As artificial intelligence scales, the concepts of alignment, agency, and autonomy have become central to AI safety, governance, and control. However, even in human contexts, these terms lack universal definitions, varying across disciplines such as philosophy, psychology, law, computer science, mathematics, and political science. This inconsistency complicates their application to AI, where differing interpretations lead to conflicting approaches in system design and regulation. This paper traces the historical, philosophical, and technical evolution of these concepts, emphasizing how their definitions influence AI development, deployment, and oversight. We argue that the urgency surrounding AI alignment and autonomy stems not only from technical advancements but also from the increasing deployment of AI in high-stakes decision making. Using Agentic AI as a case study, we examine the emergent properties of machine agency and autonomy, highlighting the risks of misalignment in real-world systems. Through an analysis of automation failures (Tesla Autopilot, Boeing 737 MAX), multi-agent coordination (Metas CICERO), and evolving AI architectures (DeepMinds AlphaZero, OpenAIs AutoGPT), we assess the governance and safety challenges posed by frontier AI.

Agency is a system's capacity to steer outcomes toward a goal, and is a central topic of study across biology, philosophy, cognitive science, and artificial intelligence. Determining if a system exhibits agency is a notoriously difficult question: Dennett (1989), for instance, highlights the puzzle of determining which principles can decide whether a rock, a thermostat, or a robot each possess agency. We here address this puzzle from the viewpoint of reinforcement learning by arguing that agency is fundamentally frame-dependent: Any measurement of a system's agency must be made relative to a reference frame. We support this claim by presenting a philosophical argument that each of the essential properties of agency proposed by Barandiaran et al. (2009) and Moreno (2018) are themselves frame-dependent. We conclude that any basic science of agency requires frame-dependence, and discuss the implications of this claim for reinforcement learning.

There is significant concern about the impact of generative AI on society. Modern AI tools are capable of generating ever more realistic text, images, and videos, and functional code, from minimal prompts. Accompanying this rise in ability and usability, there is increasing alarm about the misuses to which these tools can be put, and the intentional and unintentional harms to individuals and society that may result. In this paper, we argue that \emph{agency} is the appropriate lens to study these harms and benefits, but that doing so will require advancement in the theory of agency, and advancement in how this theory is applied in (agent-based) models.

Rite Aid will not be able to use any kind of facial recognition security system for next five years as part of its settlement with the Federal Trade Commission, which accused it of "reckless use of facial surveillance systems." The FTC said in its complaint that the drugstore chain deployed an artificial intelligence-powered facial recognition technology from 2012 to 2020 to identify customers who may have previously shoplifted or have engaged in problematic behavior. Apparently, the company had created a database with "tens of thousands" of customer images, along with their names, dates of birth and alleged crimes. Those photos were of poor quality, taken by its security cameras, employees' phones and even from news stories. As a result, the system generated thousands of false-positive alerts.

Deep learning (DL) systems have been widely adopted in many industrial and business applications, dramatically improving human productivity, and enabling new industries. However, deep learning has a carbon emission problem.a For example, training a single DL model can consume as much as 656,347 kilowatt-hours of energy and generate up to 626,155 pounds of CO2 emissions, approximately equal to the total lifetime carbon footprint of five cars. Therefore, in pursuit of sustainability, the computational and carbon costs of DL have to be reduced. Modeled after systems in the human brain and nervous system, neuromorphic computing has the potential to be the implementation of choice for low-power DL systems.

Abstract: Human and material agency have been investigated in the IS literature to understand how technology and humans influence each other. Some framings of agency treat humans and technology symmetrically, some privilege the agency of humans over technology, and others do not attribute agency to either humans or non-humans. The authors argue that with the new generation of technologies, such as AI, the notion of agency needs to differentiate between the actions of AI from that of traditional information systems and humans. The authors introduce the dimensions of agency to differentiate agencies while not privileging any actor. They illustrate the application of dimensions by using it as a lens to study the case of a technician using an AI solution for screening patients for early-stage breast cancer.

From cyber operations to disinformation, artificial intelligence extends the reach of national security threats that can target individuals and whole societies with precision, speed, and scale. As the US competes to stay ahead, the intelligence community is grappling with the fits and starts of the impending revolution brought on by AI. The US intelligence community has launched initiatives to grapple with AI's implications and ethical uses, and analysts have begun to conceptualize how AI will revolutionize their discipline, yet these approaches and other practical applications of such technologies by the IC have been largely fragmented. As experts sound the alarm that the US is not prepared to defend itself against AI by its strategic rival, China, Congress has called for the IC to produce a plan for integration of such technologies into workflows to create an "AI digital ecosystem" in the 2022 Intelligence Authorization Act. The term AI is used for a group of technologies that solve problems or perform tasks that mimic humanlike perception, cognition, learning, planning, communication, or actions.

Companies are using artificial intelligence for basic communication, to make product recommendations and more. In the world of marketing and advertising, the applications for AI are virtually endless--brands come up with new ideas and innovative ways to leverage it every day. What is the best way to employ AI in marketing and advertising in 2022? Every agency leader will have their own preferences and notions about the most effective use cases for AI in their space, and below, members of Forbes Agency Council share their current favorites. Forbes Agency Council members share exciting ways to leverage artificial Intelligence in marketing and advertising.

Agencies today are leveraging artificial intelligence technologies in many different ways and to varying degrees of success.

Agencies today are leveraging artificial intelligence technologies in many different ways and to varying degrees of success. A big question that has been on the minds of industry leaders in recent years is, "What is the best way for our organization to invest in AI?" From automating processes to serving as a conduit for better customer care and communication, agencies are embracing AI capabilities they hope will streamline operations and improve results for clients. Below, members of Forbes Agency Council draw upon their industry insights and personal experiences to explore how agencies can incorporate AI and what kinds of results they can expect. Forbes Agency Council members share ways to leverage artificial intelligence to improve an agency's workflow. At our company, we're using AI in a few areas, including for website SEO, where it has propelled a more than 50% increase in page views per visitor, year over year.