The Digiday AI Marketing Summit is coming up in April and will cover how brands can understand and use AI and machine learning in their marketing. If you're a brand marketer figuring out how to use AI for customer service, content creation, internal data organization and more, apply for a complimentary pass here. Cars aren't the only thing Volkswagen wants to automate. Artificial intelligence is managing the brand's media buys in Germany and proving to be more effective than its media agency. Whenever Volkswagen uses the recommendations from Blackwood Seven, a Danish media agency that uses AI and predictive analytics to forecast ad spend decisions, it sells more cars than it would have if it had gone with its media agency's recommendation, according to Lutz Kothe, the head of marketing and PR for Volkswagen's passenger cars.

This could create security nightmares. Worse still, it could strip away from each of us a part of our uniqueness. But companies, universities and governments are already working furiously to decode the human voice for many applications. Technologically adept nation states (the US, China and Estonia) have waded into this space and tech giants such as Google, Amazon, Apple and Facebook also have special projects on voice. It's not that hard to develop an artificial voice, then model and reproduce spoken words and phrases.

GoPro still isn't in the best of health, and that's leading it to make some tough decisions about its future. The action camera maker has confirmed that it's cutting nearly 300 jobs (from 1,254 to "fewer than 1,000"), and is exiting the drone market entirely. The Karma drone was the second most popular drone in its price class, GoPro said, but it faced profit margin problems in an "extremely competitive aerial market. Combine that with a "hostile regulatory environment" in Europe and the US, and the market is expected to shrink to the point where it's "untenable" to compete. The company will sell what's left of its Karma inventory, and plans to continue supporting drones.

From a global wave of automation at every level of industry to a major skills gap driving huge demand for AI talent, businesses across sector lines must begin to develop their AI strategy – today. To celebrate the launch of our Future of Work issue this month, we've compiled together some of the latest cutting-edge research, statistics, and reports from the world's leading tech and business giants, including: Stay tuned for a deep dive into the big questions surrounding the future of our economy and our organizations; the vital strategic debates surrounding AI in the boardroom; the real-world AI use cases transforming the nature of work today; and the next big steps for AI in the business world. In the next two weeks, we'll be speaking to business and tech visionaries from across the spectrum of AI innovation. Our first cover feature for the month, 'Bridging The AI Skills Gap', will offer real-world insights into how organizations can ensure their workforce don't sink beneath the rising tide of AI, featuring interviews with figures such as Rob McCargow, PwC UK AI Lead, and many more. In'Could Basic Income Hold The Key to AI Success?' we'll be speaking to Reform Scotland about the country's four universal basic income trials to explore with business leaders how AI, combined with basic income, could prompt huge shifts in the relationship between organizations and the workforce.

The Nvidia Drive Xavier, available this quarter, is the autonomous machine processor at the heart of this system. The SoC is capable of performing 30 deep learning TOPS (trillions of operations per second), using only 30 watts of power. According to the developer, Xavier also enables rich, diverse I/O connections to many different types of sensors. Germany-based ZF brings expertise for system integration of the car computer and the sensors. ZF's new Xavier-based ProAI will process inputs from multiple cameras, plus lidar and radar, paint a 360-degree view around the vehicle, locate it on an HD map, and find a safe path through traffic.

Time to give Gandhi a rest. Via RPS, The University of Cambridge's Centre for the Study of Existential Risk has released a mod for Civilization V called Superintelligence, which adds some artificial intelligence options to the late-game. Artificial intelligence can initially provide some benefits, and eventually can turn into superintelligence that brings mastery of science to its discoverer. However, if too much artificial intelligence research goes uncontrolled, rogue superintelligence can destroy humanity and bring an instant loss of the game. Considering the latter is how most of my games end up anyway, what is there to lose.

Byton has revealed the first look at the high-tech electric car it claims will'perceive more than what a human being will ever do.' The firm finally unveiled its first drivable prototype at the Consumer Electronics Show in Las Vegas today, after teasing the so-called Smart Intuitive Vehicle (SIV) since September. The Nanjing-based company plans to roll out the mid-sized crossover vehicle with level 3 autonomy in China in 2019, starting at $45,000, before hitting the US and Europe in 2020. The car will respond to conditions on the road to avoid potential threats; for example, 'if the exterior lighting conditions change, your Byton will adjust to your needs automatically,' the co-founder said. The base model will be able to achieve 250 miles on a charge, while the higher end variant will go up to about 325 miles – and, the firm claims it will charge incredibly fast.

Regression models for supervised learning problems with a continuous target are commonly understood as models for the conditional mean of the target given predictors. This notion is simple and therefore appealing for interpretation and visualisation. Information about the whole underlying conditional distribution is, however, not available from these models. A more general understanding of regression models as models for conditional distributions allows much broader inference from such models, for example the computation of prediction intervals. Several random forest-type algorithms aim at estimating conditional distributions, most prominently quantile regression forests (Meinshausen, 2006, JMLR). We propose a novel approach based on a parametric family of distributions characterised by their transformation function. A dedicated novel "transformation tree" algorithm able to detect distributional changes is developed. Based on these transformation trees, we introduce "transformation forests" as an adaptive local likelihood estimator of conditional distribution functions. The resulting models are fully parametric yet very general and allow broad inference procedures, such as the model-based bootstrap, to be applied in a straightforward way.

The present paper aims to demonstrate the usage of Convolutional Neural Networks as a generative model for stochastic processes, enabling researchers from a wide range of fields - such as quantitative finance and physics - to develop a general tool for forecasts and simulations without the need to identify/assume a specific system structure or estimate its parameters.

Knowledge graph embedding aims to embed entities and relations of knowledge graphs into low-dimensional vector spaces. Translating embedding methods regard relations as the translation from head entities to tail entities, which achieve the state-of-the-art results among knowledge graph embedding methods. However, a major limitation of these methods is the time consuming training process, which may take several days or even weeks for large knowledge graphs, and result in great difficulty in practical applications. In this paper, we propose an efficient parallel framework for translating embedding methods, called ParTrans-X, which enables the methods to be paralleled without locks by utilizing the distinguished structures of knowledge graphs. Experiments on two datasets with three typical translating embedding methods, i.e., TransE [3], TransH [17], and a more efficient variant TransE- AdaGrad [10] validate that ParTrans-X can speed up the training process by more than an order of magnitude.