This month the US President's National Science and Technology Council [NSTC] issued an important report on'Preparing for the Future of Artificial Intelligence'. It is not its final word on the matter - another Report is due on the effect of AI-driven automation on jobs and the economy but the general tone is, as you would expect in a technologically-driven culture, cautiously positive about AI's contribution to economic growth (at least in the US). The undertone though is one of concern about social cohesion and fairness and, above all, about appropriate regulatory regimes. The Report is a good corrective to some of the speculative fantasies about AI. Although nothing is entirely certain in this field, it pushes back AI that matches or exceeds human intelligence beyond the next five Presidential terms.

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. This is a reminder that while we further delve into the seemingly countless possibilities of this exciting new technology, we should keep our eyes open for the repercussions and unwanted side-effects.

Following are the White House's strategy recommendations for dealing with artificial intelligence: Strategy 1: Make long-term investments in AI research. Prioritize investments in the next generation of AI that will drive discovery and insight and enable the United States to remain a world leader in AI. less Following are the White House's strategy recommendations for dealing with artificial intelligence: Strategy 1: Make long-term investments in AI research. Strategy 2: Develop effective methods for human-AI collaboration. Rather than replace humans, most AI systems will collaborate with humans to achieve optimal performance. Research is needed to create effective interactions between humans and AI systems.

Virtual assistants - such as the iPhone's Siri and Microsoft's Cortana - are included on smartphones and computers to make life easier, allowing users to issue commands, such as "call mum", or easily search for nearby facilities such as restaurants and petrol stations. But Eckstein believes five per cent of interactions with his company's chatbot are now sexually explicit. He also claims a third of conversations take place for no particular reason, with many users just wanting to chat. Deborah Harrison, a writer for Microsoft's Cortana, told the Virtual Assistant Summit earlier this year that "a good chunk of the volume of early-on inquiries" were about the chatbot's sex life. The issue has inspired films such as Her, which was released in 2013 and tells the tale of a lonely writer, who becomes obsessed with his operating system's virtual assistant, voiced by Scarlett Johansson.

ALIAS can fly a military helicopter and then move into another aircraft and fly that too-- and ALIAS is not human. Driverless cars may have been making headlines of late, but DARPA's ALIAS program has also been making great strides in the development of "digital pilot" technology. The brainchild of the legendary institution DARPA (the Defense Advanced Research Projects Agency), ALIAS easily drops into an aircraft and becomes an invisible, automated co-pilot for a human pilot. And ALIAS is so good that it has the potential to eventually fly all sorts of military aircraft on its own-- and it could even fly commercial jets like the ones Americans take to visit family or go on vacation. Two teams are currently joining forces with DARPA to make ALIAS a reality: Aurora Flight Sciences and Lockheed Martin Sikorsky.

Results will be integrated into the above repository eventually. The Recurrent Networks take as input a 3D Tensor batch_size x seq_length x hidden_size and output the last hidden state, compute a MSE loss and compute the gradients of error with respect to each parameter. The hidden_size specifies the size of the output and input layer of the networks. The code of the scripts we ran are available. The code for the regular theano RNN implementations were borrowed from the rnn-benchmarks repository.

Vine, the six-second video app, is shutting down completely. Owner Twitter has decided to discontinue the mobile app, apparently as part of its plan to rescue itself from its ongoing crisis. The app and its looping, six-second videos helped define some of the aesthetic of the videos that now flood social networks like Facebook. And it helped launch a range of new stars, many of whom have now branched out into other places like Snapchat. An employee shows a Samsung Electronics' Gear S3 Classic during Korea Electronics Show 2016 in Seoul, South Korea Visitors experience Samsung Electronics' Gear VR during the Korea Electronics Grand Fair at an exhibition hall in Seoul, South Korea Amy Rimmer, Research Engineer at Jaguar Land Rover, demonstrates the car manufacturer's Advanced Highway Assist in a Range Rover, which drives the vehicle, overtakes and can detect vehicles in the blind spot, during the first demonstrations of the UK Autodrive Project at HORIBA MIRA Proving Ground in Nuneaton, Warwickshire Chris Burbridge, Autonomous Driving Software Engineer for Tata Motors European Technical Centre, demonstrates the car manufacturer's GLOSA V2X functionality, which is connected to the traffic lights and shares information with the driver, during the first demonstrations of the UK Autodrive Project at HORIBA MIRA Proving Ground in Nuneaton, Warwickshire In its facilities, JAXA develop satellites and analyse their observation data, train astronauts for utilization in the Japanese Experiment Module'Kibo' of the International Space Station (ISS) and develop launch vehicles The robot developed by Seed Solutions sings and dances to the music during the Japan Robot Week 2016 at Tokyo Big Sight.

This is a guest repost by Jacob Joseph from CleverTap. Why would you want to convert a numerical variable into categorical one? Depending on the situation, it can lead to a better interpretation of the numerical variable, quick segmentation or just an additional feature for building your predictive model by creating bins for the numerical variable. Binning is a popular feature engineering technique. Suppose your hypothesis is that the age of a customer is correlated with their tendency to interact with a mobile app.

Join us November 8th in the Cyril Magnin 2 room at 2:15 pm on for a panel discussion on how advances in machine learning, deep learning and neural networks are transforming data management and decision making in almost every industry sector from retail to security and healthcare. These experts will discuss real world implementation challenges.

Judging others' personalities is an essential skill in successful social living, as personality is a key driver behind people's interactions, behaviors, and emotions. Although accurate personality judgments stem from social-cognitive skills, developments in machine learning show that computer models can also make valid judgments. This study compares the accuracy of human and computer-based personality judgments, using a sample of 86,220 volunteers who completed a 100-item personality questionnaire. We show that (i) computer predictions based on a generic digital footprint (Facebook Likes) are more accurate (r 0.56) than those made by the participants' Facebook friends using a personality questionnaire (r 0.49); (ii) computer models show higher interjudge agreement; and (iii) computer personality judgments have higher external validity when predicting life outcomes such as substance use, political attitudes, and physical health; for some outcomes, they even outperform the self-rated personality scores. Computers outpacing humans in personality judgment presents significant opportunities and challenges in the areas of psychological assessment, marketing, and privacy.