Optimizing National Security Strategies through LLM-Driven Artificial
Intelligence Integration
- URL: http://arxiv.org/abs/2305.13927v1
- Date: Sun, 7 May 2023 21:51:39 GMT
- Title: Optimizing National Security Strategies through LLM-Driven Artificial
Intelligence Integration
- Authors: Dmitry I Mikhailov
- Abstract summary: We will examine the United States progress in AI and ML from a military standpoint.
We will highlight the strategic significance of AI to national security and a set of strategic imperatives for military leaders and policymakers.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: As artificial intelligence and machine learning continue to advance, we must
understand their strategic importance in national security. This paper focuses
on unique AI applications in the military, emphasizes strategic imperatives for
success, and aims to rekindle excitement about AI's role in national security.
We will examine the United States progress in AI and ML from a military
standpoint, discuss the importance of securing these technologies from
adversaries, and explore the challenges and risks associated with their
integration. Finally, we will highlight the strategic significance of AI to
national security and a set of strategic imperatives for military leaders and
policymakers
Related papers
- Imagining and building wise machines: The centrality of AI metacognition [78.76893632793497]
We argue that shortcomings stem from one overarching failure: AI systems lack wisdom.
While AI research has focused on task-level strategies, metacognition is underdeveloped in AI systems.
We propose that integrating metacognitive capabilities into AI systems is crucial for enhancing their robustness, explainability, cooperation, and safety.
arXiv Detail & Related papers (2024-11-04T18:10:10Z) - Standardization Trends on Safety and Trustworthiness Technology for Advanced AI [0.0]
Recent AI technologies based on large language models and foundation models are approaching or surpassing artificial general intelligence.
These advancements have raised concerns regarding the safety and trustworthiness of advanced AI.
Efforts are being expended to develop internationally agreed-upon standards to ensure the safety and reliability of AI.
arXiv Detail & Related papers (2024-10-29T15:50:24Z) - Attack Atlas: A Practitioner's Perspective on Challenges and Pitfalls in Red Teaming GenAI [52.138044013005]
generative AI, particularly large language models (LLMs), become increasingly integrated into production applications.
New attack surfaces and vulnerabilities emerge and put a focus on adversarial threats in natural language and multi-modal systems.
Red-teaming has gained importance in proactively identifying weaknesses in these systems, while blue-teaming works to protect against such adversarial attacks.
This work aims to bridge the gap between academic insights and practical security measures for the protection of generative AI systems.
arXiv Detail & Related papers (2024-09-23T10:18:10Z) - Strategic AI Governance: Insights from Leading Nations [0.0]
Artificial Intelligence (AI) has the potential to revolutionize various sectors, yet its adoption is often hindered by concerns about data privacy, security, and the understanding of AI capabilities.
This paper synthesizes AI governance approaches, strategic themes, and enablers and challenges for AI adoption by reviewing national AI strategies from leading nations.
arXiv Detail & Related papers (2024-09-16T06:00:42Z) - Trustworthy, Responsible, and Safe AI: A Comprehensive Architectural Framework for AI Safety with Challenges and Mitigations [14.150792596344674]
AI Safety is an emerging area of critical importance to the safe adoption and deployment of AI systems.
Our goal is to promote advancement in AI safety research, and ultimately enhance people's trust in digital transformation.
arXiv Detail & Related papers (2024-08-23T09:33:48Z) - Towards Guaranteed Safe AI: A Framework for Ensuring Robust and Reliable AI Systems [88.80306881112313]
We will introduce and define a family of approaches to AI safety, which we will refer to as guaranteed safe (GS) AI.
The core feature of these approaches is that they aim to produce AI systems which are equipped with high-assurance quantitative safety guarantees.
We outline a number of approaches for creating each of these three core components, describe the main technical challenges, and suggest a number of potential solutions to them.
arXiv Detail & Related papers (2024-05-10T17:38:32Z) - AI-Powered Autonomous Weapons Risk Geopolitical Instability and Threaten AI Research [6.96356867602455]
We argue that the recent embrace of machine learning in the development of autonomous weapons systems (AWS) creates serious risks to geopolitical stability and the free exchange of ideas in AI research.
ML is already enabling the substitution of AWS for human soldiers in many battlefield roles, reducing the upfront human cost, and thus political cost, of waging offensive war.
Further, the military value of AWS raises the specter of an AI-powered arms race and the misguided imposition of national security restrictions on AI research.
arXiv Detail & Related papers (2024-05-03T05:19:45Z) - Quantifying AI Vulnerabilities: A Synthesis of Complexity, Dynamical Systems, and Game Theory [0.0]
We propose a novel approach that introduces three metrics: System Complexity Index (SCI), Lyapunov Exponent for AI Stability (LEAIS), and Nash Equilibrium Robustness (NER)
SCI quantifies the inherent complexity of an AI system, LEAIS captures its stability and sensitivity to perturbations, and NER evaluates its strategic robustness against adversarial manipulation.
arXiv Detail & Related papers (2024-04-07T07:05:59Z) - Managing extreme AI risks amid rapid progress [171.05448842016125]
We describe risks that include large-scale social harms, malicious uses, and irreversible loss of human control over autonomous AI systems.
There is a lack of consensus about how exactly such risks arise, and how to manage them.
Present governance initiatives lack the mechanisms and institutions to prevent misuse and recklessness, and barely address autonomous systems.
arXiv Detail & Related papers (2023-10-26T17:59:06Z) - AI Maintenance: A Robustness Perspective [91.28724422822003]
We introduce highlighted robustness challenges in the AI lifecycle and motivate AI maintenance by making analogies to car maintenance.
We propose an AI model inspection framework to detect and mitigate robustness risks.
Our proposal for AI maintenance facilitates robustness assessment, status tracking, risk scanning, model hardening, and regulation throughout the AI lifecycle.
arXiv Detail & Related papers (2023-01-08T15:02:38Z) - Proceedings of the Artificial Intelligence for Cyber Security (AICS)
Workshop at AAAI 2022 [55.573187938617636]
The workshop will focus on the application of AI to problems in cyber security.
Cyber systems generate large volumes of data, utilizing this effectively is beyond human capabilities.
arXiv Detail & Related papers (2022-02-28T18:27:41Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.