In this post I refer to lyrics of certain bands as being "Metal". I know some people have strong feelings about how genres are defined, and would probably disagree with me about some of the bands I call metal in this post. I call these band "Metal" here for the sake of brevity only, and I apologise in advance. It is all around us, and the rate at which it is produced in written, stored form is only increasing. It is also quite unlike any sort of data I have worked with before. Natural language is made up of sequences of discrete characters arranged into hierarchical groupings: words, sentences and documents, each with both syntactic structure and semantic meaning. Not only is the space of possible strings huge, but the interpretation of a small sections of a document can take on vastly different meanings depending on what context surround it.

Who here remembers taking computer programming in school? Whether you learned programming by punching holes in a never ending series of cards, or by writing simple DOS or other computer language commands, the fact remained that computers needed an incredibly precise set of instructions to accomplish a task. The more complicated the task, the more complicated your instructions had to be. Machine learning is inherently different. Rather than telling a computer exactly how to solve a problem, the programmer instead tells it how to go about learning to solve the problem for itself.

Any aspiring science fiction writer looking for a good protagonist could do worse than ripping off the Wikipedia page for Demis Hassabis: He grew up in England as a chess prodigy and built absurdly sophisticated video games before getting a degree in computer science from Cambridge, started studying neuroscience and publishing respected papers on amnesia and other topics, and then proceeded to co-found one of the hottest artificial-intelligence startups. Now that his company, DeepMind, has been snapped up by Google for a reported 400 million to 500 million (depending on your tech blog of choice), exactly how this latest twist will change his story remains to be seen--but there's a decent chance Hassabis will ultimately become commander of an army of humanoid Googlebots. Google's acquisition of Hassabis and the rest of the DeepMind team points to the surging interest in the field of deep learning, a funky part of computer science seen as key to building truly intelligent machines. It centers on having computers learn to do tasks and find patterns on their own. Google, for example, received attention a couple of years ago, when its network of self-learning computers were able to understand the concept of a cat and find cats in YouTube videos.

Scientists at IBM have claimed a computational breakthrough after imitating large populations of neurons for the first time. Neurons are electrically excitable cells that process and transmit information in our brains through electrical and chemical signals. These signals are passed over synapses, specialised connections with other cells. It's this set-up that inspired scientists at IBM to try and mirror the way the biological brain functions using phase-change materials for memory applications. Using computers to try to mimic the human brain is something that's been theorised for decades due to the challenges of recreating the density and power.

Machine learning and artificial intelligence are some of the hottest fields in tech right now. While the terms have kind of become buzzwords in startup land, Google has had teams doing research and building AI-driven applications for years. For example, the company established its "Brain" group five years ago and the team has since penned dozens of papers, built an open-source AI system called TensorFlow, and influenced a bunch of Google products and services like Photos, SmartReply, and speech recognition. The team held a question and answer session yesterday on Reddit, and one of the most striking parts (to someone not entrenched in that world, at least) was reading about the crazy-diverse backgrounds that Google Brain team members have. You might think that to be working at one of the preeminent machine learning groups, you would have to have a degree in computer science from Stanford.

Sessions include: 1) Setting the Scene: The Machine Intelligence Landscape in Renewables; 2) Predicting Energy Consumption Using Machine Learning; 3) Deep Learning for Multiple Weather Prediction Models; and 4) Discussion: What Are The Next Steps?

I just got back from SXSW where I was on a panel entitled "Can AI Systems Really'Think?'"

Seymour Papert with LEGO Mindstorms robotics kits, which were named in recognition of Papert's seminal book, Mindstorms: Children, Computers, and Powerful Ideas. Seymour Papert with LEGO Mindstorms robotics kits, which were named in recognition of Papert's seminal book, Mindstorms: Children, Computers, and Powerful Ideas. The field of educational technology is mourning a visionary whose work was considered 50 years ahead of its time. Seymour Papert, who died July 31 at age 88, was a mathematician and computer scientist who spent decades at MIT. "Seymour was one of the very first people to recognize that new computer technologies could be used by kids to create things in new ways and express themselves," Mitchel Resnick, a professor of learning research at MIT and a longtime colleague and friend, told NPR Ed. "It's amazing that Seymour was thinking these ideas in the 1960s," Resnick adds, "when computers cost hundreds of thousands of dollars, but he foresaw the day that every child would have access to a computer." The great theme of Papert's work and life was the nature of intelligence, or what he called thinking about thinking.

When we talk about artificial intelligence, many imagine a world of science fiction where robots dominate. In reality, artificial intelligence is already improving current technologies such as online shopping, surveillance systems and many others. In the area of cyber security, artificial intelligence is being used via machine learning techniques. Indeed, the machine learning algorithms allow computers to learn and make predictions based on available known data. This technique is especially effective for daily process of millions of malware.

Ronnie Vuine runs a Berlin-based startup that harnesses artificial intelligence for industrial uses – but that doesn't mean he's all business, all the time. His company Micropsi Industries also programmed an A.I. game piece known as D1 "for fun, and because we want to show what's possible," he told Handelsblatt. D1's A.I. "brain" looks a bit like a fire hydrant spraying out countless tiny streams of water. The figure uses trial and error to track down food, Mr. Vuine explained, and each stream represents a different experience.