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The potential for big data to contribute to the US intelligence mission goes beyond bulk collection, social media and counterterrorism. Applications will speak to a range of issues of major concern to intelligence agencies, from military operations to climate change to cyber security. There are challenges too: procurement lags, data stovepiping, separating signal from noise, sources and methods, a range of normative issues, and central to managing these challenges, human capital. These potential applications and challenges are discussed and a closer look at what data scientists do in the Intelligence Community (IC) is offered. Effectively filling the ranks of the IC’s data science workforce will depend on the provision of well-trained data scientists from the higher education system. Program offerings at America’s top fifty universities will thus be surveyed (just a few years ago there were reportedly no degrees in data science). One Master’s program that has melded data science with intelligence is examined as well as a university big data research center focused on security and intelligence. This discussion goes a long way to clarify the prospective uses of data science in intelligence while probing perhaps the key challenge to optimal application of big data in the IC.

Treatment planning is an essential step of the radiotherapy workflow. It has become more sophisticated over the past couple of decades with the help of computer science, enabling planners to design highly complex radiotherapy plans to minimize the normal tissue damage while persevering sufficient tumor control. As a result, treatment planning has become more labor intensive, requiring hours or even days of planner effort to optimize an individual patient case in a trial-and-error fashion. More recently, artificial intelligence has been utilized to automate and improve various aspects of medical science. For radiotherapy treatment planning, many algorithms have been developed to better support planners. These algorithms focus on automating the planning process and/or optimizing dosimetric trade-offs, and they have already made great impact on improving treatment planning efficiency and plan quality consistency. In this review, the smart planning tools in current clinical use are summarized in 3 main categories: automated rule implementation and reasoning, modeling of prior knowledge in clinical practice, and multicriteria optimization. Novel artificial intelligence–based treatment planning applications, such as deep learning–based algorithms and emerging research directions, are also reviewed. Finally, the challenges of artificial intelligence–based treatment planning are discussed for future works.

The aim of this study was to make unstructured neuropathological data, located in the NeuroBioBank (NBB), follow Findability, Accessibility, Interoperability, and Reusability principles and investigate the potential of large language models (LLMs) in wrangling unstructured neuropathological reports. By making the currently inconsistent and disparate data findable, our overarching goal was to enhance research output and speed. The NBB catalog currently includes information from medical records, interview results, and neuropathological reports. These reports contain crucial information necessary for conducting an in-depth analysis of NBB data but have multiple formats that vary across different NBB biorepositories and change over time. In this study, we focused on a subset of 822 donors with Parkinson’s disease (PD) from seven NBB biorepositories. We developed a data model with combined Brain Region and Pathological Findings data at its core. This approach made it easier to build an extraction pipeline and was flexible enough to convert resulting data to Common Data Elements, a standardized data collection tool used by the neuroscience community to improve consistency and facilitate data sharing across studies. This pilot study demonstrated the potential of LLMs in structuring unstructured neuropathological reports of PD patients available in the NBB. The pipeline enabled successful extraction of detailed tissue-level (microscopic) and gross anatomical (macroscopic) observations, along with staging information from pathology reports, with extraction quality comparable to manual curation results. To our knowledge, this is the first attempt to automatically standardize neuropathological information at this scale. The collected data have the potential to serve as a valuable resource for PD researchers, facilitating integration with clinical information and genetic data (such as genome-wide genotyping and whole-genome sequencing) available through the NBB, thereby enabling a more comprehensive understanding of the disease.

China is engaged in longstanding cyber espionage against the U.S., as well as other nations, to collect sensitive public and private information in support of national objectives laid out in its 12thFive Year Plan. Foreign governments citing China’s malfeasance have rebuked these activities, a claim vehemently denied by Beijing. In response, China is leveraging the “Three Warfares” an integrated three-prong information warfare strategy to combat these accusations by leveraging Media, Legal, and Psychological components designed to influence the international community. While the United States has threatened the imposition of economic sanctions, Beijing has successfully parried consequential actions by arresting U.S.-identified hackers, thereby demonstrating its commitment toward preserving a stable and peaceful cyberspace. These interrelated “Three Warfares” disciplines have targeted the cognitive processes of the U.S. leadership, as well as the international public’s perception of China as a global threat, thereby having successfully forestalled the implementation of any effective punitive or economic deterrence strategy to include the imposition of cyber sanctions.

Midway through the second decade of the twenty-first century, it has become increasingly apparent that the majority of Americans are relatively ignorant of international affairs and lacking in foreign language proficiency. For the emergent academic discipline of intelligence studies, this represents a serious challenge. All too often policy decisions, particularly in American foreign policy, have been driven by assumptions, especially in regard to cultures and societies with which Americans have had little familiarity. Therefore, the twenty-first century intelligence studies curriculum would be well served by educating students in global affairs and foreign languages as well as in the core skills related to analysis and collection.

Espionage is often referred to as the world’s second oldest profession, and human intelligence is the oldest collection discipline. When many people think of espionage the images that often come to mind are fictional characters such as Jason Bourne or James Bond. Human intelligence encompasses much more than “secret agents” using their “toys” to collect top-secret information. Teaching human intelligence within an academic setting can be difficult because of the clandestine nature of tradecraft and sources of intelligence. Ironically, it is television and film that brought us Bourne and Bond that can also aid in the teaching of the variety of issues and concepts important to the study of human intelligence. This paper will examine how television and movies inspired by actual events are used as case studies to teach human intelligence in an academic setting. Cases are examined through the lenses of a variety of issues and concepts related to human intelligence, including source acquisition and development, sleepers, interrogation, denial and deception, and the legal and ethical issues impacting collection efforts. The Assets, The Americans, Turn, and Zero Dark Thirty are some of the titles that are utilized in this teaching approach and examples of how these specific titles are used are provided.

Abstract The use of children’s drawings to examining their conceptual understanding has been proven to be an effective method, but there are two major problems with previous research: (i) The content of the drawings heavily relies on the task, and the ecological validity of the conclusions is low. (ii) The interpretation of drawings relies too much on the subjective feelings of the researchers. To address this issue, this study uses the Large Language Model (LLM) to identify 1420 children’s scientific drawings (covering nine scientific themes/concepts) and uses the word2vec algorithm to calculate their semantic similarity. The study explores whether there are consistent drawing representations for children on the same theme and attempts to establish a norm for children’s scientific drawings, providing a baseline reference for follow-up children’s drawing research. The results show that the representation of most drawings has consistency, manifested as most semantic similarity >0.8. At the same time, it was found that the consistency of the representation is independent of the accuracy (of LLM’s recognition), indicating the existence of consistency bias. In the subsequent exploration of influencing factors, we used Kendall rank correlation coefficient to investigate the effects of “sample size,” “abstract degree,” and “focus points” on drawings and used word frequency statistics to explore whether children represented abstract themes/concepts by reproducing what was taught in class. It was found that accuracy (of LLM’s recognition) is the most sensitive indicator, and data such as sample size and semantic similarity are related to it. The consistency between classroom experiments and teaching purpose is also an important factor, many students focus more on the experiments themselves rather than what they explain. In addition, most children tend to use examples they have seen in class to represent more abstract themes/concepts, indicating that they may need concrete examples to understand abstract things.

We present a formal model of an intelligence agency that must divide its resources between the collection and analysis of information pertaining to terror plots. The model highlights the negative consequences of queues which form when collection exceeds analytic capacity. We incorporate the response of a terrorist organization to the operating characteristics of the intelligence system it faces, and solve for equilibrium strategies for the intelligence system and terrorist organization. Our results demonstrate the importance of properly balancing resources between collection and analysis, and stand in contrast to the observed state of overcollection in US intelligence agencies.

The Iranian Revolutionary Guard Corps (IRGC) is a military and paramilitary organization that is meant to defend the ideals of the Iranian Islamic Revolution in 1979. Since its formation, the IRGC has grown in influence and its intelligence role has expanded. This paper examines the role of the IRGC in Iran’s intelligence system through a comprehensive analysis of the organization of the IRGC’s intelligence arm, along with its operations and capabilities. In doing so, the scope, objectives, resources, customers, and sponsors of the IRGC’s intelligence activities are also analyzed. Additionally, this paper explores how the IRGC interacts with the government of Iran, the Ministry of Intelligence and Security (MOIS), other key internal stakeholders, and foreign client organizations. A key focus of this analysis is the evolution of the relationship between the IRGC and the MOIS and the growing influence of the IRGC in Iran’s intelligence community over the last decade. The paper concludes that the IRGC has now eclipsed the MOIS within Iran’s intelligence community and is one of the most powerful institutions in Iranian politics today, using its intelligence activities as its key means of maintaining power and influence within the country.

The intelligence community is facing a new type of organization, one enabled by the world’s information and communications infrastructure. These asyngnotic networks operate without leadership and are self-organizing in nature. They pose a threat to national security because they are difficult to detect in time for intelligence to provide adequate warning. Social network analysis and link analysis are important tools but can be supplemented by application of neuroscience principles to understand the forces that drive asyngnotic self-organization and triggering of terrorist events. Applying Living Systems Theory (LST) to a terrorist attack provides a useful framework to identify hidden asyngnotic networks. There is some antecedent work in propaganda analysis that may help uncover hidden asyngnotic networks, but computerized SIGINT methods face a number of challenges.