Unless you've been living under a rock for the last few years, you have probably heard of TV shows such as Breaking Bad, Mad Men, How I Met Your Mother or Game of Thrones. While I generally don't spend a whole lot of time watching TV, I have also undergone some pretty intense binge-watching sessions in the past (they generally coincided with exam periods, which was actually not a coincidence…). As I was watching the epic final season of Breaking Bad, it got me thinking on how TV series compare to one another, and how their ratings evolve over time. I therefore decided to look a bit further into user rating trends of popular TV series (and by popular I mean the ones I know). For this, I simply had to define a quick scraping function in R that retrieves the average IMDB user ratings assigned to each episode of a given series.

Artificial intelligence is already powering your Google searches, your Netflix recommendations, and your smartphone's virtual assistant. It is playing humans at complex, intuitive games like Go, and it is beating them. Now, researchers say, they want AI to power your doctor's diagnoses, your drug prescriptions, and your smartphone's virtual psychologist. They want AI to perform tasks that radiologists do, and at least match them. Machine learning has made tremendous strides in the last decade, becoming one of the fastest-growing, most-hyped areas of computer science.

AUTONOMOUS cars are just around the corner. Cities across the world are rolling out pilots of driverless vehicles, and soon motorists in Germany will be able to relax on the autobahn as their cars drive them from Munich to Berlin (see "London is set for driverless car roll-out – so what comes next?"). In other words, we are on the brink of a transport revolution as potentially radical as the one that began in 1908 with the Model T Ford. By 1931 the automobile's transformative power was so clear that Aldous Huxley imagined the people of his Brave New World worshipping Henry Ford as the creator of their dystopian society. Huxley was on to something. The Ford revolution changed Western society.

BigML Spring 2016 release is here! GMT 02:00) for a FREE live webinar to learn about the latest and greatest version of BigML. We'll be focusing exclusively on WhizzML, a new domain-specific language that lets you automate Machine Learning workflows, implement high-level Machine Learning algorithms, and share them with others. WhizzML stands to make a big difference not only in how developers conceive of and implement smart applications, but also how analysts and scientists reduce the burden of repetitive analyses.

Introduction to deep learning based on Google's TensorFlow framework. These tutorials are direct ports of Newmu's Theano Tutorials.

DeepMind, Google's AI lab in London, is well known for creating an algorithm that beat the best human in the world at Chinese board game Go. It's also been in the news this month for the controversial work it's doing with the NHS in healthcare. But DeepMind is understood to have a collection of other projects on the go that no one knows about. The research-intensive organisation, which employs around 250 people in a discreet building in King's Cross, writes on its website that it is building self-learning algorithms that can complete a wide variety of tasks straight out of the box. The company, which was backed by PayPal billionaire Elon Musk in its early days, also writes on its website that it wants to "solve intelligence" to "make the world a better place."

Computers do what we tell them to do. Any talk of computers doing things they weren't programmed to do is only a way of speaking. It's a convenient shorthand when used properly, misleading mysticism when used improperly. But of course the computer was programmed to print the number 168. It just wasn't directly programmed to do so.

Emails won't literally get sucked down a vortex of analytics solutions. If you have ever bought something from an online store, chances are the store's used your email address with wanton disregard, bombarding you with email after email about its products and sales until you reach for the sweet oblivion of unsubscription. Stores and brands do this to keep customers engaged--but they don't know how many emails are too many. Adobe previewed a tool today with the promise to help alleviate what they call "customer fatigue." By using machine learning algorithms to crunch the numbers of how often emails are opened and clicked on, marketers can see whether customers are tired of getting their emails.

This image, taken from a YouTube video, shows a pilot project at Walgreens to provide 3D views of goods sought by a shopper in a store. The technology comes from Google's Project Tango and Aisle411, a shopping location application maker. A new partnership between Google and chip maker Movidius could change the way your phone looks at the world. According to Movidius, Google will be licensing Movidius' latest flagship chip, the MA2450, along with the accompanying software stack. The chip has 12 cores, and is able to perform low power, advanced computer vision processing.

Logistic Regression is one of the most powerful classification methods within machine learning and can be used for a wide variety of tasks. Think of pre-policing or predictive analytics in health; it can be used to aid tuberculosis patients, aid breast cancer diagnosis, etc. Think of modeling urban growth, analysing mortgage pre-payments and defaults, forecasting the direction and strength of stock market movement, and even predicting sport outcomes. Reading all of this, the theory[1] of Maximum Entropy Classification might look difficult. In my experience, the average Developer does not believe they can design a proper Maximum Entropy / Logistic Regression Classifier from scratch. I strongly disagree: not only is the mathematics behind is relatively simple, it can also be implemented with a few lines of code.