"Computers are able to see,hear and learn.Welcome to the future." According to the World Economic Forum,more than 65% of students will work in jobs that don't even exist today.We want to help prepare them for that future by getting them excited about what computer science (CS) can take them.With a focus on girls and others who are underrepresented in the field today. Robotics and automation are dramatically reshaping the global economy.From delivering faster customer service to better quality products and efficient operations, robotics and automation provide enormous value for organizations that adopt them at scale. "Robots and automation will take 800 million jobs by 2030."-McKinsey.Using AI, the company hopes to teach the robot to copy human movements automatically, so that it can operate without a pilot. From the initially reported outbreak of coronavirus (COVID-19) in China to the spread of it across the globe, Medtech companies are rolling out robots and drones to help fight it and provide services and care to those quarantined or practicing social distancing. This pandemic has fast-tracked the "testing" of robots and drones in public as officials seek out the most expedient and safe way to grapple with the outbreak and limit contamination and spread of the virus.

In the pooled data problem we are given a set of $n$ agents, each of which holds a hidden state bit, either $0$ or $1$. A querying procedure returns for a query set the sum of the states of the queried agents. The goal is to reconstruct the states using as few queries as possible. In this paper we consider two noise models for the pooled data problem. In the noisy channel model, the result for each agent flips with a certain probability. In the noisy query model, each query result is subject to random Gaussian noise. Our results are twofold. First, we present and analyze for both error models a simple and efficient distributed algorithm that reconstructs the initial states in a greedy fashion. Our novel analysis pins down the range of error probabilities and distributions for which our algorithm reconstructs the exact initial states with high probability. Secondly, we present simulation results of our algorithm and compare its performance with approximate message passing (AMP) algorithms that are conjectured to be optimal in a number of related problems.

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.

In a previous article of February 6, 2020, we discussed the EPO Receiving Section's refusal, in January 2020, of two European patent applications where an AI system called DABUS was indicated as the inventor1 . We then looked at the grounds of the decisions2 (concerning applications EP 18 275 163 and EP 18 275 174 for "food container" and "devices and methods for attracting enhanced attention"), and predicted that the EPO Board of Appeal (BoA) was bound to shed light on the novel and intriguing legal issue of whether a non-human, such as an artificial intelligence (AI), could be named as inventor in the system of the EPC. The BoA has now issued its decision, which is worth commenting. The applicant, one Mr. Stephen Thaler, had filed his appeals against the refusal (cases J 8/20 and J 9/20), along with an auxiliary request whereby no person was allegedly identified as inventor, but a natural person was indicated to hold "the right to the European Patent by virtue of being the owner and creator of" the DABUS AI system. By decision of December 21, 20213, the BoA dismissed the appeal, confirming that the EPC required the inventor to be a person with legal capacity.

It's not a coincidence that two global multinational investment banks and financial services companies, Morgan Stanley and Goldman Sachs, agrees that the nascent metaverse market could be worth $8 trillion in the future. In its latest Technology Vision 2022 report, titled Meet me in the metaverse, multinational information technology services company, Accenture surveyed more than 4,600 business and technology leaders across 23 industries in 35 countries. Like an arms race, futuristic big tech companies Microsoft, Facebook (FB now Meta), and Apple Inc, Google (now Alphabet) amongst others, are scrambling to sweep up the metaverse. Facebook (now Meta) describes the metaverse as "a set of virtual spaces where you can create and explore with other people who aren't in the same physical space as you". CEO Mark Zuckerberg says Meta is working on egocentric data, which involves seeing worlds from a first-person perspective.

SUNNYVALE, Calif – April 13, 2022 -- Cerebras Systems, the pioneer in high performance artificial intelligence (AI) computing, today released version 1.2 of the Cerebras Software Platform, CSoft, with expanded support for PyTorch and TensorFlow. In addition, customers can now quickly and easily train models with billions of parameters via Cerebras' weight streaming technology. PyTorch is the leading machine learning framework. It is used by developers to accelerate the path from research prototyping to production deployment. As model size increases and as transformer models become more popular, it is essential that machine learning practitioners have access to fast, easy to set up and use compute solutions like the Cerebras CS-2.

It has been more than five months since the United States sanctioned the Israeli spyware company NSO Group, and stories about the use and abuse of its Pegasus product continue to break. As various organisations try to push for further measures against Israel for supplying human rights abusers with this tool to further their violations, it is important to remember that Israeli military and surveillance technology is first developed for and tested on Palestinians, before being exported. Unsurprisingly, Pegasus has already been found on the phones of six Palestinian human rights activists, one of whom is now suing NSO in France. Another target happened to be my friend and colleague whose field of work is directly connected to the relationship between Palestine and the International Criminal Court (ICC) in The Hague. The thought that the Israelis have had full access to our personal conversations and exchanges in group chats has been quite disturbing, to say the least. However, this is not the first time Israel has violated my privacy and it won't be the last.

Artificial Intelligence (AI) is transforming society as algorithms increasingly impact access to jobs and insurance, justice, medical treatments, as well as our daily interactions with friends and family. As these technologies improve, we are starting to see unintended social consequences: algorithms that promote everything from racial bias in healthcare to the misinformation eroding faith in democracies. To ensure the creation of ethical Artificial Intelligence that supports core human values, the German philanthropic foundation, Stiftung Mercator, has awarded a €3.8M grant to a collaboration between the University of Bonn and Cambridge University. Led by Professor Markus Gabriel from the Institute for Philosophy at Bonn and Dr Stephen Cave from the Leverhulme Centre for the Future of Intelligence at Cambridge, the project, 'Desirable Digitalisation: Rethinking AI for Just and Sustainable Futures', places ethical principles at the heart of AI development. The new research project comes as the European Commission negotiates its Artificial Intelligence Act, which has ambitions to ensure AI becomes more'trustworthy' and'human-centric'.

Detailed feature investigations and comparisons across climates, continents and time series types can progress our understanding and modelling ability of the Earth's hydroclimate and its dynamics. As a step towards these important directions, we here propose and extensively apply a multifaceted and engineering-friendly methodological framework for the thorough characterization of seasonal hydroclimatic dependence, variability and change at the global scale. We apply this framework using over 13 000 quarterly temperature, precipitation and river flow time series. In these time series, the seasonal hydroclimatic behaviour is represented by 3-month means of earth-observed variables. In our analyses, we also adopt the well-established Koppen-Geiger climate classification system and define continental-scale regions with large or medium density of observational stations. In this context, we provide in parallel seasonal hydroclimatic feature summaries and comparisons in terms of autocorrelation, seasonality, temporal variation, entropy, long-range dependence and trends. We find notable differences to characterize the magnitudes of most of these features across the various Koppen-Geiger climate classes, as well as between several continental-scale geographical regions. We, therefore, deem that the consideration of the comparative summaries could be more beneficial in water resources engineering contexts than the also provided global summaries. Lastly, we apply explainable machine learning to compare the investigated features with respect to how informative they are in explaining and predicting either the main Koppen-Geiger climate or the continental-scale region, with the entropy, long-range dependence and trend features being (roughly) found to be less informative than the remaining ones at the seasonal time scale.