Research into drugs to treat mosquito-borne flaviviruses such as Zika and dengue as well as COVID-19will benefit from a major funding boost, says a group of international scientists using artificial intelligence to discover new oral antivirals. A research consortium led by the non-profit COVID Moonshot has been awarded more than US$68 million from the US National Institutes of Health (NIH) to discover and develop globally accessible and affordable novel oral antivirals to combat COVID-19 and future pandemics. The development comes as monkeypox outbreaks have been declared around the world, raising concerns about the rapid spread of such viruses. Monkeypox is a viral disease that the World Health Organization says has emerged in at least 23 countries where the disease is not regularly found since 13 May. The open-science COVID Moonshot was established in 2020 with the objective of developing a safe, globally accessible and affordable antiviral pill for COVID-19.

The explanation to an AI model's prediction used to support decision making in cyber security, is of critical importance. It is especially so when the model's incorrect prediction can lead to severe damages or even losses to lives and critical assets. However, most existing AI models lack the ability to provide explanations on their prediction results, despite their strong performance in most scenarios. In this work, we propose a novel explainable AI method, called PhilaeX, that provides the heuristic means to identify the optimized subset of features to form the complete explanations of AI models' predictions. It identifies the features that lead to the model's borderline prediction, and those with positive individual contributions are extracted. The feature attributions are then quantified through the optimization of a Ridge regression model. We verify the explanation fidelity through two experiments. First, we assess our method's capability in correctly identifying the activated features in the adversarial samples of Android malwares, through the features attribution values from PhilaeX. Second, the deduction and augmentation tests, are used to assess the fidelity of the explanations. The results show that PhilaeX is able to explain different types of classifiers correctly, with higher fidelity explanations, compared to the state-of-the-arts methods such as LIME and SHAP.

Oyinmiebi Elena Ebikefe is a robotics and artificial intelligence (AI) engineer from Nigeria. She holds a first-class honours degree in electrical and electronics engineering, which led her to explore industrial automation, where she subsequently became hooked on robotics and AI. She went on to secure an MSc in Control, Automation and Artificial Intelligence at Coventry University and is open to robotics opportunities whilst she continues to develop her career through self-directed study. "Robotics uncovered a whole new side of me -- I keep surprising myself with the level of discipline and focus to study, dedication, investment and results I have gained with each robotics project." My name is Oyinmiebi Elena Ebikefe, and I'm a robotics and artificial intelligence engineer from Nigeria.

FIFA has confirmed that a semi-automated offside system will be used at this year's football World Cup in Qatar. The new technology utilises a limb-tracking camera system to track player movements and a sensor in the ball. It then quickly shows 3D images on stadium screens at the tournament to help fans understand the referee's call. It is the third World Cup in a dispute that will see FIFA introduce new technology to help referees. The optical tracking system was trialled at the FIFA Club World Cup in Abu Dhabi earlier this year and had also been tested at the Arab Cup in Qatar last December.

In this paper we consider the machine learning (ML) task of predicting tipping point transitions and long-term post-tipping-point behavior associated with the time evolution of an unknown (or partially unknown), non-stationary, potentially noisy and chaotic, dynamical system. We focus on the particularly challenging situation where the past dynamical state time series that is available for ML training predominantly lies in a restricted region of the state space, while the behavior to be predicted evolves on a larger state space set not fully observed by the ML model during training. In this situation, it is required that the ML prediction system have the ability to extrapolate to different dynamics past that which is observed during training. We investigate the extent to which ML methods are capable of accomplishing useful results for this task, as well as conditions under which they fail. In general, we found that the ML methods were surprisingly effective even in situations that were extremely challenging, but do (as one would expect) fail when ``too much" extrapolation is required. For the latter case, we investigate the effectiveness of combining the ML approach with conventional modeling based on scientific knowledge, thus forming a hybrid prediction system which we find can enable useful prediction even when its ML-based and knowledge-based components fail when acting alone. We also found that achieving useful results may require using very carefully selected ML hyperparameters and we propose a hyperparameter optimization strategy to address this problem. The main conclusion of this paper is that ML-based approaches are promising tools for predicting the behavior of non-stationary dynamical systems even in the case where the future evolution (perhaps due to the crossing of a tipping point) includes dynamics on a set outside of that explored by the training data.

You might not notice it, but you've likely adopted artificial intelligence into your daily life. It can be as simple as personalizing your news feeds, searching for products on shopping sites or voice-to-text conversion on smartphones. It can also be applied to more sophisticated tasks like predicting court outcomes in cases involving employment law or used for robotic welding applications. The transformative power of AI is also an economic growth driver, which is why the Canadian government has given the green light to advancing the country's AI strategy. According to a recent announcement from Minister of Innovation, Science and Industry François-Philippe Champagne, more than $443 million in Budget 2021 is designated for the second phase of the pan-Canadian Artificial Intelligence Strategy.

We explained the 5 Ws of artificial intelligence in developing countries. In recent years, artificial intelligence hasn't had a very favorable reputation overall. It is considered a threat to human employment opportunities even though we use artificial intelligence in everyday life. Is artificial intelligence better than human intelligence? The answer to this question will differ from person to person, but there is something that cannot be denied.

Procrastination, the irrational delay of tasks, is a common occurrence in online learning. Potential negative consequences include higher risk of drop-outs, increased stress, and reduced mood. Due to the rise of learning management systems and learning analytics, indicators of such behavior can be detected, enabling predictions of future procrastination and other dilatory behavior. However, research focusing on such predictions is scarce. Moreover, studies involving different types of predictors and comparisons between the predictive performance of various methods are virtually non-existent. In this study, we aim to fill these research gaps by analyzing the performance of multiple machine learning algorithms when predicting the delayed or timely submission of online assignments in a higher education setting with two categories of predictors: subjective, questionnaire-based variables and objective, log-data based indicators extracted from a learning management system. The results show that models with objective predictors consistently outperform models with subjective predictors, and a combination of both variable types perform slightly better. For each of these three options, a different approach prevailed (Gradient Boosting Machines for the subjective, Bayesian multilevel models for the objective, and Random Forest for the combined predictors). We conclude that careful attention should be paid to the selection of predictors and algorithms before implementing such models in learning management systems.

Advocates for Neuro-Symbolic Artificial Intelligence (NeSy) assert that combining deep learning with symbolic reasoning will lead to stronger AI than either paradigm on its own. As successful as deep learning has been, it is generally accepted that even our best deep learning systems are not very good at abstract reasoning. And since reasoning is inextricably linked to language, it makes intuitive sense that Natural Language Processing (NLP), would be a particularly well-suited candidate for NeSy. We conduct a structured review of studies implementing NeSy for NLP, with the aim of answering the question of whether NeSy is indeed meeting its promises: reasoning, out-of-distribution generalization, interpretability, learning and reasoning from small data, and transferability to new domains. We examine the impact of knowledge representation, such as rules and semantic networks, language structure and relational structure, and whether implicit or explicit reasoning contributes to higher promise scores. We find that systems where logic is compiled into the neural network lead to the most NeSy goals being satisfied, while other factors such as knowledge representation, or type of neural architecture do not exhibit a clear correlation with goals being met. We find many discrepancies in how reasoning is defined, specifically in relation to human level reasoning, which impact decisions about model architectures and drive conclusions which are not always consistent across studies. Hence we advocate for a more methodical approach to the application of theories of human reasoning as well as the development of appropriate benchmarks, which we hope can lead to a better understanding of progress in the field. We make our data and code available on github for further analysis.

As Ben Buchanan and Andrew Imbrie note in their recent book, "AI's [artificial intelligence's] new capabilities are both marvels and distractions." The marvel versus distraction dichotomy is an interesting one: due to the two possible natures of AI, the question of whether advances in AI will favor autocracies or democracies has come to the forefront of the tech and global power debate. On the one hand, AI has the potential to tackle some of the world's most challenging social problems, such as issues related to healthcare, the environment, and crisis response, leading some to believe that democracies will wield AI to create a future for human good. On the other hand, some fear AI-enabled surveillance, information campaigns, and cyber operations will empower existing tyrants and produce new ones, leading to a future where autocracies thrive and democracies struggle. By examining how advances in AI capabilities in the near future could benefit autocracies and democracies, as well as how these advances could benefit both, I believe that AI is likely to favor autocracies in the near term, but under one necessary condition: that democracies are negligent in their response to autocracies' destructive use of AI.