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.

Recent developments in image classification and natural language processing, coupled with the rapid growth in social media usage, have enabled fundamental advances in detecting breaking events around the world in real-time. Emergency response is one such area that stands to gain from these advances. By processing billions of texts and images a minute, events can be automatically detected to enable emergency response workers to better assess rapidly evolving situations and deploy resources accordingly. To date, most event detection techniques in this area have focused on image-only or text-only approaches, limiting detection performance and impacting the quality of information delivered to crisis response teams. In this paper, we present a new multimodal fusion method that leverages both images and texts as input. In particular, we introduce a cross-attention module that can filter uninformative and misleading components from weak modalities on a sample by sample basis. In addition, we employ a multimodal graph-based approach to stochastically transition between embeddings of different multimodal pairs during training to better regularize the learning process as well as dealing with limited training data by constructing new matched pairs from different samples. We show that our method outperforms the unimodal approaches and strong multimodal baselines by a large margin on three crisis-related tasks.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Are we moving a bit closer to the day when machines can read our minds? Scientists have developed a system using artificial intelligence that can turn brain activity into text. At the moment, it works on neural patterns interpreted when someone speaks aloud, but there is hope among researchers that it could eventually be used for patients who are unable to speak or type. "We are not there yet but we think this could be the basis of a speech prosthesis," Joseph Makin, co-author of the research from the University of California, San Francisco, told The Guardian.

With the spiraling pandemic of the Coronavirus Disease 2019 (COVID-19), it has becoming inherently important to disseminate accurate and timely information about the disease. Due to the ubiquity of Internet connectivity and smart devices, social sensing is emerging as a dynamic sensing paradigm to collect real-time observations from online users. In this vision paper we propose CovidSens, the concept of social-sensing-based risk alerting systems to notify the general public about the COVID-19 spread. The CovidSens concept is motivated by two recent observations: 1) people have been actively sharing their state of health and experience of the COVID-19 via online social media, and 2) official warning channels and news agencies are relatively slower than people reporting their observations and experiences about COVID-19 on social media. We anticipate an unprecedented opportunity to leverage the posts generated by the social media users to build a real-time analytic system for gathering and circulating vital information of the COVID-19 propagation. Specifically, the vision of CovidSens attempts to answer the questions of: how to track the spread of the COVID-19? How to distill reliable information about the disease with the coexistence of prevailing rumors and misinformation in the social media? How to inform the general public about the latest state of the spread timely and effectively and alert them to remain prepared? In this vision paper, we discuss the roles of CovidSens and identify the potential challenges in implementing reliable social-sensing-based risk alerting systems. We envision that approaches originating from multiple disciplines (e.g. estimation theory, machine learning, constrained optimization) can be effective in addressing the challenges. Finally, we outline a few research directions for future work in CovidSens.

In this paper, we propose a dimensionality reduction method applied to tensor-structured data as a hidden layer (we call it TensorProjection Layer) in a convolutional neural network. Our proposed method transforms input tensors into ones with a smaller dimension by projection. The directions of projection are viewed as training parameters associated with our proposed layer and trained via a supervised learning criterion such as minimization of the cross-entropy loss function. We discuss the gradients of the loss function with respect to the parameters associated with our proposed layer. We also implement simple numerical experiments to evaluate the performance of the TensorProjection Layer.