At a theoretical level, the concept of artificial intelligence has fueled and sharpened the philosophical debates on the nature of the mind, intelligence, and the uniqueness of human beings. Insights from the field have proved invaluable to biologists, psychologists, and linguists in helping to understand the processes of memory, learning, and language. Today, we're continuing our Q&A with Maggie Boden, Research Professor of Cognitive Science at the University of Sussex, and one of the best known figures in the field of Artificial Intelligence, answers four more questions about this developing area. ANNs are computer systems made of large number of interconnected units, each of which can compute only one (very simple) thing. They are (very broadly) inspired by the structure of brains.

Artificial intelligence (AI) is here. If you are reading this post, some kind of AI has decided it's worth your while. On social media, algorithms learn to know you intimately. They study what you like and compare your interests to a huge pool of users. And almost magically, platforms like Instagram then shows you a selection of photographs you find surprisingly pretty.

Even in the highly technical world of brain surgery, the success of an operation still depends largely on the experience and ability of surgeons. But this may change dramatically with the introduction of "smart" surgical care units, in which everything from computer monitors, robotic arms, microscopes and sensors are connected via the internet to help doctors follow the right course of action. A prototype of an advanced version of such units, the Smart Cyber Operating Theater (SCOT), was showcased Thursday at Tokyo Woman's Medical University in Shinjuku Ward. The project was funded by the state-backed Japan Agency for Medical Research and Development (AMED). Development of such units is expected to push forward the advent of so-called Medicine 4.0, a paradigm shift in the medical industry built on advanced information communications technology, said Yoshihiro Muragaki, a brain surgeon and professor at the university who is leading the project.

Google announced this morning it's opening a dedicated machine learning research center in Europe, which will be based in its Zurich office. The center will focus on the development of products and research in the areas of Machine Intelligence. Specifically, says Google, it will research ways to improve machine learning infrastructure as well as how to put it into practical use. Researchers will also work with linguists to advance Natural Language Understanding – machines' ability to understand and process human language. Google's Zurich office was already home to a lot of activity in this area, the company notes.

On Monday, Microsoft made headlines by plunking down 26 billion for LinkedIn. Now it's announcing its second acquisition of the week: For an undisclosed sum, the company has bought Wand Labs, a Silicon Valley-based startup that declares its mission is "to tear down app walls, integrate your services in chat, and make them work together so you can do more with less taps." Founded in 2013, Wand is tiny--it has just seven employees--and, though no longer in stealth mode, is hardly a household name. The iOS and Android apps it built haven't yet reached general availability, and now they never will, as their creators put them aside and contribute to the greater Microsoftian effort. But for all the ways that this acquisition is different than the mammoth LinkedIn deal, both transactions are pieces of the same puzzle.

With everyone from Google to Elon Musk warning about artificial intelligence or AI, this story is especially timely. A Russian robot learning to promote a company's products and'automatic movement profiles' escaped from its testing grounds in Perm, Russia, near the Ural mountains. The machine made it to a busy intersection, snarled traffic, and the police were called. "The robot was learning automatic movement algorithms on the testing ground, these functions will feature in the latest version of the Promobot." The co-founder of the robot's maker, Oleg Kivokurtsev, told ura.ru news agency.

In 2012 they went bankrupt. And according to a recent post by Dr Robert Goldman when technological singularity really kicks in, the same fate awaits many industries over the next decade. Self-proclaimed "Antiaging & Sports Medicine Pioneer" Dr Bob is a rather exuberant character and seemingly a big friend of the stars (he is pictured on the home page of his website with the likes of Sylvester Stallone and Arnold Schwarzenegger). Yet I'm far more interested in his recent Facebook / LinkedIn post entitled'Future Predictions' which has gone viral and already been shared well over 100,000 times. Dr Goldman initially asks us to go way back to 1998 (where is the Doc's DeLorean when you need it?) It's interesting given that digital cameras had actually been invented over 20 years earlier.

"Political elections – for better or worse – have become a gameshow. The more social media, the more clickbait headlines … whatever generates a lot of buzz is one way to make our mark in an election," he says. Istvan has attracted countless profiles in the international press, been followed around by two film documentary crews and has secured a TV show once the election is over. It certainly seems to be benefiting Brand Zoltan. Could it all be an elaborate strategy to boost his profile and become a media personality?

One of five Hong Kong booksellers who went missing in mysterious circumstances last year has said he had been detained for more than eight months by Chinese authorities. Lam Wing-kee announced on Thursday that he was arrested in the southern Chinese city of Shenzhen and that his colleague, Lee Bo, who went missing from Hong Kong in December, had also been abducted. Following months of speculation about the circumstances surrounding the disappearances, Lam called a surprise press conference just two days after being released. Lam said he was taken on a 14-hour train journey to the eastern city of Ningbo following his arrest. There, he was kept in a small room by himself, and repeatedly interrogated about the selling of banned books on the mainland.

In an earlier post I explored the value of using scalable machine learning to extract value from huge amounts of data. In this post, I will dive down into the technical side of things, particularly the challenges and benefits that come with making algorithms scalable on large clusters of computers. Machine learning algorithms are written to run on single-node systems, or on specialized supercomputer hardware, which I'll refer to as HPC boxes. They grew up in a world where they didn't have to scale across multiple nodes. It's relatively easy to get high performance when running algorithms on a single computer.