Right on cue, this past week heralded in an announcement of OpenAI, a new non-profit started by a number of tech luminaries to spearhead AI research that is publicly accessible. The motivation is that apparently these scions of capitalism lose faith in Adam Smith's invisible hand when it comes to AI R&D. Musk continues to promote the idea that AI will be humanity's largest existential threat. Challenging this view, the HBR asks if "OpenAI [is] Solving the Wrong Problem", pointing to the implied lack of trust in capitalism. This is similar to my own parry: that the biggest existential threat to humanity is humanity.

The term artificial intelligence (AI) has been around since the 1950s. However, it's only within the last five years that excitement about it has really begun to grow. This excitement is, in part, due to a new technique called'deep learning'. This is where artificial systems'learn' (in that they make and strengthen connections), through repeatedly working through large numbers of examples. The systems consist of networks of artificial'brain cells' (or neurons) that combine signals and send them between different layers within the network.

The result of standardization (or Z-score normalization) is that the features will be rescaled so that they'll have the properties of a standard normal distribution with Standardizing the features so that they are centered around 0 with a standard deviation of 1 is not only important if we are comparing measurements that have different units, but it is also a general requirement for many machine learning algorithms. Intuitively, we can think of gradient descent as a prominent example (an optimization algorithm often used in logistic regression, SVMs, perceptrons, neural networks etc.); with features being on different scales, certain weights may update faster than others since the feature values play a role in the weight updates Other intuitive examples include K-Nearest Neighbor algorithms and clustering algorithms that use, for example, Euclidean distance measures – in fact, tree-based classifier are probably the only classifiers where feature scaling doesn't make a difference. In fact, the only family of algorithms that I could think of being scale-invariant are tree-based methods. Let's take the general CART decision tree algorithm. Without going into much depth regarding information gain and impurity measures, we can think of the decision as "is feature x_i some_val?"

People will get the chance to see rarely-viewed art from the Tate's collection in a new exhibition that uses artificial intelligence (AI) to match historic paintings with modern photos. The display at Tate Britain will use cutting-edge technology to study the latest pictures from global news agency Reuters and compare them with one of 50,000 pieces in the gallery's archives based on their colours and themes. The creators of Recognition, which will run 24 hours a day online as well as in the Tate, hope the digital project will not only unearth some pieces of art that people rarely see, but also create a virtual gallery of its own. "The Tate archive is very difficult to explore, and this makes it easy," said Isaac Joseph Vellentin, 22, one of the creators of Recognition. "In our digital age, there is so much content. We are juxtaposing these images to get more out of them. We are taking two things that are close but far apart in their time frame. But we are also looking at human life. It's more what people take away for themselves."

Researchers were able to predict the results of human rights cases with 79 percent accuracy by using artificial intelligence to analyze the factual sections of published human rights judgments. The study, published in PeerJ Computer Science, found that the outcomes were best predicted by analyzing the "circumstances" section of a case--which includes factual background--along with the topics covered by the case and the language used, according to a press release. Publications covering the findings include the Wall Street Journal Law Blog, Law.com The researchers examined 584 cases before the European Court of Human Rights with a machine-learning algorithm. They found that the court's judgments were highly correlated to facts rather than legal arguments.

How to get people interested in art? How to expose permanent-collection works that sit in storage? These are questions art museums constantly ponder. Recently, Tate Britain asked another one: How can artificial intelligence help? It put the question to anyone who wanted to compete for the 2016 IK Prize, which promotes the use of digital technology in the exploration of art at Tate Britain or on the Tate website.

We have discussed some AI topics in the previous posts, and it should seem now obvious the extraordinary disruptive impact AI had over the past few years. However, what everyone is now thinking of is where AI will be in five years time. I find it useful then to describe a few emerging trends we start seeing today, as well as make few predictions around machine learning future developments. The following proposed list does not want to be either exhaustive or truth-in-stone, but it comes from a series of personal considerations that might be useful when thinking about the impact of AI on our world. Companies like Vicarious or Geometric Intelligence are working toward reducing the data burden needed to train neural networks.

Ben Dickson is a software engineer and the founder of TechTalks. While machine learning is introducing innovation and change to many sectors, it also is bringing trouble and worries to others. One of the most worrying aspects of emerging machine learning technologies is their invasiveness on user privacy. From rooting out your intimate and embarrassing secrets to imitating you, machine learning is making it hard to not only hide your identity but also keep ownership of it and prevent from being attributed to you words you haven't uttered and actions you haven't taken. Here are some of the technologies that might have been created with good-natured intent, but can also be used for evil deeds when put into the wrong hands.

Facebook's giving away servers for AI: So what does it get in return? Facebook is distributing 22 high-powered GPU servers to 15 research groups across nine European countries. Just east of the Belgium capital, Brussels, Marian Verhelst's microchip research group at the University of Leuven anxiously awaits a large shipment of supercomputing servers from Facebook's Silicon Valley headquarters. Verhelst, an assistant professor of electrical engineering at the university known as KU Leuven, plans to employ the deep-learning potential of these computers to help develop more advanced processors. As part of a global research partnership to stimulate artificial-intelligence research into deep learning, Facebook is donating 22 GPU-accelerated computer servers to researchers in nine countries across Europe.

A few months back I was making my way through the latest literature on "real time analytics" and "in stream analytics" and my blood pressure was rising. The cause was the developer-driven hyperbole that claimed that the creation of brand new insights using advanced analytics has become "real time". The issue then as now is the failure to differentiate between time-to-action and time-to-insight. Not infrequently the statements about'fast data' are accompanied by a diagram like this, which to me has a fatal flaw. The flaw, to my way of thinking, is that there are really two completely different tasks here with very different time frames.