AI researchers say data sharing is a key part of economic growth in Africa but that it faces a number of common obstacles, including the threat of data colonialism. The African data market is expected to grow steadily in the coming years, and the African Data Centre trade organization predicts the African data market will need hundreds of new datacenters to meet demand in the coming decade. In a paper titled "Narratives and Counternarratives on Data Sharing in Africa," the research team lays out structural problems including but limited to financial or infrastructure problems. Coauthors argue that failure to consider ethical concerns associated with those obstacles could cause irreparable harm. "Currently, a significant proportion of Africa's digital infrastructure is controlled by Western technology powers, such as Amazon, Google, Facebook, and Uber," the paper reads.

For all their simplicity, viruses are sneaky little life forces. Take SARS-Cov-2, the virus behind Covid-19. Challenged with the human immune system, the virus has gradually reshuffled parts of its genetic material, making it easier to spread among a human population. The new strain has already terrorized South Africa and shut down the UK, and recently popped up in the United States. The silver lining is that our existing vaccines and antibody therapies are still likely to be effective against the new strain. "Viral escape" is a nightmare scenario, in which the virus mutates just enough so that existing antibodies no longer recognize it.

Logic-based argumentation is a well-established formalism modelling nonmonotonic reasoning. It has been playing a major role in AI for decades, now. Informally, a set of formulas is the support for a given claim if it is consistent, subset-minimal, and implies the claim. In such a case, the pair of the support and the claim together is called an argument. In this paper, we study the propositional variants of the following three computational tasks studied in argumentation: ARG (exists a support for a given claim with respect to a given set of formulas), ARG-Check (is a given set a support for a given claim), and ARG-Rel (similarly as ARG plus requiring an additionally given formula to be contained in the support). ARG-Check is complete for the complexity class DP, and the other two problems are known to be complete for the second level of the polynomial hierarchy (Parson et al., J. Log. Comput., 2003) and, accordingly, are highly intractable. Analyzing the reason for this intractability, we perform a two-dimensional classification: first, we consider all possible propositional fragments of the problem within Schaefer's framework (STOC 1978), and then study different parameterizations for each of the fragment. We identify a list of reasonable structural parameters (size of the claim, support, knowledge-base) that are connected to the aforementioned decision problems. Eventually, we thoroughly draw a fine border of parameterized intractability for each of the problems showing where the problems are fixed-parameter tractable and when this exactly stops. Surprisingly, several cases are of very high intractability (paraNP and beyond).

Corruption continues to be one of the biggest societal challenges of our time. New hope is placed in Artificial Intelligence (AI) to serve as an unbiased anti-corruption agent. Ever more available (open) government data paired with unprecedented performance of such algorithms render AI the next frontier in anti-corruption. Summarizing existing efforts to use AI-based anti-corruption tools (AI-ACT), we introduce a conceptual framework to advance research and policy. It outlines why AI presents a unique tool for top-down and bottom-up anti-corruption approaches. For both approaches, we outline in detail how AI-ACT present different potentials and pitfalls for (a) input data, (b) algorithmic design, and (c) institutional implementation. Finally, we venture a look into the future and flesh out key questions that need to be addressed to develop AI-ACT while considering citizens' views, hence putting "society in the loop".

Since the outbreak of the pandemic, the world has grown increasingly reliant on artificial intelligence (AI) technologies. Thousands of new innovations -- from contact-tracing apps to the drones delivering medical equipment -- sprang up to help us meet the challenges of Covid-19 and life under lockdown. The unprecedented speed with which a vaccine for Covid-19 was discovered can partly be attributed to the use of AI algorithms which rapidly crunched the data from thousands of clinical trials, allowing researchers around the world to compare notes in real time. As Satya Nadella, the chief executive of Microsoft observed, in just two months, the world witnessed a rate of digital transition we'd usually only see in two years. In 2017, PWC published a study showing that adoption of AI technologies could increase global GDP by 14% by 2030. In addition to creating jobs and boosting economies, AI technologies have the potential to drive sustainable development and even out inequalities, democratising access to healthcare and education, mitigating the effects of climate change and making food production and distribution more efficient.

We deploy and demonstrate the CoinTossX low-latency, high-throughput, open-source matching engine with orders sent using the Julia and Python languages. We show how this can be deployed for small-scale local desk-top testing and discuss a larger scale, but local hosting, with multiple traded instruments managed concurrently and managed by multiple clients. We then demonstrate a cloud based deployment using Microsoft Azure, with large-scale industrial and simulation research use cases in mind. The system is exposed and interacted with via sockets using UDP SBE message protocols and can be monitored using a simple web browser interface using HTTP. We give examples showing how orders can be be sent to the system and market data feeds monitored using the Julia and Python languages. The system is developed in Java with orders submitted as binary encodings (SBE) via UDP protocols using the Aeron Media Driver as the low-latency, high throughput message transport. The system separates the order-generation and simulation environments e.g. agent-based model simulation, from the matching of orders, data-feeds and various modularised components of the order-book system. This ensures a more natural and realistic asynchronicity between events generating orders, and the events associated with order-book dynamics and market data-feeds. We promote the use of Julia as the preferred order submission and simulation environment.

Individual behavior and decisions are substantially influenced by their contexts, such as location, environment, and time. Changes along these dimensions can be readily observed in Multiplayer Online Battle Arena games (MOBA), where players face different in-game settings for each match and are subject to frequent game patches. Existing methods utilizing contextual information generalize the effect of a context over the entire population, but contextual information tailored to each individual can be more effective. To achieve this, we present the Neural Individualized Context-aware Embeddings (NICE) model for predicting user performance and game outcomes. Our proposed method identifies individual behavioral differences in different contexts by learning latent representations of users and contexts through non-negative tensor factorization. Using a dataset from the MOBA game League of Legends, we demonstrate that our model substantially improves the prediction of winning outcome, individual user performance, and user engagement.

Transformers are arguably the main workhorse in recent Natural Language Processing research. By definition a Transformer is invariant with respect to reorderings of the input. However, language is inherently sequential and word order is essential to the semantics and syntax of an utterance. In this paper, we provide an overview of common methods to incorporate position information into Transformer models. The objectives of this survey are to i) showcase that position information in Transformer is a vibrant and extensive research area; ii) enable the reader to compare existing methods by providing a unified notation and meaningful clustering; iii) indicate what characteristics of an application should be taken into account when selecting a position encoding; iv) provide stimuli for future research. The Transformer model as introduced by Vaswani et al. (2017) has been found to perform well for many tasks, such as machine translation or language modeling. With the rise of pretrained language models (PLMs) (Peters et al., 2018; Howard & Ruder, 2018; Devlin et al., 2019; Brown et al., 2020) Transformer models have become even more popular. As a result they are at the core of many state of the art natural language processing (NLP) models. A Transformer model consists of several layers, or blocks. Each layer is a self-attention (Vaswani et al., 2017) module followed by a feed-forward layer. Layer normalization and residual connections are additional components of a layer.

Violence, whether it is focused on individuals such as crime or at larger scales such as war, seems to be almost inevitable in our world. However, researchers are asking if it can be prevented and if geospatial big data techniques, including machine learning methods, could potentially be used to prevent violence from going out of control. A recent World Economic Form blog has highlighted varied efforts that attempt to mitigate violence at different scales, with geospatial data often a core feature of different methods discussed in these tools.[1] Two tools have been recently developed that focus on small-scale acts of violence and harassment. Using crowdsourcing and hot spot mapping, Safecity and HarassMap[2] have been created, which depict recent trends of assaults, sexual harassment, and local crime to help individuals determine areas to avoid.

In computer vision, object detection is the problem of locating one or more objects in an image. Besides the traditional object detection techniques, advanced deep learning models like R-CNN and YOLO can achieve impressive detection over different types of objects. These models accept an image as the input and return the coordinates of the bounding box around each detected object. This tutorial discusses the confusion matrix, and how the precision, recall and accuracy are calculated. In another tutorial, the mAP will be discussed. In binary classification each input sample is assigned to one of two classes. Generally these two classes are assigned labels like 1 and 0, or positive and negative.