IBM (NYSE: IBM) today revealed a series of new servers designed to help propel cognitive workloads and to drive greater data center efficiency. Featuring a new chip, the Linux-based lineup incorporates innovations from the OpenPOWER community that deliver higher levels of performance and greater computing efficiency than available on any x86-based server. Collaboratively developed with some of the world's leading technology companies, the new Power Systems are uniquely designed to propel artificial intelligence, deep learning, high performance data analytics and other compute-heavy workloads, which can help businesses and cloud service providers save money on data center costs. The three new systems are an expansion of IBM's Linux server portfolio comprised of IBM's specialized line of servers co-developed with fellow members of the OpenPOWER Foundation. The new servers join the Power Systems LC lineup that is designed to outperform x86-based servers on a variety of data-intensive workloads.

The Pentagon's research agency has a new challenge for scientists: make wireless radios with artificial intelligence that can figure out the most effective, efficient way to use the radio frequency spectrum, and win a pile of cash. Winners of the Defense Advanced Research Projects Agency's (DARPA) Spectrum Collaboration Challenge (SC2) could take home up to 3.5 million, but to do that, teams will have to demonstrate new technologies that represent a "paradigm shift" with both military and commercial applications, said Paul Tilghman, a DARPA program manager who is leading the challenge. "The real crux of the problem is -- when you look at users of the spectrum, whether they are commercial users of the spectrum, whether they're consumers or they're the military -- the thing that is ubiquitously true is we all are placing more and more and more demand on the spectrum, and all of that demand is really adding up and going to stress the way that we actually manage the spectrum," he said. "Where do we put our communications systems? Where do we put our radars? Where do we put our [electronic warfare] systems?"

I've been writing about the work of Cathy "Mathbabe" O'Neil for years: she's a radical data-scientist with a Harvard PhD in mathematics, who coined the term "Weapons of Math Destruction" to describe the ways that sloppy statistical modeling is punishing millions of people every day, and in more and more cases, destroying lives. Today, O'Neil brings her argument to print, with a fantastic, plainspoken, call to arms called (what else?) Weapons of Math Destruction. Discussions about big data's role in our society tends to focus on algorithms, but the algorithms for handling giant data sets are all well understood and work well. Models are what you get when you feed data to an algorithm and ask it to make predictions. As O'Neil puts it, "Models are opinions embedded in mathematics." Other critical data scientists, like Patrick Ball from the Human Rights Data Analysis Group have located their critique in the same place.

The traditional definition of artificial intelligence is the ability of machines to execute tasks and solve problems in ways normally attributed to humans. Some tasks that we consider simple--recognizing an object in a photo, driving a car--are incredibly complex for AI. Machines can surpass us when it comes to things like playing chess, but those machines are limited by the manual nature of their programming; a 30 gadget can beat us at a board game, but it can't do--or learn to do--anything else. This is where machine learning comes in. Show millions of cat photos to a machine, and it will hone its algorithms to improve at recognizing pictures of cats.

The US space agency (Nasa) has launched a mission to retrieve a rock sample from a 500m-wide asteroid called Bennu. Scientists hope the material will reveal details about the formation of the planets, and improve our knowledge of how potentially dangerous space objects move through the Solar System. The probe, dubbed Osiris-Rex, blasted away from Florida on an Atlas rocket at 19:05 local time (00:05 BST). It will be seven years before it returns to Earth with its bounty. This will be delivered in a capsule that will be parachuted down to the Utah desert on 24 September 2023.

In a variety of application domains the content to be recommended to users is associated with text. This includes research papers, movies with associated plot summaries, news articles, blog posts, etc. Recommendation approaches based on latent factor models can be extended naturally to leverage text by employing an explicit mapping from text to factors. This enables recommendations for new, unseen content, and may generalize better, since the factors for all items are produced by a compactly-parametrized model. Previous work has used topic models or averages of word embeddings for this mapping. In this paper we present a method leveraging deep recurrent neural networks to encode the text sequence into a latent vector, specifically gated recurrent units (GRUs) trained end-to-end on the collaborative filtering task. For the task of scientific paper recommendation, this yields models with significantly higher accuracy. In cold-start scenarios, we beat the previous state-of-the-art, all of which ignore word order. Performance is further improved by multi-task learning, where the text encoder network is trained for a combination of content recommendation and item metadata prediction. This regularizes the collaborative filtering model, ameliorating the problem of sparsity of the observed rating matrix.

Project Wing, a subdivision of Google's parent company Alphabet, will use self-guiding drones to deliver Chipotle food at Virginia Tech this fall. The drones are capable of both flying and hovering on pre-planned routes, avoiding hazards as they go. Dave Vos, head of Project Wing, told Bloomberg that human pilots will be there to take over in case of emergency, as is required by the FAA. This specific experiment is novel, because "it's the first time that we're actually out there delivering stuff to people who want that stuff," Vos told Bloomberg. The drones will launch from a food truck, make the delivery, and return to the truck as a "home base."

It's every college student's dream – airborne drones that deliver burritos to campus. Alphabet's Project Wing and Chipotle have teamed up for a pilot program that flies these stuffed tortillas to the students at Virginia Tech in Blacksburg, Virginia. The hybrid drones will fly to the delivery location from a Chipotle food truck, hover over the customer and lower the order down using a wire cable. Chipotle and Alphabet Inc.'s Project Wing have teamed up for a pilot program that flies these stuffed tortillas to the students at Virginia Tech. The drones will fly to the delivery location from a Chipotle food truck, hover overhead and lower the order down to the customer using a wire cable.

The politicians (Governments) need not stop the outsourcing of the jobs, it will mostly come to an halt by itself when the employment sectors start to employ more and more AI robot automation. Nevertheless, there will be no respite from the jobless problems it will rather skyrocket. To deal with this issue, what we need is a Scientific Approach and the historical perspective of this situation. "Think Out Of The Box". The gist of the theory is; there must be A Gradual Reduction in Working Hours, which In the present situation, ENACTMENT of A THREE-DAY/24/HOUR WORK WEEK. Before which, the principle behind my theory is; the fruits of the societies technological progress should not be appropriated only by a few elite but it is their historical responsibility to see to it that the benefit should be equally shared among all members of the society.

Israel has deployed autonomous military vehicles to patrol the border of the Palestinian Authority. These vehicles are apparently unarmed, so far, and groups of them are controlled by a remotely placed soldier, so they are not quite up to the level of a Robocop, which I suppose is some kind of relief. There are no fully robotic warriors out there, yet. Something along the lines of, that which does not kill us now, might still kill us later if we aren't ready. The short version of all this is that as AI research engages more and more with the real world, things may take some dangerous turns.