Learned experts in the field of science and technology have had predicted how'Artificial Intelligence' may take over the world. On close heels, a Russian robot appears to have'escaped' the research lab in which it was housed. Do you know what's more weird? It is the second time the bot has tried to flee the lab in just a span of two weeks, according to news reports. Engineers at the Russian lab reprogrammed the intelligent machine, dubbed Promobot IR77, after last week's incident, but the robot recently made a second escape attempt, The Mirror reported. Reportedly, in its first effort to flee last week, the robot almost made 50 meters into the street before it got'partially paralyzed' due to power exhaustion.

Two years ago, our ambitious team of data scientists, engineers and visualisation experts set out to tackle the challenging problem of interconnecting the entire universe of research. Using this incredibly powerful knowledge graph, we aimed to provide breakthrough insights about the past and present of research, and by applying predictive techniques we sought to outline the future of research at a global scale. Using big data analytics, machine learning and artificial intelligence, our team worked determinedly for two years piecing together the world's most comprehensive and continuously updating knowledge graph. Today, we are excited to introduce wizdom.ai, Our goal is to utilise this powerful research graph, representing the collective knowledge of human civilisation to answer the most fundamental questions for researchers, research institutions, publishers, funding organisations, businesses and governments – explore the extensive range of questions addressed by our team on the wizdom.ai

Since we moved away from chat a few days ago, we've had a lot of users come and ask us why we decided to bring chat to a bare minimum in our app. While several of our users loved the chat feature, for a lot of our users chat was a cumbersome way of doing things (too many taps, easier if they did it themselves etc.). After extensive debate internally, we took the call to phase out chat from our app and create the same level of experience using automation and simple user interfaces. The rest of the app remains the same – but it is simplified, fast and without any delays. Since a lot of people believe that chat is the new universal UI (like we once passionately believed), I thought it might be useful to talk about our experience and learning.

It's been just about six months since IBM closed its acquisition of The Weather Company, but it's not resting on its laurels. This week Big Blue moved to leverage The Weather Company's go-to-market strength to launch Deep Thunder, a machine learning-driven weather model developed by IBM Research to help industries ranging from aviation and agriculture to retail better predict the business impact of weather. "One of the greatest things about being part of IBM is having a relationship with IBM's Research arm," says Mary Glackin, head of Science & Forecast Operations for The Weather Company. The Weather Company is actually merging its existing Rapid Precision Mesoscale (RPM) model -- a numerical weather prediction system based on the Advanced Research Weather Research and Forecast System (WRF-ARW) -- with Deep Thunder. RPM generates forecasts up to 24 hours ahead, with updates every three hours in the U.S. and every six hours outside the U.S. Precipitation forecasts are calculated from half-hourly instantaneous precipitation forecasts provided by RPM.

Machine learning, and principally deep learning, is an area of intense interest in computer science today. Tech giants including (but certainly not limited to) Google, Facebook, Baidu, IBM, Microsoft are spending an enormous amount of money and effort to hire the best machine learning researchers. Deep learning has outperformed traditional computer vision (CV) technology in recent years. In the 2010 ImageNet Challenge, the best traditional CV algorithm had an error rate of 28.2% which meant that it got about 72 out of 100 images correct. In 2011, the best algorithm clocked in at 25.8% error rate.

Many people today can't resist snapping beautifully artistic photos of their meals, from the simple morning smoothie to that deliciously sinful sticky toffee pudding. But what if you could instantly find out how many calories you're about to consume as well? Boston-based startup AVA has launched an "intelligent eating" service that allows you to take a photo of your meal, send it to AVA via text and instantly receive nutritional and caloric information about your grub with the help of artificial intelligence and nutritionists. "We wanted to provide an easier way for people to track what they're eating and provide them with really personalised recommendations from a health coach based on what their specific needs are," co-founder and CMO of AVA, Jeanne Connon told IBTimes UK. "AVA uses artificial intelligence to assist nutritionists in estimating calories as well as making recommendations, factoring in historical eating habits, diet patterns, location and behavioural analysis against a database of roughly 50,000 meals." The team has not disclosed exactly how the AI-powered technology works since the service is still in private beta mode.

Facebook today is publishing an academic paper and a blog post detailing Torchnet, a new piece of open-source software that's designed to streamline deep learning, a type of artificial intelligence. Deep learning is a trendy approach that involves training artificial neural networks on lots of data, like photos, and then getting the neural networks to make predictions about new data. Rather than build a completely new deep learning framework, of which there are many, Facebook chose to build on top of Torch, an open-source library to which Facebook has previously contributed. "It makes it really easy to, for instance, completely hide the costs for I/O [input/output], which is something that a lot of people need if you want to train a practical large-scale deep learning system," Laurens van der Maaten, a research scientist in Facebook's Artificial Intelligence Research (FAIR) lab, told VentureBeat in an interview. Torchnet, which is written in Lua and can run on standard x86 chips or graphics processing units (GPUs), also lets programmers reuse certain code, which means doing less work and lowering the chances of introducing bugs, said van der Maaten.

The traditional definition of artificial intelligence is the ability of machines to execute tasks and solve problems in ways normally attributed to humans. Some tasks that we consider simple--recognizing an object in a photo, driving a car--are incredibly complex for AI. Machines can surpass us when it comes to things like playing chess, but those machines are limited by the manual nature of their programming; a 30 gadget can beat us at a board game, but it can't do--or learn to do--anything else. This is where machine learning comes in. Show millions of cat photos to a machine, and it will hone its algorithms to improve at recognizing pictures of cats.

TOKYO (Reuters) – Toyota Motor Corp is targeting developing in the next five years driver assistance systems that integrate artificial intelligence to improve vehicle safety, the head of its advanced research division said. Gill Pratt, CEO of recently set up Toyota Research Institute (TRI), the Japanese automaker's research and development company that focuses on AI, said it aims to improve car safety by enabling vehicles to anticipate and avoid potential accident situations. Toyota has said the institute will spend 1 billion over the next five years, as competition to develop self-driving cars intensifies. Earlier this month, home rival Honda Motor Co said it was setting up a new research body which would focus on artificial intelligence, joining other global automakers which are investing in robotics research, including Ford and Volkswagen AG. "Some of the things that are in car safety, which is a near-term priority, I'm very confident that we will have some advances come out during the next five years," Pratt told reporters late last week in comments embargoed for Monday.

Adele's record-breaking album 25 is coming to Spotify, Apple Music, and other streaming services tomorrow (or right now if you happen to reading from New Zealand or Australia). While there are other artists who are absent from most music subscription services--Prince and Neil Young come to mind--Adele's 25 is a unique for two reasons: First, it's the best-selling album since 2001, when music sales began their epic collapse. Secondly, 25 has not been made available on any streaming service until now. Other big name recent albums have either limited their release to certain services (Tidal, more often than not) or delayed their streaming debut all together, but these windows and exclusives typically don't last seven months. While the subscription services are undoubtedly thrilled to finally offer Adele's latest, the stunning success she achieved without their help doesn't bode well for the streaming music model.