The AI conversation can be traced as far back as Alan Turing's milestone paper published in 1950, which considered the fundamental question "Can machines think?" [1]. In 1956, AI got its name and mission as a scientific field at the first AI conference held at Dartmouth College [2]. Following AI's foundational period in the 1950s ~ 1970s, AI has evolved from early rule-based systems (1970s ~ 1990s), through classical machine learning and deep learning with neural networks (1990s ~ 2020s), to today's generative and agentic AI systems (since 2010s). Correspondingly, as a vital requirement of these systems, the reliability concept and concerns are also evolving, particularly in the interpretation of "required function" (see Table 1 in Chapter 10), based on the definition in standards like ISO 8402 "The ability of an item to perform a required function, under given environmental and operational conditions and for a stated period of time ". While a conventional AI system is concerned with providing stable and accurate classifications, predictions, or optimizations, a reliable generative AI system focuses on producing outputs that are trustworthy, consistent, safe, and contextually appropriate [3]. Building on both, a reliable agentic AI system should additionally conduct functions of reasoning, goal alignment, planning, safe adaption and interaction in dynamic and collaborative multi-agent contexts. The expansion of reliability concepts has introduced new challenges and research opportunities, as exemplified in Figure 1. In the following sections, we shed lights on these challenges and opportunities in building reliable AI systems, particularly, agentic AI systems.

Membership in ACM includes a subscription to Communications of the ACM (CACM), the computing industry's most trusted source for staying connected to the world of advanced computing. Addressing the Arab world's unique challenges against misinformation and disinformation requires efforts at technical, institutional, and social levels. Misinformation and disinformation are global risks. However, the Arab region is particularly vulnerable due to its geopolitical instabilities, linguistic diversity, and other cultural nuances. Misinformation includes false or misleading content, such as rumors, satire taken as fact, or conspiracy theories, while disinformation is the intentional and targeted spread of such content to deceive or manipulate specific audiences. To limit the spread and influence of misinformation, it is essential to advance research on technological methods for early detection, tracking, and mitigation, while also strengthening media literacy and promoting active citizen participation.

Non-manual signals in sign languages continue to be a challenge for signing avatars. More specifically, emotional content has been difficult to incorporate because of a lack of a standard method of specifying the avatar's emotional state. This paper explores the application of an intuitive two-parameter representation for emotive non-manual signals to the Paula signing avatar that shows promise for facilitating the linguistic specification of emotional facial expressions in a more coherent manner than previous methods. Users can apply these parameters to control Paula's emotional expressions through a textual representation called the EASIER notation. The representation can allow avatars to express more nuanced emotional states using two numerical parameters. It also has the potential to enable more consistent specification of emotional non-manual signals in linguistic annotations which drive signing avatars.

The increasing size and complexity of machine learning (ML) models have driven the growing need for custom hardware accelerators capable of efficiently supporting ML workloads. However, the design of such accelerators remains a time-consuming process, heavily relying on engineers to manually ensure design interpretability through clear documentation and effective communication. Recent advances in large language models (LLMs) offer a promising opportunity to automate these design interpretability tasks, particularly the generation of natural language descriptions for register-transfer level (RTL) code, what we refer to as "RTL-to-NL tasks." In this paper, we examine how design interpretability, particularly in RTL-to-NL tasks, influences the efficiency of the hardware design process. We review existing work adapting LLMs for these tasks, highlight key challenges that remain unaddressed, including those related to data, computation, and model development, and identify opportunities to address them. By doing so, we aim to guide future research in leveraging ML to automate RTL-to-NL tasks and improve hardware design interpretability, thereby accelerating the hardware design process and meeting the increasing demand for custom hardware accelerators in machine learning and beyond.

Legacy software systems, written in outdated languages like MUMPS and mainframe assembly, pose challenges in efficiency, maintenance, staffing, and security. While LLMs offer promise for modernizing these systems, their ability to understand legacy languages is largely unknown. This paper investigates the utilization of LLMs to generate documentation for legacy code using two datasets: an electronic health records (EHR) system in MUMPS and open-source applications in IBM mainframe Assembly Language Code (ALC). We propose a prompting strategy for generating line-wise code comments and a rubric to evaluate their completeness, readability, usefulness, and hallucination. Our study assesses the correlation between human evaluations and automated metrics, such as code complexity and reference-based metrics. We find that LLM-generated comments for MUMPS and ALC are generally hallucination-free, complete, readable, and useful compared to ground-truth comments, though ALC poses challenges. However, no automated metrics strongly correlate with comment quality to predict or measure LLM performance. Our findings highlight the limitations of current automated measures and the need for better evaluation metrics for LLM-generated documentation in legacy systems.

This paper explores how automation and artificial intelligence (AI) are transforming U.S. cyber diplomacy. Leveraging these technologies helps the U.S. manage the complexity and urgency of cyber diplomacy, improving decision-making, efficiency, and security. As global inter connectivity grows, cyber diplomacy, managing national interests in the digital space has become vital. The ability of AI and automation to quickly process vast data volumes enables timely responses to cyber threats and opportunities. This paper underscores the strategic integration of these tools to maintain U.S. competitive advantage and secure national interests. Automation enhances diplomatic communication and data processing, freeing diplomats to focus on strategic decisions. AI supports predictive analytics and real time decision making, offering critical insights and proactive measures during high stakes engagements. Case studies show AIs effectiveness in monitoring cyber activities and managing international cyber policy. Challenges such as ethical concerns, security vulnerabilities, and reliance on technology are also addressed, emphasizing human oversight and strong governance frameworks. Ensuring proper ethical guidelines and cybersecurity measures allows the U.S. to harness the benefits of automation and AI while mitigating risks. By adopting these technologies, U.S. cyber diplomacy can become more proactive and effective, navigating the evolving digital landscape with greater agility.

In this article, we seek to elucidate challenges and opportunities for differential privacy within the federal government setting, as seen by a team of differential privacy researchers, privacy lawyers, and data scientists working closely with the U.S. government. After introducing differential privacy, we highlight three significant challenges which currently restrict the use of differential privacy in the U.S. government. We then provide two examples where differential privacy can enhance the capabilities of government agencies. The first example highlights how the quantitative nature of differential privacy allows policy security officers to release multiple versions of analyses with different levels of privacy. The second example, which we believe is a novel realization, indicates that differential privacy can be used to improve staffing efficiency in classified applications. We hope that this article can serve as a nontechnical resource which can help frame future action from the differential privacy community, privacy regulators, security officers, and lawmakers.

Large Language Models (LLMs) have made significant advances in natural language processing, but their underlying mechanisms are often misunderstood. Despite exhibiting coherent answers and apparent reasoning behaviors, LLMs rely on statistical patterns in word embeddings rather than true cognitive processes. This leads to vulnerabilities such as "hallucination" and misinformation. The paper argues that current LLM architectures are inherently untrustworthy due to their reliance on correlations of sequential patterns of word embedding vectors. However, ongoing research into combining generative transformer-based models with fact bases and logic programming languages may lead to the development of trustworthy LLMs capable of generating statements based on given truth and explaining their self-reasoning process.

The end of Moore's Law and Dennard Scaling has combined with advances in agile hardware design to foster a golden age of domain-specific acceleration. However, this new frontier of computing opportunities is not without pitfalls. As computer architects approach unfamiliar domains, we have seen common themes emerge in the challenges that can hinder progress in the development of useful acceleration. In this work, we present the Magnificent Seven Challenges in domain-specific accelerator design that can guide adventurous architects to contribute meaningfully to novel application domains. Although these challenges appear across domains ranging from ML to genomics, we examine them through the lens of autonomous systems as a motivating example in this work. To that end, we identify opportunities for the path forward in a successful domain-specific accelerator design from these challenges.

AI systems for high quality music generation typically rely on extremely large musical datasets to train the AI models. This creates barriers to generating music beyond the genres represented in dominant datasets such as Western Classical music or pop music. We undertook a 4 month international research project summarised in this paper to explore the eXplainable AI (XAI) challenges and opportunities associated with reducing barriers to using marginalised genres of music with AI models. XAI opportunities identified included topics of improving transparency and control of AI models, explaining the ethics and bias of AI models, fine tuning large models with small datasets to reduce bias, and explaining style-transfer opportunities with AI models. Participants in the research emphasised that whilst it is hard to work with small datasets such as marginalised music and AI, such approaches strengthen cultural representation of underrepresented cultures and contribute to addressing issues of bias of deep learning models. We are now building on this project to bring together a global International Responsible AI Music community and invite people to join our network.