A team of researchers from Belgium think that they are close to extending the anticipated end of Moore's Law, and they didn't do it with a supercomputer. Using an artificial intelligence (AI) algorithm called reservoir computing, combined with another algorithm called backpropagation, the team developed a neuro-inspired analog computer that can train itself and improve at whatever task it's performing. Reservoir computing is a neural algorithm that mimics the brain's information processing abilities. Backpropagation, on the other hand, allows for the system to perform thousands of iterative calculations that reduce error, which lets the system improve its solution to a problem. "Our work shows that the backpropagation algorithm can, under certain conditions, be implemented using the same hardware used for the analog computing, which could enhance the performance of these hardware systems," Piotr Antonik explains.

In 1962, one year after the first industrial robot joined a production line at General Motors, the animated sitcom The Jetsons debuted. For just one season, the show forecast a future when people could have whatever they wanted (a gourmet dinner, a clean house, a flying car that folds into a briefcase) by pushing a button. It was fantasy then, and much of it still is today--progress is sometimes slow. The Hunter-Gatherer Age lasted a couple million years, the Agricultural Age lasted several thousand years, and the Industrial Age lasted a couple of centuries. Then the Information Age came along and dramatically accelerated the speed at which we evolve, at least technologically.

The advancement could mark a major breakthrough in the development of AI, as the "differentiable neural computer" (DNC) can solve problems without any prior knowledge. Instead, the DNC learns to use its own memory to answer questions about complex data. In a study published in the journal Nature, the technology also demonstrated it can solve a block puzzle game using reinforcement learning. What makes the DNC impressive is that it can learn to form and navigate complex data structures all on its own. The researchers demonstrated how the program can analyze a description of an arbitrary graph and answer questions about it.

When someone can take a very challenging present-day problem and translate it into a problem that has been studied for centuries, the result can be amazing. Such is the case with Word2Vec, a method for transforming words into vectors. Text is unstructured data and has been explored mathematically far less than vectors, both historically and today. Physicist and mathematician Sir Isaac Newton may have been the first person to study vectors in the context of forces in physics. The concept of vectors has almost three centuries of scientific maturity.

This is a problem for scientists working toward the creation of Artificial Intelligence (AI) systems capable of performing complex tasks with minimal human supervision. In a step toward overcoming this hurdle, researchers at Google's DeepMind -- the company that developed the Go-playing computer program AlphaGo -- announced earlier this week the creation of a neural network that can not only learn, but can also use data stored in its memory to "logically reason" and make inferences to answer questions. DeepMind's new system -- called a Differentiable Neural Computer (DNC) -- combines deep learning, wherein it can learn from examples and make sense of complex input it has never received before, with an external memory, which, as the DeepMind researchers Alexander Graves and Greg Wayne explain in a blog post, allows it to "store knowledge quickly and reason about it flexibly." In order to achieve this, the researchers first trained the neural network using randomly generated map-like structures -- a process that allowed the DNC to learn how to store connections between various parts in its external memory. After this, when it was confronted with a new map, the DNC was able to provide answers that were not explicitly stated in the data set.

A former solicitor general has said she is concerned the Ched Evans rape case could discourage victims of sexual offences from coming forward. The 27-year-old footballer was cleared on Friday of raping a 19-year-old woman in a hotel room. Vera Baird told the BBC that details of the woman's sexual past should not have been heard in court. Mr Evans was found guilty of rape in 2012, but that conviction was quashed in April. The Chesterfield striker was accused of attacking the woman at a Premier Inn in Rhuddlan, Denbighshire, on 30 May 2011.

Designed with strong technical expertise and high intelligence, it's not so far-fetched that the robots of the future could outperform human managers. This article titled "Why a robot could be the best boss you've ever had" was written by Tomas Chamorro-Premuzic, for theguardian.com Would you like to work for a robot? Although the automation of skilled jobs is a reality, your boss is probably (still) a human. Even though the most optimistic artificial intelligence (AI) enthusiast would struggle to persuade us that the technology to create non-human leaders is upon us just yet, robots could be managing people in the decades to come.

This time next week, on Tuesday 11 October at Bletchley Park, sees the launch of an initiative to celebrate women in maths and computing. As a new branch of the existing Suffrage Science scheme, it will encourage women into science, and to reach senior leadership roles. Women make up no more than four in ten undergraduates studying maths (London Mathematical Society), and fewer than two in ten of those studying computer science (WISE report, 2014). Despite much effort, there has been little sign of improvement. In fact, the number of women studying computer science at the undergraduate level has been in decline since the 1980s.

And – why we aren't ready for Superintelligence, DeepMind created an AI with memory, Facebook's ideas for VR and more! Last weekend we saw Cybathlon, the world's first "bionic Olympics", where disabled athletes assisted with exoskeletons, prosthetic robotic hands or brain-computer interfaces competed in a series of challenges. This article from BBC describes the games and lists all the winners. Some amputees want to have a prosthetic limb that can do a bit more or just looks better.Waterproof, dustproof, customized to client's skin color, matching to the owner's tattoos. And there are companies that are ready to help them for an appropriate price.

Scientists at Google have created an artificial intelligence program that can compute problems requiring strategic reasoning, The Guardian reports. The algorithm, part of an emerging field called deep learning, is able to master tasks independently using external memory, similar to the way humans work through a new recipe, according to the study published in Nature. In this case, it was able to figure out on its own the quickest route between stops on the London Underground and reassess if the destination was overshot. This could pave the way to more efficient virtual assistant applications, which might be bad news for Apple's sassy sidekick.