The Moto X was Google's vision for the future of mobile technology. Billed as the world's first artificially intelligent phone, its permanent listening mode enabled it to serve up directions or give weather forecasts in response to spoken commands. That was until the search giant called an end to the adventure with the $2.9bn (£1.8bn) sale of Motorola to rising Chinese star Lenovo on Wednesday, a move that will free its engineers to focus on other revolutionary technologies. Google's recent acquisitions – from robot maker Boston Dynamics to a London company that aims to make computers think like humans – show how its ambitions stretch far beyond the smartphone. After all, mobile phones are now a low-margin, high-volume manufacturing business, which makes them a better fit for Lenovo than Google.

An Introduction to Fuzzy Logic Applications in Intelligent Systems consists of a collection of chapters written by leading experts in the field of fuzzy sets. Each chapter addresses an area where fuzzy sets have been applied to situations broadly related to intelligent systems. The volume provides an introduction to and an overview of recent applications of fuzzy sets to various areas of intelligent systems. Its purpose is to provide information and easy access for people new to the field. The book also serves as an excellent reference for researchers in the field and those working in the specifics of systems development.

Illustration of how a virtual interviewer sees a human. Although most would agree that the average person is smarter than the average cat, comparing humans and machines is not as straightforward. A computer may not excel at abstract reasoning, but it can process vast amounts of data in the blink of an eye. In recent years, researchers in artificial intelligence (AI) have used this computational firepower on the scads of data accumulating online, in academic research, in financial records, and in virtually all walks of life. The algorithms they develop help machines learn from data and apply that knowledge in new situations, much like humans do.

There are approximately 86 billion neurons in the human brain. Over the past decades, we have made enormous progress in understanding their molecular, genetic, and structural makeup as well as their function. However, the real power of the central nervous system lies in the smooth coordination of large numbers of neurons. Neurons are thus organized on many different scales, from small microcircuits and assemblies all the way to regional brain networks. To interact effectively on all these levels, neurons, nuclei, cortical columns, and larger areas need to be connected.

When a select band of computer scientists met at Dartmouth College in Hanover, New Hampshire, in 1956 to begin work on a field they called'artificial intelligence', they were optimistic, to say the least. Their founding principle of developing machine intelligence was based on an assumption that human intelligence could itself be well characterized. They argued that: "Every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it." Ask ten people to define human intelligence and you will get at least eleven answers. To a philosopher, intelligence is the absence of a lack of intelligence.

Three research papers about the potential power of computers to outsmart us -- while comforting us with cute pet videos -- caught the attention of the online science community this week. Researchers shared a PLoS ONE paper about a computational fact-checker that can sort truth from fiction; an arXiv preprint about a computer program that can read and comprehend news stories; and a study of the psychology of watching Internet cat videos. In the PLoS ONE paper1, researchers at Indiana University in Bloomington mined Wikipedia's information boxes, which summarize the key facts in most Wikipedia entries, to create a'knowledge graph' of 3 million people, places and things. The resulting algorithm could then use that knowledge to gauge the truth of simple statements that were presented to it, such as "Rome is the capital of Italy", with nearly the same accuracy as human fact-checkers. The researchers acknowledge that the source material is not 100% reliable, something that online commenters also noted.

Google's DeepMind division is using Daily Mail and CNN articles to teach its artificial intelligence programs to read. Using the unique style of articles on the sites - with concise bullet points summarising a story at the top of a page - artificial intelligence was able to learn key facts about articles to answer queries. Ultimately, scientists hope that the study could lead to complex artificial'brains' that can read entire documents and respond to questions put to them by a human. The British-based DeepMind unit analysed almost 400,000 articles from the sites (language process shown). They were used for their unique style of bullets, text and captions. Artificial intelligence was able to learn key facts from the articles.

Many motorists dream of the day they can sit back and relax while their car drives itself. And while Google and other companies are working hard to make autonomous vehicles a reality, it could take years to create a car that can negotiate complex situations on the road – including wet weather conditions. Google's self-driving cars can't currently cope in heavy rain or snow – or find their way around 99 per cent of the US, an insider has admitted. A Google Insider has admitted that the firm's cars (pictured) are incredibly reliant on maps, can't cope with wet weather conditions and are unable to'see' potholes. According to MIT Technology Review, the current prototype cars are very reliant on maps to navigate and can't react like a human driver, dodging potholes and other hazards.

By day, Dr John Elliott is a professor in Computing and Creative Technologies at Leeds Metropolitan University. But by night, his work takes on a more out-of-this-world nature – he's devising the methods we'll need to talk to aliens. Making first contact with extraterrestrial life by detecting a signal would be one of the defining moments in the history of humanity, but what if we don't know what they're saying? That's where Dr John Elliott comes in - he's been devising methods to decode an alien language for the day he believes we'll receive a message Dr Elliot has been working on decoding languages for two decades. In 1959, Cornell physicists Gieuseppi Cocconi and Philip Morrison published an article discussing the potential to use microwave radio to communicate between stars.

Scientists have created a revolutionary technology that could save countless lives by preventing car crashes, and it is inspired by locusts. The insects have an early warning system which helps them avoid colliding with each other when flying in swarms at high speed. Researchers have adopted key features of the locusts' system to develop a computer system which could become a blueprint for highly-accurate collision sensors in cars. Scientists at the University of Lincoln created a collision sensor for cars (pictured) after being inspired by locusts' anti-collision systems Professor Shigang Yue and Dr Claire Rind of the University of Lincoln were inspired by the unique way locusts process electrical and chemical signals in their brain. Professor Yue said: 'We created a system inspired by the locusts' motion sensitive interneuron - the lobula giant movement detector.