A mix of artificial intelligence programs has been used to design, direct and edit a music video. Ad agency Saatchi & Saatchi commissioned the film for a song by a French electro band. It made its debut at the Cannes Lions advertising festival to coincide with the anniversary of AI pioneer Alan Turing's birth. However, the band - which does not want to be named - is not allowing the final edit to be made public.

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.

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I recently participated in a meeting of technologists, economists and European philosophers and theologians. Other attendees included Andrew McAfee, Erik Brynjolfsson, Reid Hoffman, Sam Altman, Father Eric Salobir. One of the interesting things about this particular meeting for me was to have a theological (in this case Christian) perspective to our conversation. Among other things, we discussed artificial intelligence and the future of work. The question about how machines will replace human beings and place many people out of work is well worn but persistently significant.

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.

Let's assume we are really into mountain climbing, and to add a little extra challenge, we cover eyes this time so that we can't see where we are and when we accomplished our "objective," that is, reaching the top of the mountain. Since we can't see the path upfront, we let our intuition guide us: assuming that the mountain top is the "highest" point of the mountain, we think that the steepest path leads us to the top most efficiently. We approach this challenge by iteratively "feeling" around you and taking a step into the direction of the steepest ascent -- let's call it "gradient ascent." But what do we do if we reach a point where we can't ascent any further? I.e., each direction leads downwards?

XGBoost (short for Extreme Gradient Boosting) is a relatively new classification technique in machine learning which has won more and more popularity because of its exceptional performance in multiple competitions hosted on Kaggle.com. A lesser known benefit of using XGBoost is that the tree ensemble model can rank features for high-dimensional data sets. The official implementation of XGBoost (Python) provides only one feature scoring function called get_fscore. What it does is that, it computes feature scores by counting how many times a feature appears in the splits and rank the features according to the splits. It is simple, and it is straightforward, but I believe we should not ignore another metric which is critical to the decision tree method.

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.