Robotic grasping and manipulation has historically been dominated by rigid grippers, force/form closure constraints, and extensive grasp trajectory planning. The advent of soft robotics offers new avenues to diverge from this paradigm by using strategic compliance to passively conform to grasped objects in the absence of active control, and with minimal chance of damage to the object or surrounding environment. However, while the reduced emphasis on sensing, planning, and control complexity simplifies grasping and manipulation tasks, precision and dexterity are often lost. This talk will discuss efforts to increase the robustness of soft grasping and the dexterity of soft robotic manipulators, with particular emphasis on grasping tasks that are challenging for more traditional robot hands. This includes compliant objects, thin flexible sheets, and delicate organisms.

AI ethics has long been a hot-button issue. For some, it's reduced to a debate about whether AI should be making decisions traditionally reserved for humans. There are misconceptions that AI systems will quickly evolve into superhuman intelligence, or that they'll be a silver bullet to solve problems for which we as a society don't yet have answers. As a community, we need to do better. As AI proliferates, it's time to figure out how to operationalize AI ethics.

The world runs on data, and humans alone could never monitor or safeguard all of it. When applied thoughtfully, artificial intelligence (AI)-enhanced cybersecurity can add essential layers of protection for modern enterprise networks. Research firm Technavio expects the AI-based cybersecurity market to grow by $19 billion from 2021 to 2025. The company cites the increased complexity of enterprise networking environments, which often include a mix of legacy, on-premises infrastructure, and cloud resources, all of which need to be accessed remotely. AI approaches add efficiency and accuracy and reduce the impact of the ongoing worker shortage in this field.

Fast developing artificial intelligence (AI) technology has enabled various applied systems deployed in the real world, impacting people's everyday lives. However, many current AI systems were found vulnerable to imperceptible attacks, biased against underrepresented groups, lacking in user privacy protection, etc., which not only degrades user experience but erodes the society's trust in all AI systems. In this review, we strive to provide AI practitioners a comprehensive guide towards building trustworthy AI systems. We first introduce the theoretical framework of important aspects of AI trustworthiness, including robustness, generalization, explainability, transparency, reproducibility, fairness, privacy preservation, alignment with human values, and accountability. We then survey leading approaches in these aspects in the industry. To unify the current fragmented approaches towards trustworthy AI, we propose a systematic approach that considers the entire lifecycle of AI systems, ranging from data acquisition to model development, to development and deployment, finally to continuous monitoring and governance. In this framework, we offer concrete action items to practitioners and societal stakeholders (e.g., researchers and regulators) to improve AI trustworthiness. Finally, we identify key opportunities and challenges in the future development of trustworthy AI systems, where we identify the need for paradigm shift towards comprehensive trustworthy AI systems.

We propose nonparametric Bayesian estimators for causal inference exploiting Regression Discontinuity/Kink (RD/RK) under sharp and fuzzy designs. Our estimators are based on Gaussian Process (GP) regression and classification. The GP methods are powerful probabilistic modeling approaches that are advantageous in terms of derivative estimation and uncertainty qualification, facilitating RK estimation and inference of RD/RK models. These estimators are extended to hierarchical GP models with an intermediate Bayesian neural network layer and can be characterized as hybrid deep learning models. Monte Carlo simulations show that our estimators perform similarly and often better than competing estimators in terms of precision, coverage and interval length. The hierarchical GP models improve upon one-layer GP models substantially. An empirical application of the proposed estimators is provided.

Think of all the things we depend on daily life that were made possible by the U.S. military: the internet, jet travel, GPS and, perhaps most important, duct tape. That such things grew out of a desire to find new and better ways to kill lots and lots of people is easy to put out of mind, just as we tend to forget where that juicy ribeye on our plate originally came from. If consumers want truly smart machines (smarter than Amazon's new Astro, anyway) and the "internet of things," Google and Apple and Elon Musk will all play their roles, but much of the research and development will come from the Pentagon -- and, no doubt, the People's Liberation Army. Congress, holder of the strings to the Defense Department's $740 billion purse, is going to have to make it happen.

NYSEG, an electric and gas utility that serves areas of the Capital Region not served by National Grid, is developing a new computer-based outage prediction system that will use artificial intelligence. New York State Electric & Gas says it is developing what it is calling an "outage prediction model," essentially a software program that will use machine learning or artificial intelligence -- AI -- to predict outages during storm events. NYSEG and its parent company, Avangrid, along with its sister utility, RG&E, short for Rochester Gas & Electric, are working with researchers at the University at Albany and the University of Connecticut on developing the AI system. The system will use AI to analyze weather forecasts to predict -- or guess -- which parts of the electrical grid will be hit hardest by storms. That way the utility can prepare to deploy resources to those areas in advance.

Artificial intelligence (AI) has undoubtedly been a growing presence in the healthcare industry, shaving years and billions of pounds off drug development programmes, accurately predicting A&E influxes, and even detecting early signs of disease in patients years before it was thought possible. The field of neuroscience has been no exception to this wave of technological innovation, with exciting developments cropping up in recent months and years that could potentially revolutionise diagnoses, treatments, and outcomes for patients on a global scale. The term AI covers a field of computer science that is focused upon the simulation of human intelligence and computational processes. However, there are several subfields of AI technology currently being explored in neuroscience, including machine learning (ML) and deep learning (DL). AI covers all programming systems that can perform tasks which usually require human intelligence.

Artificial intelligence and machine learning (AI/ML) have made inroads into enterprises for a variety of different uses, including decision support, product recommendations and process control. These fields are employing big-data concepts to train software algorithms to evaluate data and respond in a similar manner to human decision-makers. These systems are boosted by data collected in the problem domain and used to successively adjust the algorithms to model that domain. For example, a retailer might use detailed data on sales experiences to recommend additional products for shoppers to purchase. By correlating purchases made by past customers, the retailer may be able to entice shoppers to make larger purchases than they had originally intended.