Creating official, high-quality statistics based on big data and register data is not a simple matter. The data sources were not designed for statistical use and safeguarding the quality and continuity is quite complex. Therefore, the ultimate challenge for data scientists is to develop methods that'translate' huge amounts of data into high-quality statistics. CBS pursues this challenge with methods such as machine learning. As CBS data scientist Marc Ponsen explains, 'Machine learning has received an enormous boost by faster computers and the huge amounts of data that have become available, although what the best possible method is very much depends on the domain under investigation.'

A YORKSHIRE university aims to use data and digital connectivity to support revolutionary changes in the region's manufacturing sector. The University of Huddersfield has established a Digital Enablers' Network (DEN) which is working with local manufacturing firms to make them more competitive in global markets. The university is forging strong ties with businesses, including enterprises run by former students, through its 3M Buckley Innovation Centre. James Devitt, the university to industry programme manager and 3M Buckley innovation centre business development manager, said the large scale automation of processes using artificial intelligence are leading to major improvements in productivity. "The University of Huddersfield is proud of its close association with industry and manufacturing,'' Mr Devitt said. "It has built strong capabilities in a range of new industrial digital technologies, centred around its Centre for Industrial Analytics (CIndA).

With banks under massive pressure to slash costs associated with the production of research on stocks and bonds in Europe, it's artificial intelligence (AI) to the rescue, with Germany's $525-billion Commerzbank leading the way with a new content-automation partnership. For Germany's second-largest bank, it's a major expenditure on research, with a partner, Retresco, that hopes to help it churn out AI-generated research reports and replace the human element in analyst notes. The AI product hasn't been fine-tuned yet, but investors will be interested to learn that it's already advanced enough to provide around 75 percent of what a human equity analyst would give an investor when writing an immediate report on quarterly earnings. Presumably, AI is more objective, too--and that's where the idea of investor protection comes into play. But equity reports reviewing quarterly earnings, says Michael Spitz, Commerzbank's head of research, have common reporting standards, so the parameters are easy to plug in to AI, the Financial Times reported.

The man who hacked into hundreds of iCloud accounts of Hollywood stars and others before leaking their nude photos across the internet has been sentenced. Connecticut man George Garafano became infamous when he stole private photos from people including Jennifer Lawrence and made them available across the internet. He was sentenced to eight months in prison this week, in federal court in Bridgeport. After prison, he must serve three years of supervised release and perform 60 hours of community service. The I.F.O. is fuelled by eight electric engines, which is able to push the flying object to an estimated top speed of about 120mph.

The 18th of March, 2018, was the day tech insiders had been dreading. That night, a new moon added almost no light to a poorly lit four-lane road in Tempe, Arizona, as a specially adapted Uber Volvo XC90 detected an object ahead. Part of the modern gold rush to develop self-driving vehicles, the SUV had been driving autonomously, with no input from its human backup driver, for 19 minutes. An array of radar and light-emitting lidar sensors allowed onboard algorithms to calculate that, given their host vehicle's steady speed of 43mph, the object was six seconds away – assuming it remained stationary. But objects in roads seldom remain stationary, so more algorithms crawled a database of recognizable mechanical and biological entities, searching for a fit from which this one's likely behavior could be inferred. At first the computer drew a blank; seconds later, it decided it was dealing with another car, expecting it to drive away and require no special action. Only at the last second was a clear identification found – a woman with a bike, shopping bags hanging confusingly from handlebars, doubtless assuming the Volvo would route around her as any ordinary vehicle would. Barred from taking evasive action on its own, the computer abruptly handed control back to its human master, but the master wasn't paying attention. Elaine Herzberg, aged 49, was struck and killed, leaving more reflective members of the tech community with two uncomfortable questions: was this algorithmic tragedy inevitable? And how used to such incidents would we, should we, be prepared to get? "In some ways we've lost agency. When programs pass into code and code passes into algorithms and then algorithms start to create new algorithms, it gets farther and farther from human agency. Software is released into a code universe which no one can fully understand."

The intelligent world is truly upon us, and based on the technological advancements that we are becoming used to, one cannot wait for the future to arrive. In this intelligent world, there is pressure on businesses to deliver an exceptional customer experience, and to use every technology they can to ensure that the customers get what they expect. I recently had the opportunity to attend the SAP Ariba Live event in Amsterdam. The event is one of the biggest supply chain and procurement conferences held across the globe. At the conference, I had the opportunity to hear and witness excellent use cases, and speak to the president of SAP Ariba, Barry Padgett.

One of the biggest challenges facing car companies developing driverless vehicles has little do with sophisticated robotics or laser technology. Instead, they must figure out how to engineer something far more amorphous but no less important: human trust, the kind that is communicated when human drivers and pedestrians make eye contact at a crosswalk. Surveys indicate that large portions of the public harbor deep reservations about the safety of self-driving technology, so Jaguar Land Rover enlisted the help of cognitive psychologists to unpack "how vehicle behaviour affects human confidence in new technology," the British automaker said in a news release. Its solution for answering that question: virtual eyes, a large cartoonish pair that bring to mind the plastic googly eyes you probably glued onto projects in elementary school. The eyes have been fitted to autonomous vehicles known as "intelligent pods."

Perception technologies in Autonomous Driving are experiencing their golden age due to the advances in Deep Learning. Yet, most of these systems rely on the semantically rich information of RGB images. Deep Learning solutions applied to the data of other sensors typically mounted on autonomous cars (e.g. lidars or radars) are not explored much. In this paper we propose a novel solution to understand the dynamics of moving vehicles of the scene from only lidar information. The main challenge of this problem stems from the fact that we need to disambiguate the proprio-motion of the 'observer' vehicle from that of the external 'observed' vehicles. For this purpose, we devise a CNN architecture which at testing time is fed with pairs of consecutive lidar scans. However, in order to properly learn the parameters of this network, during training we introduce a series of so-called pretext tasks which also leverage on image data. These tasks include semantic information about vehicleness and a novel lidar-flow feature which combines standard image-based optical flow with lidar scans. We obtain very promising results and show that including distilled image information only during training, allows improving the inference results of the network at test time, even when image data is no longer used.

Settings such as lending and policing can be modeled by a centralized agent allocating a resource (loans or police officers) amongst several groups, in order to maximize some objective (loans given that are repaid or criminals that are apprehended). Often in such problems fairness is also a concern. A natural notion of fairness, based on general principles of equality of opportunity, asks that conditional on an individual being a candidate for the resource, the probability of actually receiving it is approximately independent of the individual's group. In lending this means that equally creditworthy individuals in different racial groups have roughly equal chances of receiving a loan. In policing it means that two individuals committing the same crime in different districts would have roughly equal chances of being arrested. We formalize this fairness notion for allocation problems and investigate its algorithmic consequences. Our main technical results include an efficient learning algorithm that converges to an optimal fair allocation even when the frequency of candidates (creditworthy individuals or criminals) in each group is unknown. The algorithm operates in a censored feedback model in which only the number of candidates who received the resource in a given allocation can be observed, rather than the true number of candidates. This models the fact that we do not learn the creditworthiness of individuals we do not give loans to nor learn about crimes committed if the police presence in a district is low. As an application of our framework, we consider the predictive policing problem. The learning algorithm is trained on arrest data gathered from its own deployments on previous days, resulting in a potential feedback loop that our algorithm provably overcomes. We empirically investigate the performance of our algorithm on the Philadelphia Crime Incidents dataset.

Meta-learning is increasingly used to support the recommendation of machine learning algorithms and their configurations. Such recommendations are made based on meta-data, consisting of performance evaluations of algorithms on prior datasets, as well as characterizations of these datasets. These characterizations, also called meta-features, describe properties of the data which are predictive for the performance of machine learning algorithms trained on them. Unfortunately, despite being used in a large number of studies, meta-features are not uniformly described and computed, making many empirical studies irreproducible and hard to compare. This paper aims to remedy this by systematizing and standardizing data characterization measures used in meta-learning, and performing an in-depth analysis of their utility. Moreover, it presents MFE, a new tool for extracting meta-features from datasets and identify more subtle reproducibility issues in the literature, proposing guidelines for data characterization that strengthen reproducible empirical research in meta-learning.