Actionable Cognitive Twins are the next generation Digital Twins enhanced with cognitive capabilities through a knowledge graph and artificial intelligence models that provide insights and decision-making options to the users. The knowledge graph describes the domain-specific knowledge regarding entities and interrelationships related to a manufacturing setting. It also contains information on possible decision-making options that can assist decision-makers, such as planners or logisticians. In this paper, we propose a knowledge graph modeling approach to construct actionable cognitive twins for capturing specific knowledge related to demand forecasting and production planning in a manufacturing plant. The knowledge graph provides semantic descriptions and contextualization of the production lines and processes, including data identification and simulation or artificial intelligence algorithms and forecasts used to support them. Such semantics provide ground for inferencing, relating different knowledge types: creative, deductive, definitional, and inductive. To develop the knowledge graph models for describing the use case completely, systems thinking approach is proposed to design and verify the ontology, develop a knowledge graph and build an actionable cognitive twin. Finally, we evaluate our approach in two use cases developed for a European original equipment manufacturer related to the automotive industry as part of the European Horizon 2020 project FACTLOG.

Researchers have used advanced AI and large sets of genomic data to unveil how humans have adapted to recent diseases. The method could also be applied to new pathogens such as the coronavirus that causes COVID-19, helping identify which gene mutations may be associated with more severe cases of the disease. The study, by researchers from Imperial College London, the Middle East Technical University, Turkey, and the Universita degli Studi di Bari Aldo Moro, Italy, is published today in a Special Issue of Molecular Ecology Resources, "Machine Learning techniques in Evolution and Ecology." Natural selection is the process by which beneficial gene mutations are preserved from generation to generation, until they become dominant in our genomes--the catalog of all our genes. One thing that can drive natural selection is protection against pathogens.

The digital revolution is here, but not everyone is benefiting equitably from it. And as Silicon Valley's ethos of "move fast and break things" spreads around the world, now is the time to pause and consider who is being left out and how we can better distribute the benefits of our new data economy. "Data is the main resource of a new digital economy," says Parminder Singh, executive director at nonprofit organization IT for Change. Global society will benefit because the economy will benefit, argues Singh, on decentralization of data and distributed digital models. Data commons--or open data sources--are vital to help build an equitable digital economy, but with that comes the challenge of data governance. "Not everybody is sharing data," says Singh. Big tech companies are holding onto the data, which stymies the growth of an open data economy, but also the growth of society, education, science, in other words, everything. According to Singh, "Data is a non-rival resource. It's not a material resource that if one uses it, other can't use it." Singh continues, "If all people can use the resource of data, obviously people can build value over it and the overall value available to the world, to a country, increases manifold because the same asset is available to everyone." One doesn't have to look very far to understand the value of non-personal data collected to help the public, consider GIS data from government satellites. Innovation plus the open access to geographic data helped not only create the Internet we know today, but those same tech companies.

AI researchers are constantly coming up with ways to make the technology serve us better. However, while these services are impressive they can be a tad creepy. A time when social media will be able to like pictures on our behalf, based on how our brain reacts to the picture sounds quite uncomfortable. Although Instagram isn't working on an algorithm that tracks brain waves, researchers from the University of Helsinki and Copenhagen University are. This AI matchmaker can tell when you find someone attractive.

A vastly growing literature on explaining deep learning models has emerged. This paper contributes to that literature by introducing a global gradient-based model-agnostic method, which we call Marginal Attribution by Conditioning on Quantiles (MACQ). Our approach is based on analyzing the marginal attribution of predictions (outputs) to individual features (inputs). Specificalllly, we consider variable importance by mixing (global) output levels and, thus, explain how features marginally contribute across different regions of the prediction space. Hence, MACQ can be seen as a marginal attribution counterpart to approaches such as accumulated local effects (ALE), which study the sensitivities of outputs by perturbing inputs. Furthermore, MACQ allows us to separate marginal attribution of individual features from interaction effect, and visually illustrate the 3-way relationship between marginal attribution, output level, and feature value.

Recently, autonomous driving has made substantial progress in addressing the most common traffic scenarios like intersection navigation and lane changing. However, most of these successes have been limited to scenarios with well-defined traffic rules and require minimal negotiation with other vehicles. In this paper, we introduce a previously unconsidered, yet everyday, high-conflict driving scenario requiring negotiations between agents of equal rights and priorities. There exists no centralized control structure and we do not allow communications. Therefore, it is unknown if other drivers are willing to cooperate, and if so to what extent. We train policies to robustly negotiate with opposing vehicles of an unobservable degree of cooperativeness using multi-agent reinforcement learning (MARL). We propose Discrete Asymmetric Soft Actor-Critic (DASAC), a maximum-entropy off-policy MARL algorithm allowing for centralized training with decentralized execution. We show that using DASAC we are able to successfully negotiate and traverse the scenario considered over 99% of the time. Our agents are robust to an unknown timing of opponent decisions, an unobservable degree of cooperativeness of the opposing vehicle, and previously unencountered policies. Furthermore, they learn to exhibit human-like behaviors such as defensive driving, anticipating solution options and interpreting the behavior of other agents.

With the success of large-scale pre-training and multilingual modeling in Natural Language Processing (NLP), recent years have seen a proliferation of large, web-mined text datasets covering hundreds of languages. However, to date there has been no systematic analysis of the quality of these publicly available datasets, or whether the datasets actually contain content in the languages they claim to represent. In this work, we manually audit the quality of 205 language-specific corpora released with five major public datasets (CCAligned, ParaCrawl, WikiMatrix, OSCAR, mC4), and audit the correctness of language codes in a sixth (JW300). We find that lower-resource corpora have systematic issues: at least 15 corpora are completely erroneous, and a significant fraction contains less than 50% sentences of acceptable quality. Similarly, we find 82 corpora that are mislabeled or use nonstandard/ambiguous language codes. We demonstrate that these issues are easy to detect even for non-speakers of the languages in question, and supplement the human judgements with automatic analyses. Inspired by our analysis, we recommend techniques to evaluate and improve multilingual corpora and discuss the risks that come with low-quality data releases.

We take a step towards addressing the under-representation of the African continent in NLP research by creating the first large publicly available high-quality dataset for named entity recognition (NER) in ten African languages, bringing together a variety of stakeholders. We detail characteristics of the languages to help researchers understand the challenges that these languages pose for NER. We analyze our datasets and conduct an extensive empirical evaluation of state-of-the-art methods across both supervised and transfer learning settings. We release the data, code, and models in order to inspire future research on African NLP.

Artificial intelligence (AI) is the ultimate manifestation of the promise of technology to improve our lives. Within our lifetimes AI will become omnipresent across every segment of economic activity and within every field of human endeavour. But AI is not a single genus but a constellation of technologies that must work together in harmony. By 2030 over 50 billion devices will be connected to the Internet of Things and will include new sensor technologies which will capture and create new modalities of data. Mastering this constellation of artificial intelligence technologies is a rainbow of minds from data scientists to robot builders and AI investors.

Academic research in board game playing AI has of course moved While artificial intelligence has been applied to control players' beyond most pedestrian board games, applying a diverse set of decisions in board games for over half a century, little attention algorithms for playing card games with millions of card combinations is given to games with no player competition. Pandemic is an exemplar such as Magic: the Gathering (Wizards of the Coast, 1993) [3], collaborative board game where all players coordinate to games of tactical card placement such as Lords of War (Black Box, overcome challenges posed by events occurring during the game's 2012) [19] and Carcassonne (Hans im Glück, 2000) [9], card games progression. This paper proposes an artificial agent which controls of team-based competition such as Hanabi (Abacusspiele, 2010) [26] all players' actions and balances chances of winning versus risk or Codenames (Czech Games Edition, 2015) [22], and many more. of losing in this highly stochastic environment. The agent applies Traditional board games such as chess [15] and backgammon a Rolling Horizon Evolutionary Algorithm on an abstraction of [23], as well as recent card games such as Race for the Galaxy (Rio the game-state that lowers the branching factor and simulates the Grande, 2007) [6] or digitized board games such as Hearthstone game's stochasticity. Results show that the proposed algorithm (Blizzard, 2014) [11, 18], focus on players competing to deplete another can find winning strategies more consistently in different games player's resources (pawns, hit points) or to accumulate more of varying difficulty.