The public cloud division of Chinese ecommerce company Alibaba Group today is introducing new artificial-intelligence (AI) services targeting two specific industries, health care and manufacturing. The Alibaba Cloud is touting an ET Medical Brain and an ET Industrial Brain, each of which encompasses a number of services. The latter will give companies tools for monitoring the production process, improving energy efficiency, and predicting when maintenance will be needed. Also today, Alibaba is announcing the launch of version 2.0 of its PAI machine learning service. Alibaba introduced the original PAI in 2015.

Machines, Robotic an AI: Technology is part of our lives. Like personal assistants, our smartphones and apps, computers and social media, ease our days, help us keeping in contact with friends and acquaintances, manage our working schedules, our workouts, and even help us find a job. In a society where human interaction with technology is increasing, challenges are a natural consequence. Robots have been used for quite some years now in the industry, autonomously performing repetitive tasks, in shop floors, where collaborative robots can work in close connection with humans, in medicine, just think about surgical robots, and even in our homes, where they clean our floors giving us some additional free time. Within this picture, artificial intelligence is just another step further.

With artificial intelligence set to transform our world, a new institute is putting Toronto to the front of the line to lead the charge. The Vector Institute for Artificial Intelligence, made possible by funding from the federal government revealed in the 2017 budget, will move into new digs in the MaRS Discovery District by the end of the year. There, scientists will aim to attract and retain top global talent while working on software that mimics -- and may one day surpass -- human intelligence. Vector's funding comes partially from a $125 million investment announced in last Wednesday's federal budget to launch a pan-Canadian artificial intelligence strategy, with similar institutes being established in Montreal and Edmonton. 'I think this truly is Canada's moment and we would be foolish not to take advantage of it,' said Canadian Institute for Advanced Research CEO and president Dr. Alan Bernstein.

Domino's has unleashed another set of pizza delivery drones, this time in Germany and the Netherlands. Last year, it worked with Flirtey to drop pizza to customers in New Zealand using unmanned aerial vehicles. For this pilot program, however, it chose to use autonomous rovers developed by Starship Technologies, a company built by two of Skype's founders. Domino's told Engadget that launching this program doesn't mean it has given up on developing its own delivery drones, which it's been doing for a year now. Both this pilot and the one in New Zealand come under the auspices of DRU (Domino's Robotic Unit), the same division that's developing its homegrown machine.

Who would've thought just a few short years ago that we'd be speaking about predictive analytics and machine learning in the same sentence? But it has received increased attention recently thanks to open source programming like R and Python, which have introduced new ways to process data, and the growing application of JavaScript as more than a client-side language. Open source programming also thrives on sharing technique and solutions. All of these developments, combined with cloud solutions, have ushered in accessibility to machine learning techniques in almost every industry and research. Companies have been just getting by with basic metrics that explain what has happened with marketing activity on a website.

A Dutch startup is set to release a fleet of'drones' to combat the 220 million pounds of dog droppings left on the Netherlands' streets each year. Called Dogdrones, the vehicles will work together as a team to detect and scoop up the poop. The aerial drone is fitted with cameras and thermal energy technology that transmits GPS coordinates of the feces to a rolling robot on the ground that immediately leaves its hub to clean up the waste. A Dutch startup is set release a fleet of'drones' to combat the 220 million pounds of dog droppings left on the Netherlands's streets each year. Watchdog 1, uses a camera and thermal imaging to scan the environment for canine waste. The thermal imaging will then crate a heat map showing the location, which is translated into GPS coordinates and sent to Patroldog 1 – the ground robot.

Developing new drugs is notoriously inefficient. Fewer than 12 percent of all drugs entering clinical trials end up in pharmacies, and it costs about $2.6 billion to bring a drug to market. There are so many molecules to test that pharmaceutical researchers use pipetting robots to test a few thousand variants all at once. The best candidates then go into animal models or cell cultures, where hopefully a few will go on to bigger animal and human clinical trials. Which is why more and more drug developers are turning to computers and artificial intelligence to narrow down the list of potential drug molecules--saving time and money on those downstream tests.

The calculation of minimum energy paths for transitions such as atomic and/or spin re-arrangements is an important task in many contexts and can often be used to determine the mechanism and rate of transitions. An important challenge is to reduce the computational effort in such calculations, especially when ab initio or electron density functional calculations are used to evaluate the energy since they can require large computational effort. Gaussian process regression is used here to reduce significantly the number of energy evaluations needed to find minimum energy paths of atomic rearrangements. By using results of previous calculations to construct an approximate energy surface and then converge to the minimum energy path on that surface in each Gaussian process iteration, the number of energy evaluations is reduced significantly as compared with regular nudged elastic band calculations. For a test problem involving rearrangements of a heptamer island on a crystal surface, the number of energy evaluations is reduced to less than a fifth. The scaling of the computational effort with the number of degrees of freedom as well as various possible further improvements to this approach are discussed.

The optimization of algorithm (hyper-)parameters is crucial for achieving peak performance across a wide range of domains, ranging from deep neural networks to solvers for hard combinatorial problems. The resulting algorithm configuration (AC) problem has attracted much attention from the machine learning community. However, the proper evaluation of new AC procedures is hindered by two key hurdles. First, AC benchmarks are hard to set up. Second and even more significantly, they are computationally expensive: a single run of an AC procedure involves many costly runs of the target algorithm whose performance is to be optimized in a given AC benchmark scenario. One common workaround is to optimize cheap-to-evaluate artificial benchmark functions (e.g., Branin) instead of actual algorithms; however, these have different properties than realistic AC problems. Here, we propose an alternative benchmarking approach that is similarly cheap to evaluate but much closer to the original AC problem: replacing expensive benchmarks by surrogate benchmarks constructed from AC benchmarks. These surrogate benchmarks approximate the response surface corresponding to true target algorithm performance using a regression model, and the original and surrogate benchmark share the same (hyper-)parameter space. In our experiments, we construct and evaluate surrogate benchmarks for hyperparameter optimization as well as for AC problems that involve performance optimization of solvers for hard combinatorial problems, drawing training data from the runs of existing AC procedures. We show that our surrogate benchmarks capture overall important characteristics of the AC scenarios, such as high- and low-performing regions, from which they were derived, while being much easier to use and orders of magnitude cheaper to evaluate.

However, our reason to come to Engadin was not to snowboard or ski, but to work on innovative solutions in order to maximise the region's potential. Ten selected startups from all over Europe came to St. Moritz for three days to develop new ideas, create prototypes and, of course, connect with great people from different companies. We were lucky to be one of the chosen startups. Our team was the only one that decided to work on two topics, "chatbots" & "data analytics," because we felt that both were necessary to create the best customer experience. The first evening was a get-together -- informal dinner and fun activities where tech founders, investors, innovation executives, and tourism experts got to know each other.