This study provides a comprehensive performance analysis of Data-Oriented Design (DOD) versus the traditional Object-Oriented Design (OOD), focusing on cache utilization and efficiency in multi-threaded environments. We developed and compared four distinct versions of the A* search algorithm: single-threaded OOD (ST -OOD), single-threaded DOD (ST -DOD), multi-threaded OOD (MT -OOD), and multi-threaded DOD (MT -DOD). The evaluation was based on metrics including execution time, memory usage, and CPU cache misses. In multi-threaded tests, the DOD implementation demonstrated considerable performance gains, with faster execution times and a lower number of raw system calls and cache misses. While OOD occasionally showed marginal advantages in memory usage or percentage-based cache miss rates, DOD's efficiency in data-intensive operations was more evident. Furthermore, our findings reveal that for a fine-grained task like the A* algorithm, the overhead associated with thread management led to single-threaded versions significantly outperforming their multi-threaded counterparts in both paradigms. We conclude that even when performance differences appear subtle in simple algorithms, the consistent advantages of DOD in critical metrics highlight its foundational architectural superiority, suggesting it is a more effective approach for maximizing hardware efficiency in complex, large-scale AI and parallel computing tasks.

In the context of unprecedented U.S. Department of Defense (DoD) budgets, this paper examines the recent history of DoD funding for academic research in algorithmically based warfighting. We draw from a corpus of DoD grant solicitations from 2007 to 2023, focusing on those addressed to researchers in the field of artificial intelligence (AI). Considering the implications of DoD funding for academic research, the paper proceeds through three analytic sections. In the first, we offer a critical examination of the distinction between basic and applied research, showing how funding calls framed as basic research nonetheless enlist researchers in a war fighting agenda. In the second, we offer a diachronic analysis of the corpus, showing how a 'one small problem' caveat, in which affirmation of progress in military technologies is qualified by acknowledgement of outstanding problems, becomes justification for additional investments in research. We close with an analysis of DoD aspirations based on a subset of Defense Advanced Research Projects Agency (DARPA) grant solicitations for the use of AI in battlefield applications. Taken together, we argue that grant solicitations work as a vehicle for the mutual enlistment of DoD funding agencies and the academic AI research community in setting research agendas. The trope of basic research in this context offers shelter from significant moral questions that military applications of one's research would raise, by obscuring the connections that implicate researchers in U.S. militarism.

U.S. government spending on artificial intelligence has exploded in the past year, driven by increased military investments, according to a report by the Brookings Institution, a think tank based in Washington D.C. The report found that the potential value of AI-related federal contracts increased by almost 1,200%, from 355 million in the period leading up to August 2022, to 4.6 billion in the period leading up to August 2023. This increase was almost entirely driven by the Department of Defense (DoD). The total amount committed by the DoD to AI-related contracts increased from 190 million in the period leading up to August 2022 to 557 million in the period leading up to August 2023. The total that the DoD might spend on AI-related contracts if each contract were extended to its fullest terms grew even faster, from 269 million in the period leading up to August 2022 to 4.3 billion in the period leading up to August 2023.

A series of airstrikes carried out by the United States and the United Kingdom on Saturday destroyed or damaged 17 of 18 Houthi targets in Yemen, Department of Defense (DoD) officials told Fox News on Tuesday. The targets included underground weapons storage facilities, missile storage facilities, one-way attack unmanned aerial systems, air defense systems, radars, and a helicopter, said DoD spokesperson U.S. Army Major Pete Nguyen. The coalition airstrikes targeted Yemen's Iran-backed Houthis, and came days after a British cargo ship was hit by a Houthi missile. "More broadly, since the first coalition strikes on Jan. 11, we assess that we've destroyed or degraded more than 150 missiles and launchers, including anti-ship land attack and surface-to-air missiles, plus numerous communication capabilities, unmanned aerial vehicles, unmanned surface vessels, coastal radars, air surveillance capabilities, rotary wing aircraft, underground facilities including weapon storage areas, and command and control buildings," Nguyen said. Gen. Pat Ryder said the strikes have degraded "a significant amount of capability" for the Houthis.

AGI, while powerful, could have negative consequences, warned Diveplane CEO Mike Capps and Liberty Blockchain CCO Christopher Alexander. The House last week passed a defense policy bill that strongly encourages the Pentagon to use artificial intelligence to its advantage, but also requires defense officials to examine how America's national security infrastructure may be vulnerable to AI systems deployed by China, Russia and other adversaries. Rep. Marc Molinaro, R-N.Y., pushed to include language in the bill requiring an assessment of AI vulnerabilities, and watched it pass easily on the House floor. That's a strong sign the language will remain in the final bill even after a negotiation with the Senate, and Molinaro told Fox News Digital that this assessment is needed in the face of ever-evolving AI capabilities. "The average person knows at least the rudimentary use of AI. China, terrorists, Russia are using AI in a much more sophisticated way, certainly as aggressors," he told Fox news Digital.

Center for A.I. Safety Director Dan Hendrycks explains concerns about how the rapid growth of artificial intelligence could impact society. A new report from the U.S. Government Accountability Office (GAO) to the Senate Committee on Armed Services found that the Department of Defense needs to issue department-wide artificial intelligence acquisitions guidance. The 44-page-long report shared last month found that the department has begun to pursue increasingly advanced AI capabilities. The office said the department has "historically struggled to acquire weapon systems software" and noted AI acquisitions pose "additional challenges." The GAO analyzed information provided by 13 companies in the private sector regarding how they successfully acquire AI capabilities to determine key factors. The companies considered multiple factors when acquiring such capabilities, including understanding the need and if AI is appropriate, making a business case for AI, tailoring a contracting approach to protect access to data and systems, testing and evaluating proposed solutions and forecasting fur AI capabilities that may be valuable.

The rapid adoption and scrutiny around ChatGPT, the latest artificial intelligence model, highlights the exploding potential of AI. It also underscores a stark delta between AI's current capabilities and human willingness to embrace them. This untapped potential is perhaps most apparent in the public sector where AI is both championed and feared - offering exciting federal use cases but lacking policy guidance and maturity at scale. So how can agencies better leverage AI? Results of a recent SAIC survey uncover a need to improve overall AI readiness to enable the technology's integration and benefit from it. The independent survey of federal government executives revealed that while AI is on respondents' radar, fewer than one in five are "very" likely to adopt AI in the next year.

When forward-deployed Army soldiers need air support, an operations center is tasked with identifying and assigning aircraft aid. With traditional software, an operator moves through a multistep process to search for available aircraft, identify their call signs and assess the munitions they carry. Pulling this relevant information can take several minutes--a long time to wait when making "real-time" decisions for immediate support. Considering the massive amount of information the U.S. Department of Defense must sift through every day and increasingly sophisticated UAVs and UASs collecting even more data, it's no surprise the Pentagon has turned to artificial intelligence for help. The newly launched Chief Digital and Artificial Intelligence Office and efforts and strategies such as the Artificial Intelligence and Data Acceleration initiative, Joint All Domain Command and Control and JAIC shows that DoD recognizes the potential of AI in decision compression.

In our prior series of papers for Brookings, we explored the rise of national artificial intelligence (AI) strategy documents and sought to make sense of what each country was trying to do and how effectively they were doing it. In our concluding paper, we focused on where the U.S. was lagging behind and proposed options to remedy the lagging. In particular, we recommended three options: 1) apply lessons from the U.S. space race to invigorate talent development, (2) adopt a multi-national consortium approach (similar to NATO) and (3) create a robust partnership with one other country. Following the guidance of "Deep Throat" of Watergate fame, in this new series of articles, we follow the federal trail of money to understand the federal market for AI work, the hardware, software, and services being purchased. We also track the key players who allocate the money (legislators), spend the money (program managers), and receive the money (vendors). Taken together, this series provides a comprehensive look at federal IT spending, its direction, and its key players.

Mechanisms should be explored to expand collaborations between DoD and Silicon Valley companies regarding threats posed by cyberattacks, a potential application for AI that Silicon Valley engineers see as a critical global threat. Expansion of engagements among personnel involved with military operations, DoD technical experts, and Silicon Valley individual contributors (nonmanagerial employees) working in technical roles should be explored to assess possible conduits for developing greater trust between the organizations. The potential benefits of DoD engaging Silicon Valley engineers on some of the details of how DoD would use AI should be explored; also, review how the military considers the nuanced and complex situations in which AI would be used. The value of establishing opportunities for DoD and Silicon Valley employees to engage over shared values and principles and the potential benefits of doing so should be investigated. The recently published DoD ethical principles for AI demonstrate that DoD itself is uncomfortable with some potential uses for AI: This could serve as the foundation for a conversation with Silicon Valley engineers about what AI should and should not be used for.