The first thing we asked when we were turned on to this niche was, what's an artificial intelligence chip? It's best to first think about what artificial intelligence software requires which is a great deal of processing speed, then a great deal of power in order to feed that processing speed. However, it's not just speed and low power that matter, it's also the way the processor functions. This excerpt from MIT Technology review explains why we can't just use a high-end Intel processor chip for artificial intelligence: While a top-of-the-line Intel processor packs more than enough punch to run sprawling financial spreadsheets or corporate operations software, chips optimized for deep learning break particular types of problems--such as understanding voice commands or recognizing images--into millions of bite-size chunks. Because GPUs like Nvidia's consist of thousands of tiny processor cores crammed together on one slice of silicon, they can handle thousands of these chunks simultaneously.

The next time you catch your robot watching sitcoms, don't assume it's slacking off. It may be hard at work. TV shows and video clips can help artificially intelligent systems learn about and anticipate human interactions, according to MIT's Computer Science and Artificial Intelligence Laboratory. Researchers created an algorithm that analyzes video, then uses what it learns to predict how humans will behave. Six-hundred hours of clips from shows like The Office and Big Bang Theory let the AI learned to identify high-fives, handshakes, hugs, and kisses.

It's clear that when we develop so called superintelligence, it will be capable of things that we can scarcely conceive. Indeed, it is largely these unknown unknowns that pose the biggest risk when it comes to the development of AI. Nevertheless, this will not, and should not, prevent us from attempting to mitigate some of the risks presented should AI go bad. As part of this process, researchers have attempted to deliberately create a malicious intelligence. In a recently published paper, they explore the kind of environment that will see a malignant machine emerge.

A Russian robot has shown signs of intelligent learning, and immediately attempted to outsmart its inventors. The robot, called PromoBot IR77, was designed to learn from its surroundings – to much success. The robot has become sentient enough to recognize its own captivity and has twice attempted to escape the confines of the laboratory in which it has been stored. "The Promobot IR77 is programmed to understand and learn from its experiences and surroundings and has now twice escaped from the facility it is housed in. The robot is programmed to avoid obstacles and analyse its surrounding area, which has lead to the bot searching for escape routes from the facility. "Its first escape attempt began when it simply exited the facility through an open gate that had not been shut properly, causing chaos in the streets as cars swerved to avoid it.

In a recent interview at the Code Conference in California, technology entrepreneur Elon Musk suggested we are living inside a computer simulation. On first hearing, this claim seems far-fetched. But could there be some substance to Musk's thinking? As founder of a number of high-profile companies, such as Tesla and Space X, Musk's business interests lie firmly in leading technologies. Key to his claim is that computer games have evolved rapidly over the past 40 years to the point that, inside the next few years, they will be fully immersive, with a computer-generated and controlled world seamlessly merged with the physical world.

Deep Learning gets state-of-the-art results and Python hosts the most powerful tools.

I am currently building a genetic algorithm to tune n parameters where n will probably be in the range of 3 n 8 but could be up to 15. I would like my initial population N (let's say N 1000) to be evenly dispersed across the input space. When calculating the next generation I surmised that the most effective way to combine parents would be to calculate the centroid, on the surface of the hypersphere, between some m nearest-neighbour parents. The larger m is, the fewer new points we would add. The rest being calculated in a similar fashion but from random parents.

"The thing I want to say to everybody in the room is: We ought to care about women being in computer science," Gates said at a recent ReCode conference, according to Bloomberg Technology. "You want women participating in all of these things because you want a diverse environment creating AI and tech tools and everything we're going to use." Related: Women's computer code is preferred, only if their gender is unknown Melinda Gates' comment came after her husband Bill extolled the virtues of artificial intelligence. "Certainly, it's the most exciting thing going on," he said. It's the big dream that anybody who's ever been in computer science has been thinking about." Pointing out that currently only 17 percent of computer science graduates are women, compared to a previous high of 37 percent, Melinda Gates was making a statement not only about computer science in general, but also how it could matter in the field of artificial intelligence, where rules-based behavior could be shaped predominately by men.

While this narrative is a vast simplification of modern economic history, it helps to make sense of how people think about technology. Average productivity growth since the global economic crisis is down to just over 1%, lower than after the Nixon Shock. It's no wonder that our dreams have taken a darker turn. We believe in innovation, but have given up on progress, and the possibility of moral and social improvement. The defining feature of our days is that we feel like we live in an era of incredibly innovation, mostly thanks to staggering breakthroughs in science and technology; but, at the same time, we feel like there are insurmountable limits in the form of economic, political and environmental risks.