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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.

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.