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."

Dr. Dorel Iosif is a Board Director and CEO with Cognisium, a tech executive marketplace headquartered in Australia. He held senior executive roles with KBR, WorleyParsons, PwC and Advisian Management Consulting. Dr Iosif started his career in Israel with the Technion Institute of Technology and continued in Australia with BHPBilliton and the University of Melbourne. He holds a Ph.D in applied mathematics from the University of Melbourne and studied Corporate Level Strategy - Executive Program at Harvard Business School. Dorel worked in Australia, USA, Europe and the Middle East.

Data science covers the full spectrum of deriving insight from data, from initial data gathering and interpretation, via processing and engineering of data, and exploration and modeling, to eventually producing novel insights and decision support systems. Data science can be viewed as overlapping or broader in scope than other data-analytic methodological disciplines, such as statistics, machine learning, databases, or visualization.10 To illustrate the breadth of data science, consider, for example, the problem of recommending items (movies, books, or other products) to customers. While the core of these applications can consist of algorithmic techniques such as matrix factorization, a deployed system will involve a much wider range of technological and human considerations. These range from scalable back-end transaction systems that retrieve customer and product data in real time, experimental design for evaluating system changes, causal analysis for understanding the effect of interventions, to the human factors and psychology that underlie how customers react to visual information displays and make decisions. As another example, in areas such as astronomy, particle physics, and climate science, there is a rich tradition of building computational pipelines to support data-driven discovery and hypothesis testing. For instance, geoscientists use monthly global landcover maps based on satellite imagery at sub-kilometer resolutions to better understand how the Earth's surface is changing over time.50 These maps are interactive and browsable, and they are the result of a complex data-processing pipeline, in which terabytes to petabytes of raw sensor and image data are transformed into databases of a6utomatically detected and annotated objects and information. This type of pipeline involves many steps, in which human decisions and insight are critical, such as instrument calibration, removal of outliers, and classification of pixels. The breadth and complexity of these and many other data science scenarios means the modern data scientist requires broad knowledge and experience across a multitude of topics. Together with an increasing demand for data analysis skills, this has led to a shortage of trained data scientists with appropriate background and experience, and significant market competition for limited expertise. Considering this bottleneck, it is not surprising there is increasing interest in automating parts, if not all, of the data science process.