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.

Tues, April 19, 2022 11-12pm CST Register: https://bit.ly/african-indaba-nlp Ife Adebara, PhD Candidate, African Languages, Computational Linguistics, & Deep Learning UBC, Vancouver, ATLI David Ifeoluwa Adelani, PhD Candidate, Africa NLP Universität des Saarlandes; Germany, Masakhane NLP The African DH Indabas are an avenue for occasional African DH discussions in which mostly Africa-based digital humanities scholars are in conversation with each other, or with colleagues from other parts of the world. The idea of the indaba suggests our commitment at AAAS, African Digital Humanities @KU, and IDRH-KU to creating a global community of leaders and experts in African digital humanities. Send questions to James Yeku at jyeku@ku.edu, powered by Localist, the Community Event Platform

Artificial intelligence (AI) plays a crucial role in the healthcare industry by helping doctors, patients and hospital administrators. Artificial Intelligence (AI) is defined as computing systems which are capable of performing tasks that humans are very good at, for example recognising objects, recognising and making sense of speech, and decision making in a constrained environment. For the purposes of this article, Machine Learning and Deep Learning (Deep Neural Networks) are defined as sub-branches of AI. See the Appendix for a more detailed explanation of these areas. Healthcare systems were already under a substantial strain before the arrival of the Covid-19 pandemic. This strain has only increased since the pandemic and may cause challenges that persist for many years. It takes many years and costly resources to train healthcare workers. Specialist medical practitioners tend to be in short supply and often work long hours.

DARPA, In-Q-Tel, US National Laboratories (examples: Argonne, Oak Ridge) are famous government funding agencies for deep tech on the forward boundaries, the near impossible, that have globally transformative solutions. The Internet is a prime example where more than 70% of the 7.8 billion population are online in 2022, closing in on 7 hours daily mobile usage, and global wealth of $500 Trillion is powered by the Internet. There is convergence between the early bets led by government funding agencies and the largest corporations and their investments. An example is from 2015, where I was invited to help the top 100 CEOs, representing nearly $100 Trillion in assets under management, to look ten years into the future for their investments. The resulting working groups, and private summits resulted in the member companies investing in all the areas identified: quantum computing, block chain, cybersecurity, big data, privacy and data, AI/ML, future in fintech, financial inclusion, ...

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Fixing bugs in a timely manner lowers various potential costs in software maintenance. However, manual bug fixing scheduling can be time-consuming, cumbersome, and error-prone. In this paper, we propose the Schedule and Dependency-aware Bug Triage (S-DABT), a bug triaging method that utilizes integer programming and machine learning techniques to assign bugs to suitable developers. Unlike prior works that largely focus on a single component of the bug reports, our approach takes into account the textual data, bug fixing costs, and bug dependencies. We further incorporate the schedule of developers in our formulation to have a more comprehensive model for this multifaceted problem. As a result, this complete formulation considers developers' schedules and the blocking effects of the bugs while covering the most significant aspects of the previously proposed methods. Our numerical study on four open-source software systems, namely, EclipseJDT, LibreOffice, GCC, and Mozilla, shows that taking into account the schedules of the developers decreases the average bug fixing times. We find that S-DABT leads to a high level of developer utilization through a fair distribution of the tasks among the developers and efficient use of the free spots in their schedules. Via the simulation of the issue tracking system, we also show how incorporating the schedule in the model formulation reduces the bug fixing time, improves the assignment accuracy, and utilizes the capability of each developer without much comprising in the model run times. We find that S-DABT decreases the complexity of the bug dependency graph by prioritizing blocking bugs and effectively reduces the infeasible assignment ratio due to bug dependencies. Consequently, we recommend considering developers' schedules while automating bug triage.

We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. Trust in technology is eroding. This is especially true when it comes to emerging technologies such as AI, machine learning, augmented and virtual reality and the Internet of Things. These technologies are powerful and have the potential for great good. But they are not well understood by end-users of tech and, in some cases, not even by creators of tech.