Globally renowned brands would not, ordinarily, want to be associated with Germany's far-right opposition. But Tesla, one of the world's biggest corporate names, does not have a conventional chief executive. After Elon Musk backed Alternative für Deutschland (AfD) – calling the party Germany's "only hope" – voters are considering an alternative to Tesla. Data released on Thursday showed that registrations of the company's electric cars in Germany fell 76% to 1,429 last month. Overall, electric vehicle registrations rose by 31%.

My first task as a Chief Data Officer (CDO) is to implement a data strategy. Over the past 15 years, I've learned that an effective data strategy enables the enterprise's business strategy and is critical to elevate the role of a CDO from the backroom to the boardroom. A company's business strategy is its strategic vision to achieve its business goals. Data that can be managed, protected, and monetized effectively will provide insights into how to achieve those goals. A CDO works in collaboration with senior executives to steer a business to its strategic vision through a data strategy.

I am grateful to have the health insurance I have, and grateful for the payments they've made to resolve problems I've had. Nonetheless, I can't help but be astounded at the never-ending flow of expensive, incompetent, annoying and utterly useless interaction I have had with the company's computer systems. Why can't they (and others like them) get it right? The answer is simple: the company's leaders, like most enterprise companies, want to be leaders in technology. Today, that means funding big, publicized initiatives in AI and ML. Initiatives that will, of course, transform healthcare.

Financial markets were cheered recently by the news that the US and China have reached a "phase one" deal to prevent further escalation of their bilateral trade war. But there is actually very little to cheer about. In exchange for China's tentative commitment to buy more US agricultural (and some other) goods, and modest concessions on intellectual property rights and the yuan, the US agreed to withhold tariffs on another $160bn (£124bn) worth of Chinese exports, and to roll back some of the tariffs introduced on 1 September. The good news for investors is that the deal averted a new round of tariffs that could have tipped the US and the global economy into recession and crashed global stock markets. The bad news is that it represents just another temporary truce amid a much larger strategic rivalry encompassing trade, technology, investment, currency and geopolitical issues.

With the adoption of AI in business processes, KPIs can provide a window into the health of an organization at granular levels, crystalizing the performance picture and removing ambiguity. Becoming a "data-driven organization" has been the buzz for quite some time in the circles of the CIO, CTO, and now CDO, but there isn't necessarily agreement as to what that actually looks like. Because the goals within each part of a business can vary greatly in expected outcomes, it's often hard to determine whether a data-driven project was an overall success and actually moved the needle for a global, dynamic organization. Much of the data supporting decisions about "did we do what we intended?" KPIs are supposed to be about providing data that can be analyzed, reviewed, and acted upon to improve something about how the organization operates.

The Company: Faraday Future (FF) is a California-based mobility company, leveraging the latest technologies and world's best talent to realize exciting new possibilities in sustainable transportation. We're producing user-centric, technology-first vehicles to establish new paradigms in human-vehicle interaction. We're not just seeking to change how our cars work – we're seeking to change the way we drive. At FF, we're creating something new, something connected, and something with a global impact. Job Summary The Product Strategy team is focused on providing broad, strategic vision for the organizations products and identifying and evaluating new opportunities to enhance mobility for all.

Machine learning is based on algorithms that can learn from data without relying on rules-based programming. It came into its own as a scientific discipline in the late 1990s as steady advances in digitization and cheap computing power enabled data scientists to stop building finished models and instead train computers to do so. The unmanageable volume and complexity of the big data that the world is now swimming in have increased the potential of machine learning--and the need for it. In 2007 Fei-Fei Li, the head of Stanford's Artificial Intelligence Lab, gave up trying to program computers to recognize objects and began labeling the millions of raw images that a child might encounter by age three and feeding them to computers. By being shown thousands and thousands of labeled data sets with instances of, say, a cat, the machine could shape its own rules for deciding whether a particular set of digital pixels was, in fact, a cat.1 1.Fei-Fei Li, "How we're teaching computers to understand pictures," TED, March 2015, ted.com. Last November, Li's team unveiled a program that identifies the visual elements of any picture with a high degree of accuracy.

Is the hype surrounding the promise of Machine Learning merited or are we getting ahead of ourselves? Is artificial-intelligence no longer just for the digital-only business models of Amazon, Google, and Netflix? As a finance executive, what do you need to know to maximize the opportunity for both you and your business? In this article, I will answer the questions our clients most frequently ask me. What does it take to get started?

In fact, according to a recent Forbes Insights survey of 300-plus executives--63% of whom were in the C-suite--95% believe that AI will play an important role in their responsibilities in the near future. The deep data that AI allows companies to tap into is providing insights to help solve challenges in everything from better neuroscience diagnoses to producing kegs of beer. The power of AI is understood by business leaders regardless of company size: The Forbes Insights survey drew detailed responses from companies with annual revenue over $10 billion to as low at $250 million, with remarkable consensus across business size and sectors. All told, 99% of executives in technical positions say their organizations are going to boost their AI spending in the coming year, a level of funding increases likely unmatched anywhere else in the corporate world. Implementation of AI is just getting started in business, but make no mistake: Change comes quickly.

A new study names General Motors Co. and Waymo LLC as the leaders in the autonomous car race, while placing highly touted competitors such as Tesla Inc. and Apple Inc. at the back of the pack. The study, published by Navigant Research, cited General Motors and Waymo (formerly known as the Google self-driving car project) for its efforts to move autonomous cars closer to production. "There's a vast difference between developing an autonomous car as an R&D project and building one as a real product," Sam Abuelsamid, the study's author, told Design News. "Both of these companies have built in the redundancies on the compute side and on the sensor side. The Navigant Research Leaderboard, as it's known, ranks 19 companies on 10 different automated driving criteria. Those include strategic vision, go-to-market planning, technology, production strategy, sales and marketing, product capability, product quality, product portfolio, partners, and staying power. Four of the top five companies in the study were traditional automakers. Others in the top five were the Daimler AG-Robert Bosch GmbH team, Ford Motor Co., and Volkswagen Group. GM, in particular, was singled out for its ability to put the technology in the hands of the consumer, and its willingness to treat the autonomous car as more than a science project. "All of GM's engineering teams back in Michigan are doing the same things for the autonomous Bolt as they would for any other program," Abuelsamid said. While Waymo LLC did not have some of built-in industry advantages of GM, it nevertheless scored well in the study because of its commitment to putting the technology on the road. Waymo has launched an "early rider" program in Phoenix and has teamed with Fiat Chrysler to test its hardware and software on Chrysler Pacifica minivans. In contrast, Tesla Inc. landed at the back of the pack, largely due to its lack of engineering execution and poor partner relationships. "Their technology is behind the rest of the pack because of their insistence on not using Lidar," Abuelsamid told us. "Also, they don't have redundant compute platforms.