The UK government is setting aside £2.6 million for new satellite and drone technology that could deliver essential supplies during the coronavirus lockdown. The UK Space Agency (UKSA) is funding new solutions to deliver equipment such as test kits, masks, gowns and goggles for frontline NHS staff. The joint initiative with the European Space Agency could lead to vital equipment soaring through British skies via drones to support the NHS in tackling COVID-19. Companies can submit their proposals, including ideas for deployment and a pilot phase, on the European Space Agency (ESA) website. The UK's space industry is also looking for ways to combat the spread of coronavirus and preventing future epidemics using satellites.

We study cost-effective communication strategies that can be used to improve the performance of distributed learning systems in resource-constrained environments. For distributed learning in sequential decision making, we propose a new cost-effective partial communication protocol. We illustrate that with this protocol the group obtains the same order of performance that it obtains with full communication. Moreover, we prove that under the proposed partial communication protocol the communication cost is $O(\log T)$, where $T$ is the time horizon of the decision-making process. This improves significantly on protocols with full communication, which incur a communication cost that is $O(T)$. We validate our theoretical results using numerical simulations.

The reliability of most deep learning algorithms is fundamentally challenged by the existence of adversarial examples, which are incorrectly classified inputs that are extremely close to a correctly classified input. We study adversarial examples for deep neural networks with random weights and biases and prove that the $\ell^1$ distance of any given input from the classification boundary scales at least as $\sqrt{n}$, where $n$ is the dimension of the input. We also extend our proof to cover all the $\ell^p$ norms. Our results constitute a fundamental advance in the study of adversarial examples, and encompass a wide variety of architectures, which include any combination of convolutional or fully connected layers with skipped connections and pooling. We validate our results with experiments on both random deep neural networks and deep neural networks trained on the MNIST and CIFAR10 datasets. Given the results of our experiments on MNIST and CIFAR10, we conjecture that the proof of our adversarial robustness guarantee can be extended to trained deep neural networks. This extension will open the way to a thorough theoretical study of neural network robustness by classifying the relation between network architecture and adversarial distance.

The treatment of malaria is a global health challenge that stands to benefit from the widespread introduction of a vaccine for the disease. A method has been developed to create a live organism vaccine using the sporozoites (SPZ) of the parasite Plasmodium falciparum (Pf), which are concentrated in the salivary glands of infected mosquitoes. Current manual dissection methods to obtain these PfSPZ are not optimally efficient for large-scale vaccine production. We propose an improved dissection procedure and a mechanical fixture that increases the rate of mosquito dissection and helps to deskill this stage of the production process. We further demonstrate the automation of a key step in this production process, the picking and placing of mosquitoes from a staging apparatus into a dissection assembly. This unit test of a robotic mosquito pick-and-place system is performed using a custom-designed micro-gripper attached to a four degree of freedom (4-DOF) robot under the guidance of a computer vision system. Mosquitoes are autonomously grasped and pulled to a pair of notched dissection blades to remove the head of the mosquito, allowing access to the salivary glands. Placement into these blades is adapted based on output from computer vision to accommodate for the unique anatomy and orientation of each grasped mosquito. In this pilot test of the system on 50 mosquitoes, we demonstrate a 100% grasping accuracy and a 90% accuracy in placing the mosquito with its neck within the blade notches such that the head can be removed. This is a promising result for this difficult and non-standard pick-and-place task.

This article is part 2 of a series reviewing selected papers from Altmetric's list of the top 100 most-discussed scholarly works of 2019. Deepfake is a term for videos and presentations enhanced by artificial intelligence and other modern technology to present falsified results. One of the best examples of deepfakes involves the use of image processing to produce video of celebrities, politicians or others saying or doing things that they never actually said or did. A September 2019 Deeptrace report1 on the state of deepfakes has found that since its emergence in late 2017, the phenomenon of deepfakes has been developing very quickly, with rapidly growing societal impact and technological sophistication. At the time of report publication, there were 14,678 deepfake videos online, 96% of which had pornographic content. While their use in a pornographic context continues to grow, deepfakes are also increasingly being used for the purpose of political disinformation.

In this editorial, we take a look at a post-pandemic dystopia where AI and robotics engulf the flow of labour, talent and the economy. These are our forecasts of an economy optimised by artificial intelligence and ripened by robotics. Robots' infiltration of the workforce doesn't occur at a steady, gradual pace. Instead, automation happens in bursts, concentrated especially in bad times such as the current Covid 19-induced economic paralysis, when humans become relatively more expensive as firms' revenues rapidly decline. At these moments, employers shed less-skilled workers and replace them with technology and higher-skilled workers, which increases labor productivity as a recession tapers off.

Robots could have a role to play in COVID-19, whether it's automating laboratory research, helping with logistics, disinfecting hospitals, education, or allowing carers, colleagues or loved ones to connect using telepresence. Yet many of these solutions are still in development or early deployment. The hope is that accelerating these translations could make a difference. This page aims to compile some resources for roboticists who are able to help, users who need robots for COVID-19 applications, and people who want to learn about robotics while on lockdown. This is not an exhaustive resource page, and we will regularly be updating the content.

ICT industry veteran Andile Ngcaba's inq., a Pan-African digital service provider, wants to be Africa's number one artificial intelligence (AI) service provider. The company has points of contacts in 12 African cities, Johannesburg, Gaborone, Lusaka, Ndola, Blantyre, Lilongwe, Mzuzu, Lagos, Abuja, Port Harcourt, Kanu and Abidjan. It has concluded the 100% acquisition of Vodacom Business Africa's operations in Nigeria, Zambia and Cote d'Ivoire with a further planned acquisition in Cameroon pending regulatory approvals. At the time of the announcement of the transaction last June, inq. said this deals represents a significant milestone to its vision to be a leading provider of cloud and digitally based services in key markets across sub-Saharan Africa and provides additional vital assets in its build-out of a regional footprint. Today, inq. said this landmark transaction grows inq.'s regional footprint to 13 cities in 7 countries across Africa including its existing operations in Botswana, Malawi and Mozambique.

Humans have often sought the fantasy of having someone who "understands" them. Be it a fellow companion, a pet or even a machine. No doubt man is a social animal. Yet, this may not be the exact case in case of a man engineered machine or system. Although, machines are now equipped with AI that helps them beat us by sifting through scores of data and analyze them, provide a logical solution when it comes to emotional IQ this is where man and the machine draw the line.

Most algorithms for representation learning and link prediction in relational data have been designed for static data. However, the data they are applied to usually evolves with time, such as friend graphs in social networks or user interactions with items in recommender systems. This is also the case for knowledge bases, which contain facts such as (US, has president, B. Obama, [2009-2017]) that are valid only at certain points in time. For the problem of link prediction under temporal constraints, i.e., answering queries such as (US, has president, ?, 2012), we propose a solution inspired by the canonical decomposition of tensors of order 4. We introduce new regularization schemes and present an extension of ComplEx (Trouillon et al., 2016) that achieves state-of-the-art performance. Additionally, we propose a new dataset for knowledge base completion constructed from Wikidata, larger than previous benchmarks by an order of magnitude, as a new reference for evaluating temporal and non-temporal link prediction methods.