Starting from the COMET methodology by Bosselut et al. (2019), generating commonsense knowledge directly from pre-trained language models has recently received significant attention. Surprisingly, up to now no materialized resource of commonsense knowledge generated this way is publicly available. This paper fills this gap, and uses the materialized resources to perform a detailed analysis of the potential of this approach in terms of precision and recall. Furthermore, we identify common problem cases, and outline use cases enabled by materialized resources. We posit that the availability of these resources is important for the advancement of the field, as it enables an off-the-shelf-use of the resulting knowledge, as well as further analyses on its strengths and weaknesses.

Predicting the results of matches in sport is a challenging and interesting task. In this paper, we review a selection of studies from 1996 to 2019 that used machine learning for predicting match results in team sport. Considering both invasion sports and striking/fielding sports, we discuss commonly applied machine learning algorithms, as well as common approaches related to data and evaluation. Our study considers accuracies that have been achieved across different sports, and explores whether evidence exists to support the notion that outcomes of some sports may be inherently more difficult to predict. We also uncover common themes of future research directions and propose recommendations for future researchers. Although there remains a lack of benchmark datasets (apart from in soccer), and the differences between sports, datasets and features makes between-study comparisons difficult, as we discuss, it is possible to evaluate accuracy performance in other ways. Artificial Neural Networks were commonly applied in early studies, however, our findings suggest that a range of models should instead be compared. Selecting and engineering an appropriate feature set appears to be more important than having a large number of instances. For feature selection, we see potential for greater inter-disciplinary collaboration between sport performance analysis, a sub-discipline of sport science, and machine learning.

BY RAFFAELLA AGHEMO 2022 APRIL Joint Research Centre study for a correct taxonomy of Artificial Intelligence WRITTEN BY Raffaella Aghemo, Lawyer In the context of AI Watch, the knowledge service of the European Commission to monitor the development, adoption and impact of Artificial Intelligence (AI) for Europe, launched in December 2018, is the recent study carried out by the Joint Research Centre (JRC), entitled "AI Watch - Defining Artificial Intelligence 2.0 - Towards an operational definition and taxonomy for the AI landscape". Furthermore, in April 2021, the European Commission proposed a set of actions to promote excellence in AI and rules to ensure that the technology is trusted. The taxonomy of AI is a classification of the technology itself with concepts originating mainly in mathematics, logic, philosophy and information theory, which has Russel and Norvig's taxonomy as its starting point, and is found in several studies from different perspectives. The classification in question first divides AI into weak or narrow AI (Weak or Narrow AI - ANI) and strong or General AI (AGI). ANI is the type of AI that exists today.

Knowledge base question answering (KBQA) aims to answer a natural language question over a knowledge base (KB) as its knowledge source. A knowledge base (KB) is a structured database that contains a collection of facts in the form subject, relation, object, where each fact can have properties attached called qualifiers. For example, the sentence "Barack Obama got married to Michelle Obama on 3 October 1992 at Trinity United Church" can be represented by the tuple Barack Obama, Spouse, Michelle Obama, with the qualifiers start time 3 October 1992 and place of marriage Trinity United Church . Popular knowledge bases are DBpedia and WikiData. Early works on KBQA focused on simple question answering, where there's only a single fact involved.

For example, consider the case where the perception module detects a pedestrian (PCV) on the road. It does not, however, recognize that the pedestrian is jaywalking. Even if no jaywalking events have been seen while training the CV perception module, representing knowledge of this event – i.e. (Pedestrian, participatesIn, Jaywalking) – in the scene KG could provide a new insight or cue for handling this edge-case with KEP (i.e.

TikTok users will soon have even more ways to make their videos stand out from the crowd. The service has announced the TikTok Library, which will grant creators access to more entertainment-based content. You'll be able to find GIFs, clips from your favorite TV shows, memes and other content, which you can slot into your TikToks. Although there are already ways to insert GIFs from Giphy into TikTok videos, it should be easier to do that once you have access to the library. Until now, Giphy GIFs have been available as Stickers and via the Green Screen effect.

Deep neural networks (DNNs) have found their way into many applications with potential impact on the safety, security, and fairness of human-machine-systems. Such require basic understanding and sufficient trust by the users. This motivated the research field of explainable artificial intelligence (XAI), i.e. finding methods for opening the "black-boxes" DNNs represent. For the computer vision domain in specific, practical assessment of DNNs requires a globally valid association of human interpretable concepts with internals of the model. The research field of concept (embedding) analysis (CA) tackles this problem: CA aims to find global, assessable associations of humanly interpretable semantic concepts (e.g., eye, bearded) with internal representations of a DNN. This work establishes a general definition of CA and a taxonomy for CA methods, uniting several ideas from literature. That allows to easily position and compare CA approaches. Guided by the defined notions, the current state-of-the-art research regarding CA methods and interesting applications are reviewed. More than thirty relevant methods are discussed, compared, and categorized. Finally, for practitioners, a survey of fifteen datasets is provided that have been used for supervised concept analysis. Open challenges and research directions are pointed out at the end.

Research in multi-agent teaming has increased substantially over recent years, with knowledge-based systems to support teaming processes typically focused on delivering functional (communicative) solutions for a team to act meaningfully in response to direction. Enabling humans to effectively interact and team with a swarm of autonomous cognitive agents is an open research challenge in Human-Swarm Teaming research, partially due to the focus on developing the enabling architectures to support these systems. Typically, bi-directional transparency and shared semantic understanding between agents has not prioritised a designed mechanism in Human-Swarm Teaming, potentially limiting how a human and a swarm team can share understanding and information\textemdash data through concepts and contexts\textemdash to achieve a goal. To address this, we provide a formal knowledge representation design that enables the swarm Artificial Intelligence to reason about its environment and system, ultimately achieving a shared goal. We propose the Ontology for Generalised Multi-Agent Teaming, Onto4MAT, to enable more effective teaming between humans and teams through the biologically-inspired approach of shepherding.

The trend for an aging population, which is typical for Europe and for other high-income regions, brings with it a sharp increase in the number of chronic patients and a shortage of clinicians and hospital beds. Evidence-based clinical decision-support systems are one of the promising solutions for this problem.15 In the 1990s, different research groups started to develop computer-interpretable clinical guidelines (CIGs)7 as a form of evidence-based decision-support systems (DSS). Narrative evidence-based clinical guidelines, focused on a single disease, and containing recommendations for the disease diagnosis and management, were manually represented in CIG formalisms, such as Asbru,11 GLIF,1 or PROforma.3 The CIGs formed a network of clinical decisions and actions and served as a knowledge base.

Japan will strengthen its consultation system for fertility treatment as its public health insurance program starts covering such treatment in April. The health ministry plans to integrate related public consultation windows under a single system. The new facilities will help people with specialist advice and provide emotional support to women who feel anxious. In the fiscal 2022 revision of official medical fees, the public insurance coverage will be extended to fertility treatment such as in vitro fertilization and artificial insemination as part of efforts to shore up the country's falling birthrate. Thanks to this, costs of fertility treatment that have been fully paid by patients will be limited to 30% in principle.