A team of experts from MIT have employed a novel 3D printing technique to combine solids and liquids in order to create a hydraulic six legged robot. MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) researchers reveal the first-ever technique for 3-D printing robots, that involves printing solid and liquid materials at the same time, in a new paper. This interesting bot was created using a 3D printer that had commercial value. A series of bellows provided traction force to the mechanical legs and they were filled with liquid during the period of printing. This is a progressive step in 3D printing since instead of creating individual components, whole active systems can be manufactured.

By definition, Artificial Intelligence is a branch of computer science concerned with the study and creation of computer systems that exhibit some form of intelligence; systems that learn new concepts and tasks; systems that can reason and draw useful conclusions about the world around us; systems that can understand a natural language or perceive and comprehend a visual scene; and systems that perform other types of feats that usually require human intelligence. Humans are prone to making errors. It's how we learn, survive and evolve. "Necessity is the mother of all inventions", and how true this statement is. The main reason why the computer was invented is because humans took a lot of time to perform huge mathematical calculations!

While Siri now lives on the phones of hundreds of millions of Apple users, and a few people are responsible for helping get the now ubiquitous assistant off the ground. One of those people is Dag Kittlaus, co-founder and CEO of Sir and now co-founder and CEO of Viv, a new platform-based AI product. Dag is the CEO and co-founder of Viv, which he started with Siri vets Adam Cheyer and Chris Brigham. Previously, he was the Co-founder and CEO of Siri after spinning the technology out of Stanford Research Institute in 2007. After Apple acquired Siri in 2010 Dag became the Director of iPhone Apps at Apple running the Siri and speech recognition teams.

In Melbourne, Australia, Stefan Harrer is running an artificial software brain atop an artificial hardware brain in an effort to analyze a brain that isn't artificial at all. Ultimately, he and his colleagues envision merging these three brains together so that the artificial can augment the real. Harrer is an IBM researcher stationed at the company's Australian research lab. Together with neurologists at the University of Melbourne, he's developing a computing system that can analyze your brain waves in an effort to predict epileptic seizures. 'Our aim is to replace broken neural systems with machines.'

If you've been using R for a while, and you've been working with basic data visualization and data exploration techniques, the next logical step is to start learning some machine learning. To help you begin learning about machine learning in R, I'm going to introduce you to an R package: the caret package. We'll build a very simple machine learning model as a way to learn some of caret's basic syntax and functionality. But before diving into caret, let's quickly discuss what machine learning is and why we use it. Machine learning is the study of data-driven, computational methods for making inferences and predictions.

This format is a sort of weak DRM required by our contract with MIT Press. It's intended to discourage unauthorized copying/editing of the book. Unfortunately, the conversion from PDF to HTML is not perfect, and some things like subscript expressions do not render correctly. If you have a suggestion for a better way of making the book available to a wide audience while preventing unauthorized copies, please let us know.

Yesterday, Facebook announced the release of automatic alternative text - or automatic alt text - for images posted to Facebook. Automatic alt text uses object recognition technology to generate a description of a photo, processing each through Facebook's artificial intelligence engine to establish image content. It's the latest advancement in Facebook's image recognition technology, a system they've been working on for the last few years, with artificial intelligence guru and New York University professor Yann LeCun at the helm. Last November, Facebook showcased the progress they'd made with their image recognition AI, with their system able to distinguish between objects in a photo 30% faster, and using 10x less training data, than previous industry benchmarks. The live launch of automated captions show just how far their system has advanced, and while it's still not able to provide full, detailed descriptions of everything in each image, the fact that it can be reliably used at all in a live environment is relatively impressive.

Since reading So good they can't ignore you by Cal Newport, I have been reflecting on what rare and valuable skills I should acquire/which field(s) I want to build passion in. As Cal explained so eloquently, people are not born with pre-existing passions, rather they develop passion as their skills improve, they gain experience and they get closer to mastery! My own limited experience concurs with his views, hence my identified passions are a result of previous time and effort, I was not born with them. I am not sure of the origin(s) but over time, a hunger to understand myself and reality has been steadily intensifying! At it's core, human perception of reality is entangled in the mechanics of our bodies which let us explore the world and simultaneously define the limits of our experience.

Modern Workplace is a monthly thought-leadership webcast series that gives you access to entrepreneurs and thought leaders who are changing the future, brought to you by Microsoft. Join us as we explore cognitive technology, and learn what advances in artificial intelligence will impact analytics and demand for human interaction on the next episode of Modern Workplace, Machine Learning: How technology advances human intelligence, airing April 12th at 8:00 AM PDT / 3:00 PM GMT. This month, two of the industry's most forward thinkers share their predictions of where business is going with big data and how we can take advantage of the changes in technology to come. Gain an insider perspective as we dissect the world of cognitive technology, learn what technological capabilities will be available, and what tasks will still require human input.

I'm not experienced in AI or machine learning much at all, but currently want to get into it. I've heard of reductionist AI and holistic AI. Reductionist AI seems to be the kind in which solves problems based on reasoning, (e.g. a problem that a human would need to think hard on with pencil and paper). And for holistic AI, are their actions based on pattern? Again, I'm new to this so please forgive me if I'm confusing.. Do tell me if I have those two wrong or out of order.