when tossing a mattress in the back of a truck was the only way to get it home from the store. The booming'boxed bed' business has taken over the mattress industry, allowing consumers to purchase their bedding online and have it shipped to their doorstep compressed inside of a box. And a new video shows the monstrous Auto-Pac 1390HCA system using a 60-ton hydraulic press and highly consistent rolling to neatly fold your mattress to be bagged and sealed for delivery. A new video shows the monstrous Auto-Pac 1390HCA system using a 60-ton hydraulic press and highly consistent rolling to neatly fold a mattress to be bagged and sealed for delivery. The first step of preparing a mattress for shipping is to cover it in a protective plastic wrap.

Google is ringing in 2014 with a spending spree, first dropping 3.2 billion to acquire Nest Technologies and now spending a reported 400 million (or more) on the UK-based artificial intelligence outfit DeepMind. It's no secret that Google has an interest in artificial intelligence; after all, technologies derived from AI research help fuel Google's core search and advertising businesses. AI also plays a key role in Google's mobile services, its autonomous cars, and its growing stable of robotics technologies. And with the addition of futurist Ray Kurzweil to its ranks in 2012, Google also has the grandfather of "strong AI" on board, a man who forecasts that intelligent machines may exist by midcentury. If all this sounds troubling, don't worry: Google's acquisition of DeepMind isn't about fusing a mechanical brain with faster-than-human robots and giving birth to the misanthropic Skynet computer network from the Terminator franchise.

We're all a bit worried about the terrifying surveillance state that becomes possible when you cross omnipresent cameras with reliable facial recognition -- but a new study suggests that some of the best algorithms are far from infallible when it comes to sorting through a million or more faces. The University of Washington's MegaFace Challenge is an open competition among public facial recognition algorithms that's been running since late last year. The idea is to see how systems that outperform humans on sets of thousands of images do when the database size is increased by an order of magnitude or two. "We're the first to suggest that face recs algorithms should be tested at'planet-scale,'" wrote the study's lead author, Ira Kemelmacher-Shlizerman, in an email to TechCrunch. "I think that many will agree it's important. The big problem is to create a public dataset and benchmark (where people can compete on the same data). Creating a benchmark is typically a lot of work but a big boost to a research area."

Which kinds of knowledge workers are at high risk of job loss thanks to smart machines? Usually we don't love getting that question, because the answer isn't the simple one interviewers are seeking. Many jobs include tasks that can and will be automated, but by the same token, almost all jobs have major elements that -- for the foreseeable future -- won't be possible for computers to handle. Our advice therefore can't boil down to a clear "avoid careers in a, b, and c" or "apply for jobs x, y, or z." And yet, we have to admit that there are some knowledge-work jobs that will simply succumb to the rise of the robots.

DeepMind, the artificial intelligence research lab acquired by Google for a reported 400 million in 2014, has called on the UK government to consider funding degree courses that focus on machine learning, which is a subfield of AI. The company -- cofounded by Demis Hassabis, Shane Legg and Mustafa Suleyman in 2011 -- said the government needs to support the next generation of machine learning experts if it wants the UK to cement its position as a world leader in AI. Writing in evidence submitted to a parliamentary inquiry into robotics and AI last month, DeepMind said: "The government should consider funding for machine learning masters and PhD programmes at British universities, to encourage more research in the field and nurture the next generation of scientists who will help preserve the UK's preeminent position." The company added: "This funding could also include direct support for modules within programmes that train machine learning researchers in the ethics of data science and increasingly autonomous decision-making, to ensure that the pursuit of beneficial outcomes is embedded in the science of machine learning at every level." Machine learning masters degrees and PhDs can cost individuals upwards of 10,000 at the top universities.

EXPERTS warn that "the substitution of machinery for human labour" may "render the population redundant". They worry that "the discovery of this mighty power" has come "before we knew how to employ it rightly". Such fears are expressed today by those who worry that advances in artificial intelligence (AI) could destroy millions of jobs and pose a "Terminator"-style threat to humanity. But these are in fact the words of commentators discussing mechanisation and steam power two centuries ago. Back then the controversy over the dangers posed by machines was known as the "machinery question".

A robot that is claimed to be so intelligent it can think for itself is set to be scrapped because it keeps escaping from the laboratory it lives in. Last week the Promobot IR77 made a bid for freedom through the streets of the Russian city of Perm where it was developed. The bot, who I'll call Roger, was only captured after he ran out of battery life after escaping through a gate from the exercise yard he was exploring. He had caused chaos as the traffic was forced to swerve to avoid him. Roger is part of a series of robots that have been equipped with artificial intelligence, meaning they learn from their experiences and surroundings.

In a series of surveys published Thursday in the journal Science, researchers asked people what they believe a driverless car ought to do in the following scenario: A group of pedestrians are crossing the street, and the only way the car can avoid hitting them is by swerving off the road, which would kill the passengers inside. The participants generally agreed that the cars should be programmed to sacrifice their passengers if doing so would save many other people. This, broadly speaking, is a utilitarian kind of answer -- one aimed at preserving the greatest possible number of lives. But there's one problem: The people in the survey also said they wouldn't want to ride in these cars themselves. It would be OK for others to buy them, the participants said, but they personally would not.

From within the dark confines of the skull, the brain builds its own version of reality. By weaving together expectations and information gleaned from the senses, the brain creates a story about the outside world. For most of us, the brain is a skilled storyteller, but to spin a sensible yarn, it has to fill in some details itself. "The brain is a guessing machine, trying at each moment of time to guess what is out there," says computational neuroscientist Peggy Seriès. Guesses just slightly off -- like mistaking a smile for a smirk -- rarely cause harm.

Researchers asked 2,000 study participants to choose the moral course of action in accident scenarios where the number of vehicle passengers and at-risk pedestrians varied. Motor vehicle accidents caused nearly 40,000 traffic fatalities and 4.5 million serious injuries in the United States in 2015, and 90 percent of those accidents were due to human error. Remove the human component with self-driving vehicles, and many of those accidents could be preventable. Instead, computer-driven cars will face moral dilemmas where they must choose between two bad outcomes: Place a passenger in danger to save a pedestrian or vice versa. A new study argues that how these vehicles respond to ethical dilemmas could dictate their public safety and widespread adoption by consumers.