All of it coming at you day and night in a never-ending stream that moves so fast that it becomes a screaming blur that makes you want to bang your head against a brick wall until the lost consciousness finally silences the machine and provides you with some degree of sanctuary from the madness. With this in mind, it seems clear that Americans are suffering from information overload, right? Well, according to the latest Pew numbers, only 20 percent of us feel overloaded by the glut of information we encounter on a continual basis. And that surprisingly low number is actually down pretty dramatically from a decade ago. The NextDraft newsletter is now on WIRED.com.

Oxford University researchers have estimated that 47 percent of U.S. jobs could be automated within the next two decades. With everything from Amazon Go's cashierless stores to automated customer service to robo-advisors and driverless cars, we are moving toward a world with more productivity and fewer jobs. As many have commented, this is very reminiscent of globalization and outsourcing. But, with outsourcing, we only did half the work -- we struck trade deals around the world and increased productivity and corporate profits, but forgot to help retrain the work force to land jobs in the new economy. The result was an entire population left behind, and populist reactions like Brexit and Trump.

A new artificial intelligence system can take still images and generate short videos that simulate what happens next similar to how humans can visually imagine how a scene will evolve, according to a new study. Humans intuitively understand how the world works, which makes it easier for people, as opposed to machines, to envision how a scene will play out. But objects in a still image could move and interact in a multitude of different ways, making it very hard for machines to accomplish this feat, the researchers said. But a new, so-called deep-learning system was able to trick humans 20 per cent of the time when compared to real footage. Researchers at the Massachusetts Institute of Technology (MIT) pitted two neural networks against each other, with one trying to distinguish real videos from machine-generated ones, and the other trying to create videos that were realistic enough to trick the first system. This kind of setup is known as a "generative adversarial network" (GAN), and competition between the systems results in increasingly realistic videos.

Dead Rising 4 released this week, and that's (as far as I can remember) the last big tentpole of 2016. Time for everyone to pick out their Game of the Year lists and settle in for a nice winter's nap--and maybe a chance to catch up on your backlog, finally. This week Battlefield 1 adds a Grenade Crossbow, Fallout gets the pinball treatment, someone builds an Atari 2600 in Minecraft, and Sean Bean finds out he died in the Civilization VI trailer. Before the onslaught of paid Battlefield 1 DLC starts flooding out, DICE is giving away a map to everyone for free. Starting December 20 (or December 13 if you preordered), you'll be able to play Giant's Shadow, which looks like it takes place next to a crashed zeppelin.

Last month, at the world's leading general management conference--the Drucker Forum in Vienna Austria--Julian Birkinshaw, Professor of Strategy and Entrepreneurship at the London Business School and Director of the Deloitte Institute of Innovation and Entrepreneurship, declared provocatively that we are living in "the Age of Agile." The full text of Julian's important talk is set out below. He also has a new book coming out next year entitled Fast/Forward: Make Your Company Fit For the Future. Earlier this week, I discussed these issues with Julian. Steve Denning: In your talk, you spoke about three possible forms of organization: bureaucracy, meritocracy and adhocracy. Could you tell us more? Julian Birkinshaw: Bureaucracy is about and occupying roles and following rules. Meritocracy is a knowledge-based view of the organization, including big data and analytics. Adhocracy is an action-based view of the organization focused on capturing opportunities, solving problems and getting results. Denning: Where does "the age of Agile" fit into this scheme?

Video Friday is your weekly selection of awesome robotics videos, collected by your Automaton bloggers. We'll also be posting a weekly calendar of upcoming robotics events for the next two months; here's what we have so far (send us your events!): Let us know if you have suggestions for next week, and enjoy today's videos. What you're not seeing here is the person on the other end of this, operating the robot remotely. I feel like there's a market for this sort of thing in stadiums.

Microsoft's slow advance into the mobile territory held by rivals Apple and Google continues this week with updates and expanded availability of a key mobile application that runs on the opposition's platforms. As iOS and Android circle the wagons to make sure that no other OS platforms can become established in the mobile space, Microsoft is playing a slightly longer game with a different target. Although Windows 10 Mobile is a great environment (and one that I personally enjoy) it does not have the market share or volume of mobile users that are needed to be self-sustaining. Which is why Microsoft is not only focusing on cloud-based services, but also ensuring they are usable on both iOS and Android as well as Windows 10. A quick glance at the app stores shows the prominence of Outlook, the utility of Outlook365, and the quiet magic of OneNote all riding high.

One of China's leading tech companies is building an AI lab that could soon rival those operated by the likes of Google, Facebook, Baidu, and Amazon. Tencent, based in Shenzhen, in southern China, operates a range of online and mobile services, including the hugely popular social mobile apps WeChat and QQ. The company created its AI lab in April, and it is growing rapidly. Tencent sent a delegation of AI researchers, recruiters, and business representatives to the industry's preeminent event, the Neural Information Processing Systems conference, held in Barcelona, Spain, this week. Tencent's push into AI research reflects a broader shift across China's consumer technology industry toward more fundamental research designed to spur real innovation.

The final argument of the pessimists is that automation is "hollowing out" the workforce by replacing the jobs of the middle-skill professions, so we will be left with a world of hedge-fund managers and their maids. There has been some disproportionate losses of middle-income jobs in America and Europe since 1980, but as the MIT economist David Autor argues, it's as much to do with competition from China as automation per se. And he thinks it is running out of steam anyway. Journalists, he says "tend to overstate the extent of machine substitution for human labor and ignore the strong complementarities between automation and labor that increase productivity, raise earnings, and augment demand for labor."

Multiple instance learning (MIL) is a form of weakly supervised learning where training instances are arranged in sets, called bags, and a label is provided for the entire bag. This formulation is gaining interest because it naturally fits various problems and allows to leverage weakly labeled data. Consequently, it has been used in diverse application fields such as computer vision and document classification. However, learning from bags raises important challenges that are unique to MIL. This paper provides a comprehensive survey of the characteristics which define and differentiate the types of MIL problems. Until now, these problem characteristics have not been formally identified and described. As a result, the variations in performance of MIL algorithms from one data set to another are difficult to explain. In this paper, MIL problem characteristics are grouped into four broad categories: the composition of the bags, the types of data distribution, the ambiguity of instance labels, and the task to be performed. Methods specialized to address each category are reviewed. Then, the extent to which these characteristics manifest themselves in key MIL application areas are described. Finally, experiments are conducted to compare the performance of 16 state-of-the-art MIL methods on selected problem characteristics. This paper provides insight on how the problem characteristics affect MIL algorithms, recommendations for future benchmarking and promising avenues for research.