In 2017, the Australian military drone-racing team made its competitive debut during the Australian Drone Nationals. Given the technology's long history within the military, you might think they would have an edge. But that's not what happened. Drone racing is a fairly new sport that merges video game racing with real-life drone flying. Racers put on a pair of first-person view (or FPV) goggles, which allow them to see exactly what they would if they were sitting in the teeny-tiny cockpit.

Its proposed solutions, while excellent, are partial and create the impression that the underlying purpose of this report was to make academia's voice heard in Whitehall and secure new long-term funding. No one doubts the stature of the UK's universities and research institutes, or the world-leading AI and robotics expertise within them, but by appearing to regard AI solely as an academic discipline, the report misses nearly all of the most important challenges facing the UK. As noted above, this report is interesting because it pushes the voices of workers to the front of the debate on the impact of AI. Too often we get drawn into the excitement around the potential of automation, the opportunities that could be gained, without thinking about the people that could be left behind as a result. Let's hope that this new commission can apply real pressure to the government to come up with an effective strategy and some new practical policies around addressing the forthcoming changes – working with citizens, employees and trade unions, rather than against them.

India plans to roll out state-of-the-art 5G telecom services in the next four years, a senior official said, as the nation rushes to catch up with its Asian peers. "We are not there yet," Telecom Secretary Aruna Sundararajan said in an interview in New Delhi, adding that the complete rollout of 5G will be done by 2022. "5G won't be driven by supply, it'll be driven by demand and the rest of industry needs to wake up to this." The South Asian nation, traditionally a laggard in embracing the latest technology in telecommunications, will follow South Korea, Japan and China, countries where 5G service will be offered within the next two years. The high-speed and low-latency service will help Prime Minister Narendra Modi's Digital India plan, which seeks to broaden Internet access.

Artificial intelligence is being credited for helping Hong Kong customs officials increase seizures of fake goods sold online by about one-third in the first six months of this year, resulting in a haul of counterfeit items worth HK$1.96 million (US$247,000). A new supercomputer they began using last December scoured websites 24 hours a day and detected close to 2,000 of the 5,200 items seized by the Customs & Excise Department. Over the same period last year, officers netted 11,800 pieces of counterfeit goods worth HK$1.47 million. A source said the department might look into expanding the capacity of the computer, which gathers important information during investigations, but he stressed it would complement rather than replace manual enforcement work by customs officers. "The analytics tool saves us a lot of time screening online platforms manually," the source said.

Almost no one knows how to utilize the technology at scale. More precisely, only a very small handful of organizations truly understand how to manage the risks of machine learning (ML) when implemented widely. Those risks include navigating the legal, reputational, and ethical issues ML can create – from wildly offensive chatbots and image classifiers to furthering racial disparities amongst zip codes, and much, much more. And that's not even taking into account the deceptively complex requirement of being able to predict how ML models will behave over long periods of time, or new laws like the EU's GDPR and their impact on ML. That's why we're thrilled to partner with the Future of Privacy Forum to release the first-ever guide to managing risk in machine learning, written specifically for practitioners.

The concept of stochastic configuration networks (SCNs) others a solid framework for fast implementation of feedforward neural networks through randomized learning. Unlike conventional randomized approaches, SCNs provide an avenue to select appropriate scope of random parameters to ensure the universal approximation property. In this paper, a deep version of stochastic configuration networks, namely deep stacked stochastic configuration network (DSSCN), is proposed for modeling non-stationary data streams. As an extension of evolving stochastic connfiguration networks (eSCNs), this work contributes a way to grow and shrink the structure of deep stochastic configuration networks autonomously from data streams. The performance of DSSCN is evaluated by six benchmark datasets. Simulation results, compared with prominent data stream algorithms, show that the proposed method is capable of achieving comparable accuracy and evolving compact and parsimonious deep stacked network architecture.

There has been a lot of recent interest in adopting machine learning methods for scientific and engineering applications. This has in large part been inspired by recent successes and advances in the domains of Natural Language Processing (NLP) and Image Classification (IC). However, scientific and engineering problems have their own unique characteristics and requirements raising new challenges for effective design and deployment of machine learning approaches. There is a strong need for further mathematical developments on the foundations of machine learning methods to increase the level of rigor of employed methods and to ensure more reliable and interpretable results. Also as reported in the recent literature on state-of-the-art results and indicated by the No Free Lunch Theorems of statistical learning theory incorporating some form of inductive bias and domain knowledge is essential to success. Consequently, even for existing and widely used methods there is a strong need for further mathematical work to facilitate ways to incorporate prior scientific knowledge and related inductive biases into learning frameworks and algorithms. We briefly discuss these topics and discuss some ideas proceeding in this direction.

In large scale coverage operations, such as marine exploration or aerial monitoring, single robot approaches are not ideal, as they may take too long to cover a large area. In such scenarios, multi-robot approaches are preferable. Furthermore, several real world vehicles are non-holonomic, but can be modeled using Dubins vehicle kinematics. This paper focuses on environmental monitoring of aquatic environments using Autonomous Surface Vehicles (ASVs). In particular, we propose a novel approach for solving the problem of complete coverage of a known environment by a multi-robot team consisting of Dubins vehicles. It is worth noting that both multi-robot coverage and Dubins vehicle coverage are NP-complete problems. As such, we present two heuristics methods based on a variant of the traveling salesman problem -- k-TSP -- formulation and clustering algorithms that efficiently solve the problem. The proposed methods are tested both in simulations to assess their scalability and with a team of ASVs operating on a lake to ensure their applicability in real world.

We study instancewise feature importance scoring as a method for model interpretation. Any such method yields, for each predicted instance, a vector of importance scores associated with the feature vector. Methods based on the Shapley score have been proposed as a fair way of computing feature attributions of this kind, but incur an exponential complexity in the number of features. This combinatorial explosion arises from the definition of the Shapley value and prevents these methods from being scalable to large data sets and complex models. We focus on settings in which the data have a graph structure, and the contribution of features to the target variable is well-approximated by a graph-structured factorization. In such settings, we develop two algorithms with linear complexity for instancewise feature importance scoring. We establish the relationship of our methods to the Shapley value and another closely related concept known as the Myerson value from cooperative game theory. We demonstrate on both language and image data that our algorithms compare favorably with other methods for model interpretation.

The Venezuelan government's account of Saturday's attempted assassination-by-drone of President Nicolás Maduro has raised more questions than it has answered. Here's what we know: A video of the event shows the chaos that ensued when the drones exploded, with Maduro abruptly stopping his speech and soldiers in the crowd fleeing. The president was unharmed, but seven National Guard soldiers were injured. Hours later, Maduro appeared on national television to accuse the outgoing Colombian president, Juan Manuel Santos, and far-right elements in the U.S. of plotting the alleged attack. A little-known group called "Soldados de Franelas" claimed responsibility for the attack on Twitter. The group regularly posts anti-Maduro content but little else is known about them.