Even Goldman Sachs has entered the race for data, leading a 15m investment round in Kensho, which stockpiles data around major world events and lets clients apply the lessons it learns to new situations. Say there's a hurricane striking the Gulf of Mexico: Kensho might have ideas on what this means for US jobs data six months afterwards, and how that affects the S&P stock index. Many businesses are using computing firepower to supercharge old techniques. Hedge funds such as Winton Capital already collate obscure data sets such as wheat prices going back nearly 1,000 years, in the hope of finding patterns that will inform the future value of commodities. Others are paying companies such as Planet Labs to monitor crops via satellite almost in real time, offering a hint of the yields to come.

Large Scale Hierarchical Text Classification is a document classification challenge to classify a given Wikipedia document into one of the 325,056 categories. Wikipedia has created this very large dataset. The dataset is multi-class, multi-label and hierarchical. The numbers of categories were somewhere around 325,000 and the numbers documents size is 2,400,000. This challenge builds upon a series of successful challenges on large-scale hierarchical text classification. Demokritos will give more information on this dataset at http://lshtc.iit.demokritos.gr/

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Research into Artificial Intelligence will evolve into many more applications than asking Amazon's Echo how many teaspoons are in a tablespoon. As the technology expands in its capabilities and applications, we'll be confronted with massive social change. How will Apple, for example, both serve us and meet competitive challenges? "Siri and Apple's Machine Learning Are About to Get a Lot Better." Author Levy was given an inside look at what Apple is doing with machine learning and the transformation of Siri.

In the early 1970s, Hannah Arendt wrote a devastating critique of the Pentagon's Vietnam-era penchant for policy by counting. "The problem-solvers did not judge," she wrote. Exuding the spirit of gamblers rather than statesmen, the decision-makers played "the percentage game", counting whatever could be counted and ignoring the rest, or the underlying problems, with "an utterly irrational confidence in the calculability of reality". With artificial intelligence and machine learning, technologies that are fast becoming very significant actors, "we are in another moment of irrational confidence", says renowned technology and culture researcher Kate Crawford. Aiming at population-level predictive gambles, these technologies filter who and what counts, including "who is released from jail, what kind of treatment you'll get in hospital, the very news that you see".

The robots of the world are uniting – and that's either a great thing or a terrifying thing depending on your view. Google has a plan to speed up robotic learning, and it involves getting robots to share their experiences – via the cloud – and collectively improve their capabilities – via deep learning. Sergey Levine from the Google Brain team, along with collaborators from Alphabet subsidiaries DeepMind and GoogleX, published a blog post on Monday describing an approach for "general-purpose skill learning across multiple robots." Teaching robots how to do even the most basic tasks in real world settings such as homes and offices has vexed roboticists for decades. To tackle this challenge, the Google researchers decided to combine two recent technology advances.

She is the head of HR for one of the largest banks in the world. She has a difficult mandate to execute. She has had to shut down a branch in a small city because that branch is no longer profitable. Many of the jobs in the bank that were once being done by humans have been handed over to robots or "bots" as they are called. "Is Artificial Intelligence a job killer", I ask her.

The diagram above uses synthetic data to show how a threshold classifier works. As you can see, picking a threshold requires some tradeoffs. Too low, and the bank gives loans to many people who default. Too high, and many people who deserve a loan won't get one. So what is the best threshold?

When Uber chose to test its robot-driven taxis in Pittsburgh, some may have wondered why the tech company had chosen America's former capital of steel for its road test into the future. But for those in the know, Pennsylvania's second city is well on its way to establishing itself as the Silicon Valley of the east – and even its roads are helping. Unlike many American cities, Pittsburgh road system is literally off the grid, its origins dating back to twisty, pre-revolution forest trails. Then there are the city's 446 bridges to navigate. More importantly Pittsburgh boasts the robotics department at Carnegie-Mellon University, recognized as the leading academic institution in the field. It was here last month that Pittsburgh opened its doors to show the world why it is so well positioned to be a new tech hub.

It should probably come as no surprise that artificial intelligence was absent from the first presidential debate. AI hasn't made much of a splash in this election cycle, and the subject matter is notoriously confusing. Even so, given some of the headline-grabbing developments in autonomous vehicle technology, one might expect a greater focus on this issue -- especially as ongoing advancements herald significant changes to American life. Uber is now deploying autonomous vehicles on the roads of Pittsburgh. Tesla's Autopilot is being implicated in a recent spat of roadway deaths.