As the automation of work processes continues apace by means of artificial intelligence, what does this mean for our jobs? Research carried out by McKinsey, analysing around 2,000 individual work activities, suggests that, rather than eliminating jobs altogether, automation will take over discrete tasks and thus redefine current roles – at least in the short term. Here are McKinsey's four fundamentals of workplace automation: Almost half of the activities individuals are paid to perform could be automated using currently available technologies. Narrative Science's artificial intelligence system, Quill, for example, can analyse raw data and write reports in natural language that readers would assume had a human author – all in a matter of seconds. In short, automation is likely to change the vast majority of occupations – at least to some extent.

To find out just how advanced our current AI systems are, researchers have developed a tougher Turing Test - called the Winograd Schema Challenge - which measures how well robotic intelligence matches human intelligence. In the end, the team found that - even though AI is definitely improving every day - our robotic pals are still seriously lacking some common sense, suggesting that it will be some time before AI is fully ready to meld with society. First, before we go any further into the new competition's results, it's important to define what a'Turing Test' actually is. Developed and coined by Alan Turing back in the 1950s, the Turing Test is a way for researchers to challenge computer-based intelligence to see if it can become indistinguishable from human intelligence, which is basically the goal for AI researchers. These tests are mostly language-based because human language is - when you truly think about it - super weird.

Basho Technologies announced the open sourcing of Riak TS 1.3. Riak TS is specifically geared for handling time series data – it supports fast write and query for time series data. In addition, Riak TS supports Data Aggregators and Arithmetic operations, integration with Apache Spark via the Spark connector, Client support for Java, Erlang and Python, and standard SQL based Query system. Riak TS 1.3 EE (Enterprise Edition) built on the open source version supports Multi-cluster Replication. A comprehensive list of features is listed in the release notes.

MNIST is the "Hello World" of machine learning. With this tutorial, you can train on MNIST with a single-layer neural network. By applying a more complex algorithm, Lenet, to MNIST, you can achieve 99 percent accuracy. Lenet is a deep convolutional network. Word2vec is a popular natural-language processing algorithm capable of creating neural word embeddings, which in turn can be fed into deeper neural networks.

After decades of stagnation in research and development of artificial intelligence solutions, machine learning has blossomed again – and its seeds are spreading to financial services. Sofia Lotto Persio reports on how AI is improving the field. Almost 20 years later, in March 2016, Google's AlphaGo programme beat South Korean champion Lee Sedol at four matches of Go, a strategy game, in what represents another historic breakthrough for artificial intelligence (AI). In the two-decade span between these achievements, AI has progressed tremendously. Go is a much more complex game than chess, as there are more possible positions on the board than there are atoms in the universe.

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.