Take a look around and you'll see evidence of the widespread adoption of wearable sensors for health and fitness, such as the Fitbit, Garmin or other devices. What many people may not know is that we are also using sensors to monitor the structural integrity of bridges and buildings, as well as tracking the movements of insects and other animals. With the rapid growth of the Internet of Things (IoT), tens of billions of sensor devices are projected to connect in the next decade. These connected sensor devices will automate processes across a broad range of economic sectors, from industrial plants to healthcare management, delivering productivity gains and hopefully quality-of-life improvements. The core of these sensor devices that will be deployed across this broad range of applications is largely the same, featuring a microprocessor, memory and a wired or wireless communication interface to the internet, along with a battery or other energy source.

If you've seen one too many mediocre sci-fi movies, the phrase'artificial intelligence' might bring to mind images of evil robots and technology taking over the world. In reality, AI is not a Hollywood concept, but a timely and practical tool for marketers. Essentially, we're talking about AI in the context of a technology that aims to solve a specific problem, one that uses datasets in order to learn and replicate information and behaviours. With AI playing an increasing role in all our lives, our Marketing in the Age of Artificial Intelligence report predicts the various ways it will continue to impact consumers. As well as making things more complicated for marketers, the appearance of multiple social channels has led to a non-linear and fractured path to purchase.

The concept of artificial intelligence, or AI, has existed for centuries, even if the phrase itself wasn't coined until 1956. The idea that humans could create something capable of thought processes similar to, or even superior to, their own, existed with the ancient Greeks and has extended through the millennia. The concept ramped up significantly in the 1950s, though computer memory and construction limitations prevented significant breakthroughs from occurring. Science fiction novels and films began foreseeing an ominous future, and recently, AI applications have started infiltrating our world. More recent years have seen some interest spikes: Deep Blue, a chess-playing supercomputer, defeated Garry Kasparov in 1997, and IBM's Watson destroyed its human competition in Jeopardy! in 2011.

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

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

Expedia has grown far beyond a search engine for flights -- it's now the parent company of a dozen travel brands including Trivago and Hotels.com The business of delivering quality flight search results is tough, and Fleischman describes it as an "unbounded computer science problem". The reason for this is because flight itineraries and schedules are constantly changing, and Expedia's proprietary'best fare search' (BFS) has to'learn' and adapt all the time. The extent of the problem can be summed up by one statistic. In those three seconds you will see, on average, 16,000 flight options, in order of convenience or price or time.

Tree methods are commonly used in data science to understand patterns within data and to build predictive models. The term Tree Methods covers a variety of techniques with different levels of complexity but my aim is to highlight three I find useful. To set the problem up let's assume we have a census dataset containing age, education, employment status and so on. Given all this information we want to see if we can predict whether a person earns more than 50k per year. How can tree methods help us?

It's a common myth that the PDF is the final format of a document, where the data and content can only be rendered on a screen or printed on paper. This is far from correct and we will explain why. John Warnock had a vision. "This project's goal is to solve a fundamental problem that confronts today's companies. The problem is concerned with our ability to communicate visual material between different computer applications and systems."

This video is the introductory'explainer', to be followed by other animations in due course.

He and his team are focused on optimizing C2FO's capital markets through applied machine learning and developing contemporary quantitative risk management systems. Piero also enjoys teaching, rowing, and hacking on open data. You can find him on Twitter and LinkedIn. Geospatial analysis is a massive field with a rich history. Python has some pretty slick packages for working with geospatial data such as, but not limited to, Shapeley, Fiona, and Descartes.