Can artificial intelligence (AI) be used in judicial processes to reduce the pendency of cases? In response to this unstarred question in the Lok Sabha during the first part of the Budget session of Parliament, Law Minister Kiren Rijiju said that while implementing phase two of the eCourts projects, under operation since 2015, a need was felt to adopt new, cutting edge technologies of Machine Learning (ML) and Artificial Intelligence (AI) to increase the efficiency of the justice delivery system. "To explore the use of AI in judicial domain, the Supreme Court of India has constituted Artificial Intelligence Committee which has mainly identified application of AI technology in Translation of judicial documents; Legal research assistance and Process automation," Mr. Rijiju stated. Several law firms are now keen try out new technologies for a quick reference on judicial precedents and pronouncements on cases with similar legal issues at stake. Mumbai-based Riverus, a "legal tech" firm, has developed ML applications that peruse troves of cases, "understand" them, and parse cases that are similar in content -- very much like a human expert would do -- in a fraction of the time.

With each passing year artificial intelligence (AI) continues to be a potent driver of transformation for industries and businesses around the world. Simultaneously, these industries supported by AI are also evolving and changing. New AI advances create new opportunities for industries to evolve and expand at an even greater rate. From hyperautomation to voice and language-driven intelligence, this article will give an overview of what we expect to be the driving main trends in 2022 and which industries will be most affected by them. Hyperautomation is the process of automating every step that is able to be automated in a given process of "events."

Artificial intelligence will continue on its path to becoming the most transformative technology humanity has ever created in 2022. According to Google CEO Sundar Pichai, it will have an even more significant impact on our evolution as a species than fire or electricity. This may appear to be a lofty claim, but considering how it is already being used to combat climate change, explore space, and develop cancer treatments, the potential is undeniable. While many top AI technologies are still relatively nebulous, a few trends will continue to flourish. As AI continues to advance, it will become more integrated into all aspects of a business.

This thesis rigorously studies fundamental reinforcement learning (RL) methods in modern practical considerations, including robust RL, distributional RL, and offline RL with neural function approximation. The thesis first prepares the readers with an overall overview of RL and key technical background in statistics and optimization. In each of the settings, the thesis motivates the problems to be studied, reviews the current literature, provides computationally efficient algorithms with provable efficiency guarantees, and concludes with future research directions. The thesis makes fundamental contributions to the three settings above, both algorithmically, theoretically, and empirically, while staying relevant to practical considerations.

High-dimensional imaging is becoming increasingly relevant in many fields from astronomy and cultural heritage to systems biology. Visual exploration of such high-dimensional data is commonly facilitated by dimensionality reduction. Consequently, exploration of such data is Figure 1: Texture-aware dimensionality reduction. An image typically split into a step focusing on the attribute space followed by (a) with black and white pixels forms multiple textures. In this paper, distance-based dimensionality reduction produces one cluster of we present a method for incorporating spatial neighborhood information black and one cluster of white pixels (b), a texture-aware version into distance-based dimensionality reduction methods, such as should create clusters for the different textures (c). We achieve this by modifying the distance measure between high-dimensional attribute vectors associated with each pixel such that it takes the pixel's spatial neighborhood into account. Based on a classification The spatial configuration is, however, commonly of interest when of different methods for comparing image patches, we explore a analyzing high-dimensional image data. We compare these approaches from neighborhood information into account, in addition to highdimensional a theoretical and experimental point of view. Typical approaches to combine high-dimensional evaluation on synthetic data and two real-world use cases. They use the embedding as a colormap and perform segmentation on the re-colored image. High-dimensional data is commonly acquired and analyzed in various Decoupling the high-dimensional and spatial analysis in such a application domains, from systems biology [26] to insurance way has several downsides: Most importantly, boundaries between fraud detection [37]. Typically, high-dimensional data are tabular clusters in an embedding are often not well defined, and as such data with many columns (or attributes), corresponding to the dimensionality classification is ambiguous and has a level of arbitrariness.

This paper reviews the recent literature on solving the Boolean satisfiability problem (SAT), an archetypal NP-complete problem, with the help of machine learning techniques. Despite the great success of modern SAT solvers to solve large industrial instances, the design of handcrafted heuristics is time-consuming and empirical. Under the circumstances, the flexible and expressive machine learning methods provide a proper alternative to solve this long-standing problem. We examine the evolving ML-SAT solvers from naive classifiers with handcrafted features to the emerging end-to-end SAT solvers such as NeuroSAT, as well as recent progress on combinations of existing CDCL and local search solvers with machine learning methods. Overall, solving SAT with machine learning is a promising yet challenging research topic. We conclude the limitations of current works and suggest possible future directions.

Preserving the performance of a trained model while removing unique characteristics of marked training data points is challenging. Recent research usually suggests retraining a model from scratch with remaining training data or refining the model by reverting the model optimization on the marked data points. Unfortunately, aside from their computational inefficiency, those approaches inevitably hurt the resulting model's generalization ability since they remove not only unique characteristics but also discard shared (and possibly contributive) information. To address the performance degradation problem, this paper presents a novel approach called Performance Unchanged Model Augmentation~(PUMA). The proposed PUMA framework explicitly models the influence of each training data point on the model's generalization ability with respect to various performance criteria. It then complements the negative impact of removing marked data by reweighting the remaining data optimally. To demonstrate the effectiveness of the PUMA framework, we compared it with multiple state-of-the-art data removal techniques in the experiments, where we show the PUMA can effectively and efficiently remove the unique characteristics of marked training data without retraining the model that can 1) fool a membership attack, and 2) resist performance degradation. In addition, as PUMA estimates the data importance during its operation, we show it could serve to debug mislabelled data points more efficiently than existing approaches.

America is getting older faster. According to the U.S. Census Bureau, the number of people aged 65 or older in the United States will grow to 95 million by the year 2060 and will account for nearly one-quarter of the population. Artificial intelligence (AI) and technology solutions have a significant potential to transform quality of life and improve health care outcomes for older Americans, including those with Alzheimer's Disease and Related Dementias (AD/ADRD). To meet this challenge, the AI/Tech Aging (a2) Collective is announcing the a2 Pilot Awards, a national competition that will earmark $40 million over the next 5 years for promising pilot projects that leverage AI and other transformative technology to support healthy aging and persons living with AD/ADRD. The a2 Collective represents the National Institute on Aging's (NIA) Artificial Intelligence and Technology Collaboratories for Aging Research (AITC) program, which is dedicated to helping Americans live longer, better through the application of AI and emerging technologies.

We introduce in this survey the major concepts, models, and algorithms proposed so far to infer causal relations from observational time series, a task usually referred to as causal discovery in time series. To do so, after a description of the underlying concepts and modelling assumptions, we present different methods according to the family of approaches they belong to: Granger causality, constraint-based approaches, noise-based approaches, score-based approaches, logic-based approaches, topology-based approaches, and difference-based approaches. We then evaluate several representative methods to illustrate the behaviour of different families of approaches. This illustration is conducted on both artificial and real datasets, with different characteristics. The main conclusions one can draw from this survey is that causal discovery in times series is an active research field in which new methods (in every family of approaches) are regularly proposed, and that no family or method stands out in all situations. Indeed, they all rely on assumptions that may or may not be appropriate for a particular dataset.

With each passing year artificial intelligence (AI) continues to be a potent driver of transformation for industries and businesses around the world. Simultaneously, these industries supported by AI are also evolving and changing. New AI advances create new opportunities for industries to evolve and expand at an even greater rate. From hyperautomation to voice and language-driven intelligence, this article will give an overview of what we expect to be the driving main trends in 2022 and which industries will be most affected by them. Hyperautomation is the process of automating every step that is able to be automated in a given process of "events."