Many modern applications deal with multi-label data, such as functional categorizations of genes, image labeling and text categorization. Classification of such data with a large number of labels and latent dependencies among them is a challenging task, and it becomes even more challenging when the data is received online and in chunks. Many of the current multi-label classification methods require a lot of time and memory, which make them infeasible for practical real-world applications. In this paper, we propose a fast linear label space dimension reduction method that transforms the labels into a reduced encoded space and trains models on the obtained pseudo labels. Additionally, it provides an analytical method to update the decoding matrix which maps the labels into the original space and is used during the test phase. Experimental results show the effectiveness of this approach in terms of running times and the prediction performance over different measures. Keywords: data stream classification, multi-label data, label compression 1. Introduction Standard classification is the task of assigning the correct class to previously unknown test instances based on training instances. Training data consist of a set of features and an associated target class or class label. Many modern data mining applications, however, need to deal with more than one label per instance.

Speaking verbally and performing sign language require the same parts of the brain, according to a new study. Researchers at New York University found that the neural skills needed to perform sign language are the similar to those required for speaking out loud. Their report is the first of its kind to prove the association between the two communication forms. Sign language communicators and verbal English speakers rely on the same neural skills, a new report says. The new research was published in the journal Scientific Reports.

According to a recent report revealed by the BBC, AI could benefit NHS healthcare officials. The report found that the amount of nurses that left the NHS last year has increased by 20% since 2013. With more than 33,000 nurses resigning from the NHS last year, this is a crisis for hospitals. The NHS is working towards resolving the issue, with artificial intelligence (AI) playing a critical role, according to the report. While NHS officials have made no mention of using AI or digital technologies as part of its recruitment and retention efforts, the UK government has indicated it has high aspirations for AI, issuing a recent report with recommendations on how the UK can become a global AI innovator. Meanwhile, we have seen healthcare officials across the UK growing increasingly interested in AI's potential benefits, including the use of cognitive agents.

Deb Roy and Rupal Patel pulled into their driveway on a fine July day in 2005 with the beaming smiles and sleep-deprived glow common to all first-time parents. Roy was an AI and robotics expert at MIT, Patel an eminent speech and language specialist at nearby Northeastern University. For years, they had been planning to amass the most extensive home-video collection ever. From the ceiling in the hallway blinked two discreet black dots, each the size of a coin. Further dots were located over the open-plan living area and the dining room. There were 25 in total throughout the house – 14 microphones and 11 fish-eye cameras, part of a system primed to launch on their return from hospital, intended to record the newborn's every move. It had begun a decade earlier in Canada – but in fact Roy had built his first robots when he was just was six years old, back in Winnipeg in the 1970s, and he'd never really stopped. As his interest turned into a career, he wondered about android brains. What would it take for the machines he made to think and talk? "I thought I could just read the literature on how kids do it, and that would give me a blueprint for building my language and learning robots," Roy told me. Over dinner one night, he boasted to Patel, who was then completing her PhD in human speech pathology, that he had already created a robot that was learning the same way kids learn.

The space race was in full swing. For the first time, a space probe had recently landed on another planet (Venus). And I was eagerly studying everything I could to do with space. Then on April 2, 1968 (May 15 in the UK), the movie 2001: A Space Odyssey was released--and I was keen to see it. So in the early summer of 1968 there I was, the first time I'd ever been in an actual cinema (yes, it was called that in the UK). I'd been dropped off for a matinee, and was pretty much the only person in the theater. And to this day, I remember sitting in a plush seat and eagerly waiting for the curtain to go up, and the movie to begin. It started with an impressive extraterrestrial sunrise. But then what was going on? Those were landscapes, and animals. I was confused, and frankly a little bored. But just when I was getting concerned, there was a bone thrown in the air that morphed into a spacecraft, and pretty soon there was a rousing waltz--and a big space station turning majestically on the screen. The next two hours had a big effect on me. It wasn't really the spacecraft (I'd seen plenty of them in books by then, and in fact made many of my own concept designs). But what was new and exciting for me in the movie was the whole atmosphere of a world full of technology--and the notion of what might be possible there, with all those bright screens doing things, and, yes, computers driving it all. It would be another year before I saw my first actual computer in real life. But those two hours in 1968 watching 2001 defined an image of what the computational future could be like, that I carried around for years. I think it was during the intermission to the movie that some seller of refreshments--perhaps charmed by a solitary kid so earnestly pondering the movie--gave me a "cinema program" about the movie. Half a century later I still have that program, complete with a food stain, and faded writing from my 8-year-old self, recording (with some misspelling) where and when I saw the movie. A lot has happened in the past 50 years, particularly in technology, and it's an interesting experience for me to watch 2001 again--and compare what it predicted with what's actually happened. Of course, some of what's actually been built over the past 50 years has been done by people like me, who were influenced in larger or smaller ways by 2001. When Wolfram Alpha was launched in 2009--showing some distinctly HAL-like characteristics--we paid a little homage to 2001 in our failure message (needless to say, one piece of notable feedback we got at the beginning was someone asking: "How did you know my name was Dave?!"). One very obvious prediction of 2001 that hasn't panned out, at least yet, is routine, luxurious space travel. But like many other things in the movie, it doesn't feel like what was predicted was off track; it's just that--50 years later--we still haven't got there yet. Well, they have lots of flat-screen displays, just like real computers today.

AI and data science has Innumerable Applications. Let's look at 100 use-cases of AI, ML and data science. We, as humans, are shaped by our experiences. Touch a hot stove element as a child, and you learn quickly never to do it again. Spin and spin with your friends on the front lawn until you fall down, to experience the feeling that they sky is spinning around you. Keep doing it over and over until you throw up, and you have learned a limitation. You may do it again, but you've learned to stop doing it sooner because of the consequences. Artificial Intelligence (AI) does not possess the amazing feedback system that has evolved on this planet for everything with a brain and sophisticated nervous system weighing more than a few grams. Instead, we have to define what constitutes success or failure for our increasingly clever AIs, and they need to assess everything they learn through a "Is this what humans would want?" filter. They fit in every industry, whether manufacturing or service, and so there is not much point in specifying how we will use it in "Real Estate" or "Biotechnology". Instead there will be many instantiations that fit multiple industries. Collecting data encompassing the accumulated wisdom of thousands of experts, an AI System could access these details to address any problem that arose on that subject. Combined Expert Systems will have a vast interdisciplinary knowledge capable of solving some of humanity's toughest questions. During an Ebola outbreak, instead of the typical two to three years of development time, one Pharma company set its AI loose on its drug molecular database and within a day it had come up with two candidates suitable for Clinical Trials. AIs discover relationships that humans beings miss, which means they will aid us in discovering essential new drugs (such as a new antibiotic, which we desperately need) or drug combination therapies to solve unique problems that arise. Britain and other countries already have AI Physician Expert Systems available to the public for a cost of about £60 per year. You can check symptoms anytime, 24 hours per day, and get practical medical advice as often as you wish, without limitation.

A couple of years ago, Zipline created a national drone delivery system to ship blood and drugs to remote medical centers in Rwanda. Now it has developed what it claims is the world's swiftest commercial delivery drone, with a top speed of 128 kilometers an hour (a hair shy of 80 miles per hour). Zipline is hoping its new fixed-wing aerial robot, which is both speedier and easier to maintain than its predecessor, will help it win business in an industry that's attracted plenty of big players. They include Amazon, which has been testing its Prime Air drone delivery service for years in the UK and elsewhere, and Project Wing, part of Alphabet's secretive X lab, which is using its drones to deliver pharmaceuticals and burritos in a pilot project in Australia. Soon these and other companies will be able to experiment more in America, too.

Digital transformation is a fact of every business today. While the transformation of business has been well documented, it is less clear how technology, and what technologies are going to drive this process. Recently, research from Grant Thorton, the world's fifth largest professional services network of independent accounting and consulting member firms based in the London, UK, has quantified how much digital transformation will cost, how it will impact financially on enterprises and what the ultimate shape of digital enterprises will be. The research, which was published at the end of February, surveyed 304 CFOs and other senior financial leaders, from companies with revenues between $100 million and over $20 billion. Overall, more than three quarters of the executives surveyed agreed that digital transformation is critical, 23 percent in the short-term and 56% in the long-term.

Standard touchscreen interfaces need an algorithmic makeover to improve accessibility for those with physical and mental impairments, new research suggests. And a team of researchers from Kochi University of Technology, Japan, and Aalto University, Finland, have thrown down the gauntlet to designers to tap into the new artificial intelligence (AI) inspired model they have developed to offer solutions to the limitations a'one size fits all' interface creates. The model is designed to enable people with challenges like dyslexia, Alzheimer's, or tremors, effectively interact with their technology. And in a demonstration the AI model was used to'simulate a user with essential tremor' – which found that the Qwerty keyboard on the smartphone wasn't fit for purpose. "After this prediction, we connected the text entry model to an optimizer, which iterates through thousands of different user interface designs. No real user could of course try out all these designs. For this reason it is important that we could automatise the evaluation with our computational model," said Jussi Jokinen, postdoctoral researcher at Aalto University.

French President Emmanuel Macron has committed to investing $1.8 billion in artificial intelligence over the next four years. The spending plan will target the healthcare sector and is accompanied by a commitment to open up French data. Macron discussed the strategy following the release of a report (PDF) from a fellow French politician that sketched out an AI strategy for France and Europe. The report called for France to make health a cornerstone of its AI policy. Macron echoed the position in an interview with WiRED, in which he said healthcare is the field that drove home the potential of AI to him.