Softbank has bet $300 million, with more to come, that robotic process automation (RPA) will be what brings artificial intelligence (AI) in the enterprise. Robotic Process Automation (or RPA) is one of the hottest areas in the enterprise technology sector these days, reaching $1.3 billion this year, says Gartner. According to the market research firm, RPA software revenue grew 63.1% in 2018 to $846 million, making it the fastest-growing segment of the global enterprise software market, with the top-five RPA vendors (UiPath, Automation Anywhere, Blue Prism, NICE, Pegasystems) controlling 47% of the market. North America continues to dominate the RPA software market, with a 51% share in 2018, followed by Western Europe, while Japan came in third, with adoption growth of 124% in 2018. "This shows that RPA software is appealing to organizations across the world, due to its quick deployment cycle times, compared with other options such as business process management platforms and business process outsourcing," said Fabrizio Biscotti, research vice president at Gartner.

The Conference Board of Canada's Global Commerce Centre (GCC) held a strategic foresight workshop on November 19, 2018. The workshop allowed GCC stakeholders to discuss and develop a series of plausible futures with specific assumptions on AI (artificial intelligence) global adoption, and the openness of the global trade environment. This strategic foresight report identifies four potential futures for consideration. The report provides insights into the challenges and opportunities that industries, the government, and the public may face as AI technologies and global economic trends continue to evolve. All four workshop groups highlighted the role of government policies and the need for good governance, ethical frameworks, and educational programs.

"Atos has developed a differentiated experience with its North American AI Lab to provide customers tangible results which they can use to kick-start their AI strategy and take into the field immediately," said Peter Cutts, Chief Digital Transformation Officer, Atos North America. "Customers are looking for industry-specific solutions for their business needs, not a cookie cutter approach. The Atos AI Lab approach empathizes with end users' needs and engages multiple stakeholders to deliver real-world code, datasets and solutions that are repeatable and globally scalable." The Atos AI Lab is a state-of-the-art facility that combines a digital experience with design thinking methodology to allow participants to problem solve and create in a format that works best for them. The Atos AI Lab offers an Incubation workshop that aims to create a use-case ready to deploy at the end of two days, meaning customers can start driving business results quickly.

The C-130 Hercules is the most versatile aircraft in aviation history. From landing at the world's highest airstrip in the Himalayas to taking off and landing on an aircraft carrier in the middle of the Atlantic Ocean, the aircraft is celebrated for its unsurpassed versatility, performance and mission effectiveness. Today, 70 countries rely on the C-130 for search and rescue, peacekeeping, medical evacuations, scientific research, military operations, aerial refueling and humanitarian relief. More than 2,500 C-130s have been produced to date. The worldwide operational fleet includes legacy C-130 models as well as the current production variant – the C-130J Super Hercules.

We are hiring an Artificial Intelligence/Machine Learning Engineer to join our project team. You will work alongside other technical staff and report directly to project manager. You will lead all the AI/ML/NLP activities for the project. This will include making recommendations on techniques, solution design, tools and model development, solution training and platform deployment of AI/ML/NLP solutions for clients. Working closely with clients to gain an understanding of their business needs and goals, ensuring the best possible solution is provided.

The next extension of machine learning is on its way. Machine teaching promises to bring the power of AI to those unskilled in data science. What could possibly go wrong? Prefer to listen to this story? Here it is in audio format.

This past summer, I watched a brilliant lecture series by Richard McElreath on Bayesian statistics. It honestly changed my whole outlook on statistics, so I couldn't recommend it more (plus, McElreath is an engaging instructor). One of the most compelling cases for using Bayesian statistics is with a collection of statistical tools called linear mixed models or multilevel/hierarchical models. It's common that data are grouped or clustered in some way. Often in psychology we have repeated observations nested within participants, so we know that data coming from the same participant will share some variance. Linear mixed models are powerful tools for dealing with multilevel data, usually in the form of modeling random intercepts and random slopes.

A recent New York Times article fretting about the soaring costs of developing and training leading-edge deep learning models and my admittedly provocative Tweet questioning the premise and motives of the article's sources led to the type of online banter that indicates a nuanced question ill-suited for pithy Twitter responses. Fears of AI creating a chasm between haves and have-nots are common, however the topic of AI-fueled inequality typically centers on its economic effects, namely that the growing substitution of manual labor with algorithmic automation serves to further polarize income distributions as the knowledge class controlling and using the algorithms get richer while the working class being displaced by machines suffers. Many new technologies -- those we call'automation technologies' -- do not increase laborís productivity, but are explicitly aimed at replacing it by substituting cheaper capital (machines) in a range of tasks performed by humans. As a result, automation technologies always reduce the laborís share in value added (because they increase productivity by more than wages and employment). They may also reduce overall labor demand because they displace workers from the tasks they were previously performing.

Editor's Note: Andrea Frittoli and Kyra Wulffert are presenting their talk"Machine Learning for Continuous Integration" at ODSC 2019 Europe. As more applications move to a DevOps model with CI/CD pipelines, the testing required for this development model to work inevitably generates lots of data. This is also true for large open-source projects, that may see millions of tests executed on a daily basis. The data produced by such CI systems contains information about several aspects of the continuous testing system; engineers with specific domain experience usually parse such data on a daily basis in an effort to maintain the system running smoothly. After years of experience in the field, we wanted to investigate if machine learning could help us extract valuable insights from CI data with minimal human intervention.

Tesla has reportedly acquired the four-year-old startup DeepScale, which provides interesting insight into the state of Artificial Intelligence in assisted and automated driving. Operating on $18M in venture funding, DeepScale described themselves as developers of perceptual systems for semi-autonomous and autonomous vehicles, focusing on low-wattage processors used in mass-market automotive crash avoidance systems to power more accurate perception. This is an important niche in the intelligent vehicle eco-system; the volumes of systems like automatic emergency braking (AEB) are increasing rapidly due to OEMs making them a standard feature. JATO reports that AEB sales as standard equipment have increased from 6% in Model Year 2016 to 39% in Model Year 2018, with the fitment rate rising to 49% of vehicles sold for Model Year 2019. As one who well remembers the refrain "safety doesn't sell" in automotive circles during the 1990's, this is remarkable and gladdening.