The National Science Foundation and the White House's Office of Science and Technology Policy are developing a plan to make high-performance computing, machine-learning datasets and other resources more widely available to artificial intelligence researchers at every level. Developing artificial intelligence tools and technologies requires lots of data and even more computing resources. Gaining a national advantage in this area will require a significant concentration of work that is currently limited to agencies and organizations that have those resources. But the best, groundbreaking ideas aren't always centered in places with the most resources. To address this issue, the 2021 National Defense Authorization Act charged NSF and OSTP with developing a plan to build up those resources and make sure they are available to people throughout the U.S. who can make good use of them.

Artificial intelligence has insinuated its way into our daily lives, whether we are aware. From opening our smartphones with facial recognition, to travel and commuting assistance, to personalizing our feeds on social media, AI is with us from dawn to dusk and beyond. But, there is a concerning new use for AI on the horizon. AI and machine learning could be tapped by the lending industry in making loan decisions. It's a prospect that has sparked warnings from consumer advocates, pundits and even banks.

The National Science Foundation has announced the creation of an artificial intelligence research center at Duke. The AI Institute for Edge Computing Leveraging Next-generation Networks--Athena for short--will be supported through a $20 million grant and five-year development plan from NSF and the Department of Homeland Security. The initiative is positioned to reshape the design, service, and operation of mobile systems. Yiran Chen, professor in the department of electrical and computer engineering, will serve as the director of Athena and principal investigator for the grant. "Athena is a big milestone of Duke AI initiatives through transforming the research landscape in AI-driven computing technology," Chen said.

AI is undergoing a paradigm shift with the rise of models (e.g., BERT, DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a wide range of downstream tasks. We call these models foundation models to underscore their critically central yet incomplete character. This report provides a thorough account of the opportunities and risks of foundation models, ranging from their capabilities (e.g., language, vision, robotics, reasoning, human interaction) and technical principles(e.g., model architectures, training procedures, data, systems, security, evaluation, theory) to their applications (e.g., law, healthcare, education) and societal impact (e.g., inequity, misuse, economic and environmental impact, legal and ethical considerations). Though foundation models are based on standard deep learning and transfer learning, their scale results in new emergent capabilities,and their effectiveness across so many tasks incentivizes homogenization. Homogenization provides powerful leverage but demands caution, as the defects of the foundation model are inherited by all the adapted models downstream. Despite the impending widespread deployment of foundation models, we currently lack a clear understanding of how they work, when they fail, and what they are even capable of due to their emergent properties. To tackle these questions, we believe much of the critical research on foundation models will require deep interdisciplinary collaboration commensurate with their fundamentally sociotechnical nature.

Recent deep-learning models have achieved impressive predictive performance by learning complex functions of many variables, often at the cost of interpretability. This chapter covers recent work aiming to interpret models by attributing importance to features and feature groups for a single prediction. Importantly, the proposed attributions assign importance to interactions between features, in addition to features in isolation. These attributions are shown to yield insights across real-world domains, including bio-imaging, cosmology image and natural-language processing. We then show how these attributions can be used to directly improve the generalization of a neural network or to distill it into a simple model. Throughout the chapter, we emphasize the use of reality checks to scrutinize the proposed interpretation techniques.

Tactical Decision Games (TDGs) are military conflict scenarios presented both textually and graphically on a map. These scenarios provide a challenging domain for machine learning because they are open-ended, highly structured, and typically contain many details of varying relevance. We have developed a problem-solving component of an interactive companion system that proposes military tasks to solve TDG scenarios using a combination of analogical retrieval, mapping, and constraint propagation. We use this problem-solving component to explore analogical learning. In this paper, we describe the problems encountered in learning for this domain, and the methods we have developed to address these, such as partition constraints on analogical mapping correspondences and the use of incremental remapping to improve robustness. We present the results of learning experiments that show improvement in performance through the simple accumulation of examples, despite a weak domain theory.

Artificial intelligence (AI) systems operate in increasingly diverse areas, from healthcare to facial recognition, the stock market, autonomous vehicles, and so on. While the underlying digital infrastructure of AI systems is developing rapidly, each area of implementation is subject to different degrees and processes of legitimization. By combining elements from institutional theory and information systems-theory, this paper presents a conceptual framework to analyze and understand AI-induced field-change. The introduction of novel AI-agents into new or existing fields creates a dynamic in which algorithms (re)shape organizations and institutions while existing institutional infrastructures determine the scope and speed at which organizational change is allowed to occur. Where institutional infrastructure and governance arrangements, such as standards, rules, and regulations, still are unelaborate, the field can move fast but is also more likely to be contested. The institutional infrastructure surrounding AI-induced fields is generally little elaborated, which could be an obstacle to the broader institutionalization of AI-systems going forward.

In the past few decades, artificial intelligence (AI) technology has experienced swift developments, changing everyone's daily life and profoundly altering the course of human society. The intention of developing AI is to benefit humans, by reducing human labor, bringing everyday convenience to human lives, and promoting social good. However, recent research and AI applications show that AI can cause unintentional harm to humans, such as making unreliable decisions in safety-critical scenarios or undermining fairness by inadvertently discriminating against one group. Thus, trustworthy AI has attracted immense attention recently, which requires careful consideration to avoid the adverse effects that AI may bring to humans, so that humans can fully trust and live in harmony with AI technologies. Recent years have witnessed a tremendous amount of research on trustworthy AI. In this survey, we present a comprehensive survey of trustworthy AI from a computational perspective, to help readers understand the latest technologies for achieving trustworthy AI. Trustworthy AI is a large and complex area, involving various dimensions. In this work, we focus on six of the most crucial dimensions in achieving trustworthy AI: (i) Safety & Robustness, (ii) Non-discrimination & Fairness, (iii) Explainability, (iv) Privacy, (v) Accountability & Auditability, and (vi) Environmental Well-Being. For each dimension, we review the recent related technologies according to a taxonomy and summarize their applications in real-world systems. We also discuss the accordant and conflicting interactions among different dimensions and discuss potential aspects for trustworthy AI to investigate in the future.

The U.S. Intelligence Community faces a moment of reckoning and AI lies at the heart of it. Since 9/11, America's intelligence agencies have become hardwired to fight terrorism. Today's threat landscape, however, is changing dramatically, with a resurgence of great power competition and the rise of cyber threats enabling states and non-state actors to spy, steal, disrupt, destroy, and deceive across vast distances -- all without firing a shot. The Intelligence Community (IC) faces a moment of reckoning. If the IC cannot adopt AI and other emerging technologies successfully, it risks failure.

This failure was aggravated by pressures in Washington to rack up successes very quickly. Officials poured billions of dollars into Afghanistan, mistakenly believing that more money would yield faster results, when in fact the cash just swelled the corruption. When officials realized this fact, they tried filtering the money through unofficial channels--and, as a result, the few honest officials in the Afghan government never learned how to manage those own agencies. U.S. personnel policy also deepened the morass. Washington's "inability to get the right people into the right jobs at the right time" marked "one of the most significant failures of the mission."