A quick visit to the page of Boston Dynamics, a subsidiary of Google, reveals the typical, flowery PR flack pleasantry that is one of the cancers of our age. Our mission is to build the most advanced robots on Earth, with remarkable mobility, agility, dexterity and speed. Surely they're developing robots that will help mankind, right? But if you look at the videos of what they're actually developing, it's easy to see how this type of machinery could soon have frightening military and police applications. Imagine a line of these coming at you, armed with weapons.

The traditional definition of artificial intelligence is the ability of machines to execute tasks and solve problems in ways normally attributed to humans. Some tasks that we consider simple--recognizing an object in a photo, driving a car--are incredibly complex for AI. Machines can surpass us when it comes to things like playing chess, but those machines are limited by the manual nature of their programming; a 30 gadget can beat us at a board game, but it can't do--or learn to do--anything else. This is where machine learning comes in. Show millions of cat photos to a machine, and it will hone its algorithms to improve at recognizing pictures of cats.

There is no limit to the experiments people will try with machine learning, which ranges from brewing beer to screenwriting. The latest exercise comes courtesy of one intrepid man who wanted to see if AI could write a new Harry Potter story. Unfortunately, it's not something you're going to want to download to your Kindle anytime soon. Max Deutsch fed a neural network four Potter books to see what the computer could conjure up. And while it manages to make grammatical sense at times and can work in the names of familiar characters, the computer's magnum opus reads more like a Madlibs page than an actual story. Here's a sampling of the chapter the AI produced: Ron didn't even upset her little ingredients on the toilet, and a group of third-year girls last year.

The Web app, which is powered partly by artificial intelligence, analyzes posts on social media as well as police radio chatter and feeds of the local airspace in virtually any region. The software, which is linked to IBM's Watson artificial intelligence, combs through tweets and images, specific hashtags and phrases, or posts from or about a particular geographic area and then uses computer algorithms to gauge the mood of that swirling digital conversation. The AI aspects of the iAWACS app only monitor the social media posts -- they don't analyze the audio from police scanners nor from the airspace maps. The result, which the Jester said was still a work in progress, was built from the ground up for law enforcement and intelligence officials with real-time information needs.

As the country reels from the spasm of gun violence that killed two black men and five officers this week, a prominent digital vigilante is using an online tool he hacked together to keep an eye on hot spots that seem at risk of boiling over into bloodshed. The Web app, which is powered partly by artificial intelligence, analyzes posts on social media as well as police radio chatter and feeds of the local airspace in virtually any region. To detect rumblings of unrest and alert the public. On a recent night, the tool had its gaze trained on Baton Rouge, La., where protesters backed by the New Black Panther Party gathered for a rally. "I'm looking for any indication they are coordinating skirmishes. Using IBM's Watson AI, the tool not only examines large collections of tweets but -- somewhat eerily -- also can go through a single user's timeline and, with Watson's machine learning technology, offer an analysis of that user's "trustworthiness, propensity toward violence [and] openness," the Jester said. That information, he said, could hold clues to a criminal's intentions. If the Jester's name sounds familiar, that's because the hacker has appeared elsewhere -- on Time's list of most influential internet personalities, on CNN and, according to a recent blog post, on an upcoming episode of USA's "Mr.

Elon Musk is develop artificial intelligence which will enable robots that can do housework, have conversations and play games. OpenAI's mission is to build safe AI, and ensure AI's benefits are as widely and evenly distributed as possible. OpenAI will measure intelligence using a metric which consists of a variety of OpenAI Gym environments with a unified action and observation space (so a single agent can run across all of them), including games, robotics, and language-based tasks. Their implementation will evolve over time, and they'll keep the community updated along the way They are working to enable a physical robot (off-the-shelf; not manufactured by OpenAI) to perform basic housework. There are existing techniques for specific tasks, but we believe that learning algorithms can eventually be made reliable enough to create a general-purpose robot.

A core methodology at Stitch Fix is blending recommendations from machines with judgments of expert humans. Our machines produce recommendations via algorithms operating over structured data, while our human stylists curate and modify these recommendations on the basis of unstructured data and knowledge that isn't yet reflected in our dataset (e.g., new fashion trends). This helps us choose the best 5 items to offer each client in each fix. The success of this strategy within our styling organization prompts consideration of how machines and humans might be brought together in the realm of fashion design. In this post we describe one implementation of such a system.

Let's go through an example of Credit Risk for Bank Loans: Decision trees create a model that predicts the class or label based on several input features. Decision trees work by evaluating an expression containing a feature at every node and selecting a branch to the next node based on the answer. A possible decision tree for predicting Credit Risk is shown below. The feature questions are the nodes, and the answers "yes" or "no" are the branches in the tree to the child nodes. Our data is from the German Credit Data Set which classifies people described by a set of attributes as good or bad credit risks.

In the last few months the Internet has been flooded with deep dreams: images augmented by neural networks which look incredibly trippy. Deep dreams have the potential to become the new fractals; beautifully backgrounds everyone knows are related to Maths, but no one knows really how. What are deep dreams, how are they generated and what can they teach us? A neural network gets an image as an input, and returns a classification result: yes, that's a face. It achieves this by recognising features in an hierarchical fashion.

She is currently in the NYC Data Science Academy 12 week full time Data Science Bootcamp program taking place between April 11th to July 1st, 2016. This post is based on her third class project - Web Scraping, due on the 6th week of the program. Oil prices have fallen sharply since the summer of 2014. Prices bottomed in February 2016, since then they have gradually increased. While the breakeven cost is a popular topic among investors, on the consumer side gasoline prices are very cheap.