Star Trek got it right: In the future, we'll use computers by talking to them. Google held an event this week and new hardware got most of the attention. Google unveiled a couple of Google-built Pixel phones, Google Wi-Fi home mesh routers, the Google Home virtual assistant appliance, a new 4k Chromecast Ultra streaming media box and the Daydream View VR headset. Critics say Google is copying and competing directly with Apple with the Pixel phones and with Amazon with Home. But this misses the point.

The Royal Bank of Scotland Group (RBS) has announced a partnership with IBM's AI platform Watson to introduce a virtual advisory service named Luvo. The pilot project, which is currently being rolled out to a select group of customers, employs Watson's Conversation technology to deliver a cloud-based chatbot service. The solution is expected to reach approximately 10% of RBS customers in Scotland by December this year, before rolling out to NatWest customers in England and Wales. Using the virtual advisory platform, customers will be able to deal with certain financial queries online – such as notifying the bank that they will be travelling abroad or updating address information. According to IBM's press release, Luvo will be able to manage these requests in seconds and direct any complex questions to a human operator.

I speculate/talk about Artificial Intelligence (the way the robots work in the movie i,Robot, Siri in your iPhone, or the AI in the Avengers: Age of Ultron) and a possible scenario which could cause AI to become self-aware and start making self decisions for it's self and us humans. I don't know, I also express my interest in the future of social interactions between humans and machines!

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.