I am running on Jupyter Notebooks with a Mac and Python 2.7. I can get the data imported and can even "print data", the following comes through. It looks like I'm picking up the first row which is unlike the tutorial. ValueError Traceback (most recent call last) in () ---- 1 number_passengers np.size(data[0::,1].astype(np.float)) The next code seems to be where it turns into a problem because I'm trying to convert the headers into a float.

It does not take an infinite number of monkeys to type a passage of Shakespeare. Instead, it takes a single monkey equipped with brain-sensing technology - and a cheat sheet. That technology, developed by Stanford Bio-X scientists Krishna Shenoy, a professor of electrical engineering at Stanford, and postdoctoral fellow Paul Nuyujukian, directly reads brain signals to drive a cursor moving over a keyboard. In an experiment conducted with monkeys, the animals were able to transcribe passages from the New York Times and Hamlet at a rate of up to 12 words per minute. Earlier versions of the technology have already been tested successfully in people with paralysis, but the typing was slow and imprecise. This latest work tests improvements to the speed and accuracy of the technology that interprets brain signals and drives the cursor.

This is the second blog posts on the reinforcement learning. In this project we will demonstrate how to use the Deep Deterministic Policy Gradient algorithm (DDPG) with Keras together to play TORCS (The Open Racing Car Simulator), a very interesting AI racing game and research platform. As a typical child growing up in Hong Kong, I do like watching cartoon movies. One of my favorite movies is called GPX Cyber Formula. It is an anime series about Formula racing in the future, a time when the race cars are equipped with super-intelligent AI computer system called "Cyber Systems".

Apple war has reached a turning point with the inclusion of the Google's new Pixel phone. The Silicon Valley giant is making waves in the mobile industry with its patented smartphone that blows artificial intelligence assistant Siri out of the water. In a showdown of giants, it seems Google is chalking up this round under their bedpost, as Apple scrambles to get the iPhone 7 back into the mainstream's top of mind. Pixel's main champion in the ring is not the hardware or the design, which by today's standards is nothing new or innovative. Anyone with the right tools can make a new phone, but the playing field evens out with the software add-ons they include in the package.

Donald Trump's newest challenger is a twitterbot that was trained by Trump himself. DeepDrumpf, a chatbot trained on Trump's own words, recently announced its run for president and launched a GoFundMe for campaign donations, all of which will go toward supporting girls in STEM studies. DeepDrumpf was created in March by Brad Hayes, an MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) postdoc with a penchant for politics and neural networks. Back then, the Republican primaries were in full swing and Trump had established himself as a contender. "Trump's style of speech lends itself extremely well to these types of generative machine learning models."

Artificial intelligence (AI) is becoming very real--and at an exponentially faster rate. Moreover, those organizations that leverage AI in sync with those Hard Trends and Soft Trends that are shaping the future stand to make the most of its extraordinary potential. On one level, artificial intelligence is poised to help anticipate and address such critical issues as cybersecurity, civil unrest and even outright acts of terrorism. For example, using technology such as automated smart detection, officials at the recent Olympics in Rio were successful in maintaining security in a wide array of venues and locations. Closer to home, the Central Intelligence Agency's deputy director for digital innovation Andrew Hallman recently addressed the issue of anticipatory intelligence at an event hosted by the government and technology website NextGov.

An important goal for us is to give you as accurate an ETA as possible. When you request a car and we tell you it's going to be 14 minutes or 12 minutes before it shows up, we want to make sure that that estimate is as precise as it can be. We gather information from millions of trips, because we know exactly how long it took for the car to come to you for each trip. We basically use data to build models that estimate the time it will typically take for the car to reach you at any given time of the day, any given time of the week. That is better than any attempt to compute the route and say, "It's going to take the car seven minutes to get to you."

"Machine learning is the field of study that gives computers the ability to learn without being explicitly programmed" Arthur Smauel. We can think of machine learning as approach to automate tasks like predictions or modelling. For example, consider an email spam filter system, instead of having programmers manually looking at the emails and coming up with spam rules. We can use a machine learning algorithm and feed it input data (emails) and it will automatically discover rules that are powerful enough to distinguish spam emails. Machine learning is used in many application nowadays like spam detection in emails or movie recommendation systems that tells you movies that you might like based on your viewing history.

Data Scientists across Skyscanner have started meeting every fortnight to discuss research papers that tackle similar problems to those that we face within Skyscanner. The 2nd paper we read was: "Mining Large Streams of User Data for Personalized Recommendations" (hi Xavier!). Just like the last post, we're we're also writing up a brief, non-technical overview the problems/opportunities we discussed. Netflix famously announced a 1M prize in 2006, calling on researchers across the world to improve their movie recommender system by 10%. To create this competition, they had to make a critical decision: how could Netflix measure a 10% improvement in their system?

Robots aren't taking our jobs, but artificial intelligence is making it easier than ever to do them. "Amy" saves entrepreneur Gillian Morris about 43 productive hours a year. Morris, the founder of Hitlist, a travel app that alerts users to cheap flights, has been using Amy, a virtual assistant from x.ai for about two years, to schedule meetings. To ask for Amy's help, Morris sends an email to the person or people she wants to meet with and copies Amy. From there, Amy takes Morris out of the email chain and handles the back and forth about dates and times.