The members of the European Laboratory for Learning & Intelligent Systems, a network of European research labs in the field of #machinelearning, today launched several ELLIS Programs at a meeting in San Sebastián, Spain: http://www.ellis.eu

If you can't beat them, join them. Or if you're Elon Musk, the billionaire boss of private rocket outfit SpaceX and electric vehicle firm Tesla, why not just become them? On Wednesday, the tech mogul unveiled plans to implant human brains with computer chips in an effort to merge man with machine, marking the start of a quest to prevent Silicon Valley's most feared scenario: society's demise at the hands of artificial intelligence. Musk has long sounded the alarm on AI, claiming that rapid advances in machine learning risk "summoning a demon" that leaves humans in the dust as super-intelligent systems out-manoeuvre our species at every task imaginable.

Palo Alto Networks on Wednesday announced the acquisition of IoT security firm Zingbox for $75 million in cash, and made public its financial results for the fiscal year 2019. Zingbox provides a cloud-based lifecycle management solution that uses AI and machine learning technologies to identify, secure and optimize devices.

Hands-on tips for companies to build Machine Learning Products that are being adopted by their users and customers. The biggest difficulty for products based on machine learning (ML) will be user or customer adoption. How did I come to this conclusion? A top executive of one of the biggest European insurance companies told me: "We have the money and technical talent to build sophisticated ML-products, but we do not know how to make users adopt those products. We spent millions of dollars on an ML-based app but only got around 300 users. We do not understand why people do not want to use our app."

In the ongoing war for control of the digital space that is the modern car, Google just conquered a major swath of territory. This week, it and General Motors announced that the tech giant's Android operating system will underpin the infotainment systems in GM's cars, starting with some model year 2022 vehicles. For future Chevy, Cadillac, Buick, and GMC drivers, that means built-in access to Google Maps and the ability to use Google Assistant to make calls, send texts, tune the radio, and more, without plugging in their phones. For GM, it's an admission that people don't want its house-made infotainment tech, and to give them what they do want. For Google, it's more direct access to more consumers--GM accounts for 17 percent of new car sales in the US--in the rare place where they're not supposed to have their phone in their hand.

As data and the business problems that can be addressed by it proliferate, our ability to analyze them is falling behind. We don't have enough data scientists, we can't create enough good models, and we can't get them into production. Enter automated machine learning (AutoML), which offers substantial potential for solving the problem. This powerful set of tools can help with a wide variety of ML activities, including preparing data for analysis, performing feature engineering, automatically generating well-fitting models using the best algorithm, and generating code or APIs to help deploy the model into production. AutoML has been around in some form since the mid-1990s, but it didn't really take off until the past few years.

Burnout has become a popular buzzword in today's business world, meant to describe prolonged periods of stress in the workplace leading to feelings of depression and dissatisfaction with one's occupation. The topic has become so pervasive that the World Health Organization (WHO) addressed it at its 2019 World Health Assembly in Geneva in May, adding burnout to the 11th revision of the International Classification of Diseases (ICD-11) -- although classifying it as an "occupational phenomenon" rather than a medical condition. Healthcare itself is not immune to burnout, and a recent study in Journal of the American College of Radiology demonstrates it is taking a toll on pediatric radiologists in particular. The study surveyed Society of Pediatric Radiology (SPR) members and found nearly two-thirds expressed at least one symptom of burnout. While burnout is a complicated phenomenon and no two people experience it the same way, a commentary on the study suggests artificial intelligence (AI) could help alleviate some of the difficulties that can lead to burnout.

IFA, or Internationale Funkausstellung Berlin, began humbly in 1924 as an international radio exhibition. Since then, it's grown into one of the largest annual consumer electronics shows anywhere, with more than 200,000 visitors anticipated over the next few days. The Berlin expo is a who's who of the appliance and device makers, with the likes of Microsoft, Bang & Olufsen, Casio, Samsung, LG, Sony, Huawei, ZTE, Asus, Acer, Toshiba, Philips, Panasonic, Amazon, and other industry titans making appearances. As in years past, a slew of announcements marked the start of IFA 2019, and VentureBeat covered the events as they happened from the show floor. Here are a few of the highlights so far.

Pragmatism from cybersecurity to enterprise imaging was in vogue at the 2019 meeting of the Society of Imaging Informatics in Medicine (SIIM). Not unexpectedly, artificial intelligence accounted for much discussion amid telltale cracks in its hype. Exerting pressure was an undercurrent of practicality, bubbling up in session talks by key opinion leaders (KOLs) and from the exhibit floor, where some company representatives spoke of the continuing need for artificial intelligence (AI) to demonstrate an ROI (return on investment). This doff of the hat to practicality could be seen in the format of scientific sessions, which were kicked off by luminary speakers providing the context in which to understand research data presented in follow-on talks. Other sessions featured faculty, as in the case of one about cybersecurity hosted by J. Anthony Seibert, Ph.D., an imaging physicist on the radiology faculty of the University of California in Davis.

Today, businesses need to be able to predict demand and trends to stay in line with any sudden market changes and economy swings. This is exactly where forecasting tools, powered by Data Science, come into play, enabling organizations to successfully deal with strategic and capacity planning. Smart forecasting techniques can be used to reduce any possible risks and assist in making well-informed decisions. One of our customers, an enterprise from the Middle East, needed to predict their market demand for the upcoming twelve weeks. They required a market forecast to help them set their short-term objectives, such as production strategy, as well as assist in capacity planning and price control.