Recently we have seen a slew of popular films that deal with artificial intelligence – most notably The Imitation Game, Chappie, Ex Machina, and Her. However, despite over five decades of research into artificial intelligence, there remain many tasks that humans find simple which computers cannot do. Given the slow progress of AI, for many the prospect of computers with human-level intelligence seems further away today than it did when Isaac Asimov's classic I, Robot was published in 1950. The fact is, however, that today neuromorphic chips offer a plausible path to realizing human-level artificial intelligence within the next few decades. Starting in the early 2000's there was a realization that neural network models – based on how the human brain works – could solve many tasks that could not be solved by other methods.

Text messaging has become a mainstream form of communication. Now, customer service industry is focusing on the chatbots – the next big thing for information management and customer service. Chatbot is a program simulating a real conversation that you interact with via a chat interface. The application of bots can be really wide, varying from functional to fun, and it can be implemented in any major chat product like Facebook Messenger, Telegram, Slack, text messages, etc. Chatbots have the ability to interpret human speech or writer inquiries and decide which information is being sought. Sometimes you might not realize it, but when it comes to buying tickets, hailing Uber, or ordering tacos, chances are there's a chatbot living in your messaging app.

It might seem like an idea out of science fiction, but the possibility of using potentially any material as a computer could soon become a reality. These alternative materials could be even better for developing artificial intelligence than existing computers. Some believe such AI could eventually have human-like intelligence and reasoning. Experts Mark Douthwaite, a PhD Candidate in High Integrity Systems Engineering, and Matt Dale, a PhD Student in Non-Standard Computation, both from the University of York, explain how this works in an article for The Conversation. It might seem like an idea out of science fiction, b ut the possibility of using potentially any materia l as a computer could soon become a reality.

Chatbots powered by AI are all the rage nowadays. Google, Facebook, Microsoft, all are developing chatbots that can not only decipher speech, but also understand the meaning behind those words. Chinese search giant Baidu wants to take that all one step further. The company just unveiled Melody, an AI-powered chatbot tailored for the medical field. The bot is able to chat with patients, getting them to reveal symptoms, and then transmits that data to doctors, along with a partial analysis.

Gartner predicts that by 2018, 20 percent of business content will be authored by machines. This figure is set to continue to rise as chatbots become increasingly useful and the users increasingly accepting. The main attention on chatbots up until now has been on how they can answer and respond to incoming requests, be they for customer service, entertainment, or other uses. But this is just half of the story. There is a huge benefit in using chatbots to initiate or continue customer engagement, not just react to it.

These systems don't just require more information than humans to understand concepts or recognize features, they require hundreds of thousands times more, says Neil Lawrence, a professor of machine learning at the University of Sheffield and part of Amazon's AI team. Once they've been trained, they can be incredibly efficient at tasks like recognizing cats or playing Atari games, says Google DeepMind research scientist Raia Hadsell. A solution to this might be something called progressive neural networks -- this means connecting separate deep learning systems together so that they can pass on certain bits of information. One way of doing this is revisiting an old, unfashionable strand of artificial intelligence known as symbolic AI or Good Old-Fashioned Artificial Intelligence (GOFAI), says Murray Shanahan, a professor of cognitive robotics at Imperial College London (and also the scientific advisor on Ex Machina).

Back in 1970, the social activist Irina Dunn scribbled a slogan on the back of a toilet cubicle door at the University of Sydney. It said: "A woman needs a man like a fish needs a bicycle." The phrase went viral and eventually became a famous refrain for the growing feminist movement of the time. The phrase is also an example of sarcasm. The humor comes from the fact that a fish doesn't need a bicycle.

DeepMind, an artificial intelligence firm that was acquired by Google in 2014 and is now under the Alphabet umbrella, has developed a computer than can refer to its own memory to learn facts and use that knowledge to answer questions. That's huge, because it means that future AI could respond to queries from humans without being taught every possible correct answer. DeepMind says its new AI model, called a differentiable neural computer (DNC), can be fed with things like a family tree and a map of the London Underground network, and can answer complex questions about the relationships between items in those data structures. For example, you could get responses to questions like, "Starting at Bond street, and taking the Central line in a direction one stop, the Circle line in a direction for four stops, and the Jubilee line in a direction for two stops, at what stop do you wind up?" DeepMind says its DNC could also help you plan an efficient route from Moorgate to Piccadilly Circus. Similarly, it could understand and answer questions about the relationships between people from a large family, like, ""Who is Freya's maternal great uncle?" This discovery builds on the concept of neural networks, which mimic the way the human mind works.

Computational workloads are growing, and processors, memory, and storage are getting faster at a blazing pace. Emerging technologies could leave computers choking for bandwidth. The potential chokepoint worries companies like Google, IBM, Samsung, and Dell, which are moving to remedy the problem. New specifications from two new consortia will bring data unprecedented boosts in data transfer speeds to computers as early as next year. OpenCAPI Consortium's connector specification will bring significant bandwidth improvements inside computers.

Having stepped down from the industry after 40 years to become warden of his alma mater, New College, Oxford, Ogilvy & Mather's former global chairman and chief executive reminisces about the'bad men' years, ruminates on today's challenges and proposes how adland can regain its swagger.