As automation becomes cheaper and robotics innovation accelerates, how we work and who we work with will change. In this excerpt from their new book, "Machine Platform Crowd: Harnessing Our Digital Future,"(W.W. Norton & Company) MIT Sloan's Andrew McAfee and Erik Brynjolfsson identify five areas driving automation and consider where humans fit in the new world of work. San Francisco-based fast causal restaurant Eatsa -- where customers order, pay for, and receive meals without encountering any employees -- wants to do more than virtualize the task of ordering meals; it also wants to automate how they're prepared. Food preparation in its kitchens is highly optimized and standardized, and the main reason the company uses human cooks instead of robots is that the objects being processed -- avocados, tomatoes, eggplants, and so on -- are both irregularly shaped and not completely rigid. These traits present no real problems for humans, who have always lived in a world full of softish blobs. Most of the robots created so far, however, are much better at handling things that are completely rigid and do not vary from one to the next. This is because robots' senses of vision and touch have historically been quite primitive -- far inferior to ours -- and proper handling of a tomato generally entails seeing and feeling it with a lot of precision. It's also because it's been surprisingly hard to program robots to handle squishiness -- here again, we know more than we can tell -- so robot brains have lagged far behind ours, just as their senses have.

Farmers spend nearly half of their operational budgets on agrochemicals such as herbicides and pesticides. Unfortunately, they usually apply these to entire fields at a time, which generates high chemical costs and decreases the efficacy of the chemicals. Such widespread application of chemicals harms the environment, endangers human health, and increases the likelihood of chem ical-resistance in weeds, pests, and diseases. And, even with that damaging widespread application, loss to weeds, pests and diseases can range from 20-50%. But manually scouting and sampling to determine the locations of these problems is time-consuming and costly, and cannot easily account for the enormous variety of factors that affect crops.

This is meant to be a practical book. The author's "objective is to provide a thorough discussion of the most useful data-mining tools that goes beyond the typical'black box' description, and to show why these tools work". I think the result of reading and doing the exercises in this book is: 1. I will have acquired some familiarity with regression techniques and a few of the problems they can help with 2. I will have performed the regression techniques in R Over half the text focuses on various kinds of regression. Then there is a little bit on classification, decision trees, clustering, principal components analysis.

Mining company Rio Tinto has 73 of these titans hauling iron ore 24 hours a day at four mines in Australia's Mars-red northwest corner. BHP Billiton, the world's largest mining company, is also deploying driverless trucks and drills on iron ore mines in Australia. Suncor, Canada's largest oil company, has begun testing driverless trucks on oil sands fields in Alberta. The company's driverless trucks have proven to be roughly 15 percent cheaper to run than vehicles with humans behind the wheel, says Atkinson--a significant saving since haulage is by far a mine's largest operational cost.

The invasion of robots into factories and offices has long been seen as final blow for workforces ravaged by cheap offshore labour and the never ending quest to cut costs. However, that is a view being seriously challenged in hi-tech steel fabricating factory just south of Brisbane. Having put "artificially intelligent" welding and cutting equipment to work, Smart Steel Systems chief executive Chis Brugeaud said he was now able to bring back jobs "onshore" and reverse the trend of laying off people as technology improves. "What makes us different from the traditional fabricator is we have as many developers as we do welders, so we can process around 42,000 tonnes of steel a year," Mr Brugeaud told The Business program. Robotics has delivered two noticeable outcomes.

With ever-growing amount of data produced by the Internet of Things, there is a need for a "smart" engine to analyze it and drive valuable insights. Outlining the challenges, this blog post highlights the six stages of an IoT workflow where artificial intelligence can help out. A scary rate of IoT data growth The Internet of Things (IoT) is covering the gamut of industries: healthcare, aviation…www.altoros.com

Chemists looking to cook up new molecules face a challenge of choosing among hundreds of potential molecular building blocks and thousands of chemical reactions for linking them together. Computational chemists have long programmed computers with known chemical reactions, hoping to create software able to calculate successful molecular recipes. Rather they produce a mix of products at different concentrations. So now researchers are looking to artificial intelligence for help. Instead of programming reactions as hard and fast rules, researchers have developed a neural network that learns from millions successful experiments and figures out on its own which reactions to choose to put together new molecules.

What do you get when you give a design tool a digital nervous system? Computers that improve our ability to think and imagine, and robotic systems that come up with (and build) radical new designs for bridges, cars, drones and much more -- all by themselves. Take a tour of the Augmented Age with futurist Maurice Conti and preview a time when robots and humans will work side-by-side to accomplish things neither could do alone. Maurice Conti explores new partnerships between technology, nature and humanity. Maurice Conti explores new partnerships between technology, nature and humanity.

You'd think computers spend most of their time and energy doing, well, computation. But that's not the case: about 90 percent of a computer's execution time and electrical energy is spent transferring data between the processor and the memory banks, says Subhasish Mitra, a computer scientist at Stanford University. Even if Moore's law continued on indefinitely, computers would still be limited by this memory bottleneck. This week in the journal Nature, Mitra and collaborators describe a new computer architecture they say addresses this problem--and that Mitra believes will improve both the energy efficiency and speed of computers by a factor of 1000. The new 3D architecture is based on novel devices including 2 million carbon nanotube transistors and over 1 million resistive RAM cells, all built on top of a layer of silicon using existing fabrication methods and connected by densely packed metal wiring between the layers.

Binary, or one-bit, representations of data arise naturally in many applications, and are appealing in both hardware implementations and algorithm design. In this work, we study the problem of data classification from binary data and propose a framework with low computation and resource costs. We illustrate the utility of the proposed approach through stylized and realistic numerical experiments, and provide a theoretical analysis for a simple case. We hope that our framework and analysis will serve as a foundation for studying similar types of approaches.