For machine learning systems to be reliable, we must understand their performance in unseen, out-of-distribution environments. In this paper, we empirically show that out-of-distribution performance is strongly correlated with in-distribution performance for a wide range of models and distribution shifts. Specifically, we demonstrate strong correlations between in-distribution and out-of-distribution performance on variants of CIFAR-10 & ImageNet, a synthetic pose estimation task derived from YCB objects, satellite imagery classification in FMoW-WILDS, and wildlife classification in iWildCam-WILDS. The strong correlations hold across model architectures, hyperparameters, training set size, and training duration, and are more precise than what is expected from existing domain adaptation theory. To complete the picture, we also investigate cases where the correlation is weaker, for instance some synthetic distribution shifts from CIFAR-10-C and the tissue classification dataset Camelyon17-WILDS. Finally, we provide a candidate theory based on a Gaussian data model that shows how changes in the data covariance arising from distribution shift can affect the observed correlations.

MONTREAL and VANCOUVER, British Columbia, July 08, 2021 (GLOBE NEWSWIRE) -- Rithmik Solutions, whose mission is building the world's most advanced and reliable analytics for mobile mining equipment, today announced the closing of a US$1.2M investment led by Chrysalix Venture Capital and joined by Fonds Ecofuel. The funding will accelerate the commercialization of the company's flagship product, Rithmik Asset Health Analyzer (AHA), which has been in development for the past three years and is currently undergoing real-time onsite trials in Alberta, Quebec and Zambia. Rithmik AHA applies a multi-tiered machine learning approach to increase mobile equipment uptime while reducing maintenance costs and lowering greenhouse gas emissions. Mining companies typically spend anywhere from 20%-50% of their annual operating budgets on equipment maintenance, and lost production from unplanned downtime has an even bigger financial impact. "We were impressed by the Rithmik team's deep technical experience in the space of mobile mining equipment data, across equipment types and OEM brands, and that experience has strongly resonated with their early customers," said Alicia Lenis, Vice President at Chrysalix Venture Capital, an industrial innovation fund.

In the popular Terminator movies, a relentless super-robot played by Arnold Schwarzenegger tracks and attempts to kill human targets. It was pure science fiction in the 1980s. Today, killer robots hunting down targets have not only become reality, but are sold and deployed on the field of battle. The new Turkish-made Kargu-2 quadcopter drone can allegedly autonomously track and kill human targets on the basis of facial recognition and artificial intelligence--a big technological leap from the drone fleets requiring remote control by human operators. A United Nations Security Council report claims the Kargu-2 was used in Libya to mount autonomous attacks on human targets.

And I am no committed die-hard Marxist either. In this paper I am merely asking if our planning, evaluations & reviews of investments made in education, employment and human capital from tax payers' money over the years till now (especially since 1991) been judicious enough to warrant comfort in future outputs. Inviting my readers to do a self (mental) due diligence of achievements and the progress made in our country in the past few decades as I do, all I am asking is if, given the commitments radiating among our warring political parties under an archaic political system, the future of our grandchildren safe enough? Or, given they will not join the emerging lumpen elements, ought we to plan their migration to as bizarre countries as Taiwan, China, South Korea?] "Bureaucracy served Man well in the past. But the nature of Work has changed and management must change for us to survive. Our goal is to move from a bureaucratic model that is focused on maximizing compliance to one that is focused on maximizing contribution"– Management Guru Gary Hamel, speaking on Humanocracy at an Open Interactive pop up on 18th February 2021.

Natural language inference (NLI) is known as one of the central tasks in natural language processing (NLP) which encapsulates many fundamental aspects of language understanding. With the considerable achievements of data-hungry deep learning methods in NLP tasks, a great amount of effort has been devoted to develop more diverse datasets for different languages. In this paper, we present a new dataset for the NLI task in the Persian language, also known as Farsi, which is one of the dominant languages in the Middle East. This dataset, named FarsTail, includes 10,367 samples which are provided in both the Persian language as well as the indexed format to be useful for non-Persian researchers. The samples are generated from 3,539 multiple-choice questions with the least amount of annotator interventions in a way similar to the SciTail dataset. A carefully designed multi-step process is adopted to ensure the quality of the dataset. We also present the results of traditional and state-of-the-art methods on FarsTail including different embedding methods such as word2vec, fastText, ELMo, BERT, and LASER, as well as different modeling approaches such as DecompAtt, ESIM, HBMP, and ULMFiT to provide a solid baseline for the future research. The best obtained test accuracy is 83.38% which shows that there is a big room for improving the current methods to be useful for real-world NLP applications in different languages. We also investigate the extent to which the models exploit superficial clues, also known as dataset biases, in FarsTail, and partition the test set into easy and hard subsets according to the success of biased models. The dataset is available at https://github.com/dml-qom/FarsTail

Malaria is a parasitic infection that poses a significant burden on global health. It kills one child every 30 seconds and over one million people annually. If diagnosed in a timely manner, however, most people can be effectively treated with antimalarial therapy. Several deaths due to malaria are byproducts of disparities in the social determinants of health; the current gold standard for diagnosing malaria requires microscopes, reagents, and other equipment that most patients of low socioeconomic brackets do not have access to. In this paper, we propose a convolutional neural network (CNN) architecture that allows for rapid automated diagnosis of malaria (achieving a high classification accuracy of 98%), as well as a deep neural network (DNN) based three-dimensional (3D) modeling algorithm that renders 3D models of parasitic cells in augmented reality (AR). This creates an opportunity to optimize the current workflow for malaria diagnosis and demonstrates potential for deep learning models to improve telemedicine practices and patient health literacy on a global scale. Our website is accessible here.

Creating a domain model, even for classical, domain-independent planning, is a notoriously hard knowledge-engineering task. A natural approach to solve this problem is to learn a domain model from observations. However, model learning approaches frequently do not provide safety guarantees: the learned model may assume actions are applicable when they are not, and may incorrectly capture actions' effects. This may result in generating plans that will fail when executed. In some domains such failures are not acceptable, due to the cost of failure or inability to replan online after failure. In such settings, all learning must be done offline, based on some observations collected, e.g., by some other agents or a human. Through this learning, the task is to generate a plan that is guaranteed to be successful. This is called the model-free planning problem. Prior work proposed an algorithm for solving the model-free planning problem in classical planning. However, they were limited to learning grounded domains, and thus they could not scale. We generalize this prior work and propose the first safe model-free planning algorithm for lifted domains. We prove the correctness of our approach, and provide a statistical analysis showing that the number of trajectories needed to solve future problems with high probability is linear in the potential size of the domain model. We also present experiments on twelve IPC domains showing that our approach is able to learn the real action model in all cases with at most two trajectories.

A state-of-the-art systematic review on XAI applied to Prognostic and Health Management (PHM) of industrial asset is presented. The work attempts to provide an overview of the general trend of XAI in PHM, answers the question of accuracy versus explainability, investigates the extent of human role, explainability evaluation and uncertainty management in PHM XAI. Research articles linked to PHM XAI, in English language, from 2015 to 2021 are selected from IEEE Xplore, ScienceDirect, SpringerLink, ACM Digital Library and Scopus databases using PRISMA guidelines. Data was extracted from 35 selected articles and examined using MS. Excel. Several findings were synthesized. Firstly, while the discipline is still young, the analysis indicates the growing acceptance of XAI in PHM domain. Secondly, XAI functions as a double edge sword, where it is assimilated as a tool to execute PHM tasks as well as a mean of explanation, in particular in diagnostic and anomaly detection. There is thus a need for XAI in PHM. Thirdly, the review shows that PHM XAI papers produce either good or excellent results in general, suggesting that PHM performance is unaffected by XAI. Fourthly, human role, explainability metrics and uncertainty management are areas requiring further attention by the PHM community. Adequate explainability metrics to cater for PHM need are urgently needed. Finally, most case study featured on the accepted articles are based on real, indicating that available AI and XAI approaches are equipped to solve complex real-world challenges, increasing the confidence of AI model adoption in the industry. This work is funded by the Universiti Teknologi Petronas Foundation.

Artificial Intelligence (AI) systems are increasingly deployed in the public sector. Existing public procurement processes and standards are in urgent need of innovation to address potential risks and harms to citizens. Read our primer based on our research and on input from leading experts in the public sector, data science, civil society, policy, social science, and the law to learn about pathways forward. The COVID-19 pandemic has underlined how biases can manifest in many different aspects of public use technology. For example, federal COVID-19 funding allocation algorithms have favored high-income communities over low-income communities due to historical biases prevalent in the training data. AI solutions that can be implemented fast are typically provided by private companies. As more and more aspects of public service are infused with AI systems and other technologies provided by private companies, we see a growing network of privately owned infrastructure. As government entities outsource critical technological infrastructure (such as data storage and cloud-based systems for data sharing and analysis) to private companies under the guise of modernizing public services, we see a trend towards losing control over critical infrastructure and decreasing accountability to the public that relies on it.

Our team of data scientists, ex-bankers, engineers and mathematicians have rich industry experience from IBM Research, Banking, Technology Companies and globally renowned consulting companies and financial institutions across Africa. The team has successfully built several credit risk scoring engines from financial data, mobile money data, transactions data and several other alternative data sets for over five industries. Timothy Kotin is the Co-Founder and CEO of Superfluid Labs a pioneering data analytics firm serving emerging market financial institutions, fintech firms and startups with offices in Kenya, Ghana and Germany. Through both Superfluid and his prior professional experience, Timothy has extensive experience, including holding proprietary patents and inventions related to developing digital financial products (credit, savings, asset finance, etc.) and credit scoring models that leverage both traditional financial data as well as new alternative data sources such as call data records or mobile money transactions from MNOs and social media. He is a recognized thought-leader at the intersection of Artificial Intelligence and international development and served on the USAID Advisory Panel on Artificial Intelligence (AI) and Machine Learning (ML) in 2019.