It has only been in recent years that we've come to see artificial intelligence as a reality and not something out of a science fiction story. Even now, we're still struggling to come up with an adequate definition of the term "artificial intelligence" which isn't surprising when you consider that after thousands of years, humans can't even decide on a definition for "intelligence." Put one way, artificial intelligence is a term given to computer systems that attempt to simulate human intelligence and learning. But even that definition is too big to wrap your head around. To simplify it, you can break artificial intelligence into two categories: Strong (or broad) AI, and weak (or narrow) AI.

This is also known in leading AI research circles as Artificial Narrow Intelligence or simply ANI. Google's autonomous car is an ANI system, so are aircraft flying systems, search engine technologies, stock market systems, Japan's industrial and home robotics or Google's AlphaGO which recently beat Grandmaster Lee Sedol at the game of Go. Tech will gain this ability by acquiring the ability to read, comprehend and derive meaning intelligently from existing big data. This is Artificial General Intelligence or simply AGI.

This is part of a series on Artificial Intelligence. If you are catching it for the first time I'd recommend that you start here where I introduce the idea and provide some instrumental background. In the last article, I talked about the usefulness of thinking about artificial intelligence in its chapters. True, you could start biting this elephant anywhere and anyhow. The phases approach is just my recommended way of understanding, with better clarity, the goals and ultimate intentions of AI.

"Alone we can do so little and together we can do much" - a phrase from Helen Keller during 50's is a reflection of achievements and successful stories in real life scenarios from decades. Same thing applies with most of the cases from innovation with big impacts and with advanced technologies world. The machine Learning domain is also in the same race to make predictions and classification in a more accurate way using so called ensemble method and it is proved that ensemble modeling offers one of the most convincing way to build highly accurate predictive models. Ensemble methods are learning models that achieve performance by combining the opinions of multiple learners. Typically, an ensemble model is a supervised learning technique for combining multiple weak learners or models to produce a strong learner with the concept of Bagging and Boosting for data sampling.

Imagine the difference between a buffalo stampede and a cheeseburger. Both are tasty sources of protein. The difference lies in their requisite culinary tools. Predictive Analytics (PA) is the buffalo stampede of quantitative research: data is big, fast, and shaggy. Interactive Analytics (IA) is a cheeseburger: structured, convenient, and easy to grill.

Nick Triantos spent the past year trying to commercialize the technology being developed at SRI International, the Stanford research offshoot that helped invent the Internet and Siri, among other things. Now he has joined Ignition Partners at its new office in Los Altos, where he says he will be able to help create companies from a broader range of innovation. Triantos said his focus will be on business-focused startups, particularly ones working in artificial intelligence, cybersecurity and augmented and virtual reality. But he doesn't expect that will include startups in the currently hot space of chatbots, despite his background with voice recognition and machine learning at SRI. The following Q&A about these and other topics has been edited for length and clarity. What was your role at SRI and why are you leaving there? Unfortunately, not many people know about SRI.

Correction: Because of incorrect data initially provided in the Narrative Science report, a previous version of this article reported that 56% of survey respondents were planning to deploy AI technologies within the next two years. The study also found that a lack of data science talent is currently the biggest barrier to adopting AI, with 59% of respondents naming it as the primary obstacle to getting value out of the Big Data available to them. A recent study from Square Root showed that despite spending up to 20 hours per week collecting, analyzing and reporting on data, nearly one in three companies fail to act on their collected data. The Narrative Science study also found that 61% of the respondents who have an innovation strategy are using AI to identify opportunities in data that would be otherwise missed. "One of the major compelling differences in this year's survey compared to last year is the changing perception of AI technologies," said Stuart Frankel, CEO of Narrative Science in an an announcement.

Medical data is an essential part of any healthcare system. This valuable information is the result of hundreds of years work by various people in the medical profession and without it, new ways to diagnose patients and new cures would never be a possibility. However, this amount of data does not come without its downfalls. Having this much data is a lot to handle and is currently an area the healthcare industry is struggling to get to grips with. Most counties welcome the idea of having a global healthcare data center that will allow those in the medical profession to have instant access to previous research, diagnosis', as well as new, revolutionary breakthrough techniques from all around the globe.

Machine learning and artificial intelligence startup Turi has been acquired by Apple in a deal characterized as a blockbuster exit for the Seattle-based company, formerly known as Dato and GraphLab, GeekWire has learned. The acquisition reflects a larger push by Apple into artificial intelligence and machine learning. It also promises to further increase the Cupertino, Calif.-based company's presence in the Seattle region, where Apple has been building an engineering outpost for the past two years. "Apple buys smaller technology companies from time to time, and we generally do not discuss our purpose or plans," said Apple in a statement when contacted by GeekWire about the deal, its standard comment after making such acquisitions. Multiple sources with knowledge of the deal confirmed that Turi has been acquired.

We don't understand AI because we don't understand intelligence