He pulls [a notebook] down--it's from the late 1950s. Ever since he was a teenager, he has captured some 10,000 examples of swapped syllables ("hypodeemic nerdle"), malapropisms ("runs the gambit"), "malaphors" ("easy-go-lucky"), and so on, about half of them committed by Hofstadter himself. He makes photocopies of his notebook pages, cuts them up with scissors, and stores the errors in filing cabinets and labeled boxes around his study.

Last of blog series on global-data-center & Artificial Intelligence – human side of story: How AI evolved to be potential threats to humans and how we should live in harmony for the bright future.

The answers are available now, thanks to technologies like Artificial Intelligence (AI) and Advanced Analytics (AA). Armed with the new technologies, businesses are moving from Descriptive to Predictive and Prescriptive Analytics.

Erica, a 23-year-old Japanese robot, is all set to make her debut as a news anchor in April 2018. The robot, which looks remarkably like a young woman, was created by Hiroshi Ishiguro, the director of the Intelligent Robotics Laboratory at Osaka University, in 2014. In a documentary conducted by the Guardian on Erica in December, Ishiguro described his creation as "the most beautiful and most human-like autonomous android in this world." However, there is still a long way to go before Erica can move and walk around like a human. At the moment, the robot's arms and legs are not functional.

So linear models definitely do, and they're at least as bad as with neural nets. For KNN with k 1, it definitely wouldn't have adversarial examples. Assuming all distinct data points are separated by more than epsilon, perturbations smaller than epsilon shouldn't change the nearest neighbor and hence shouldn't change the classification. SVMs also might not have adversarial examples, but I'm less certain.

India has the potential to be a global leader if it makes the right investments now in the technologies of the future including artificial intelligence, machine learning and blockchain, the Economic Survey 2017-18 said on Monday. Calling for greater focus and investment on R&D in science and technology, the Survey suggested a mission-driven approach that could have a huge impact on Indian society and growth. It suggested six key areas that can be taken up as national missions to promote India's R&D capabilities. One of these is the National Mission on Cyber Physical Systems. "This is hugely multidisciplinary area including deep mathematics used in artificial intelligence, machine learning, big data analytics, block chains, expert systems, contextual learning going to integration of all of these with intelligent materials and machines, control systems, sensors and actuators, robotics and smart manufacturing," the Survey noted.

How did Google get Clips, its AI-powered camera, to learn to automatically take the best shots of users and their families? Well, as the company explains in a new blog post, its engineers went to the professionals -- hiring "a documentary filmmaker, a photojournalist, and a fine arts photographer" to produce visual data to train the neural network powering the camera. The blog post explains this process in a little more detail, but it's basically what you'd expect for this sort of AI. In order for the software to recognize what makes a good or a bad photo, it had to be fed lots of examples. The programmers thought about not only obvious markers (eg, it's a bad photo if there is blurring or if something's covering the lens) but also more abstract criteria, such as "time" -- training Clips with the rule, "Don't go too long without capturing something."

Imagine predicting the emotion of a tweet without providing any training examples of tweets with that emotion label.This research discusses the paradigm of Zero-shot learning for Text Classification and the paper is aptly titled as "Train Once, Test Anywhere: Zero-shot Learning For Text Classification". You can read the paper here or try a demo here.

Leading experts in artificial intelligence (AI) debated whether machines can ever be truly creative during an event at Imperial College London. The panel debate was part of the Night of Ideas, a programme of free debates exploring the latest ideas behind issues central to our times organised by the Institut Français. Academics from Imperial and other London institutions, were joined by a director from Spotify to talk about their latest research involving AI and discussed the creativity potential of computer software. The experts debated how developments in AI were enabling machines to produce music and paintings but questioned whether this meant they were being truly creative and should be recognised as artists in their own right. Dr Aldo Faisal, from the Department of Bioengineering and Department of Computing, gave his thoughts on what is powering the AI revolution.