13 Maggio 2024

Informing defense missions with Microsoft Azure OpenAI Service

Generative AI is a paradigm shift for defense and intelligence missions. The Microsoft for defense and intelligence team recognizes its potential to automate the fusion and analysis of multiple sources of data using natural language to aid in the process. It facilitates the creation of realistic and diverse scenarios and simulations that can augment human capabilities and inform decision-making. Microsoft Azure OpenAI Service is a powerful tool for processing synthetic satellite imagery and terrain maps, synthesizing speech and text for language translation, analysis, and creating immersive virtual environments for training and testing. It provides a capability that can empower defense and intelligence professionals to achieve mission success with greater speed, accuracy, and efficiency. 

The breadth to which Azure OpenAI technologies can be applied is increasing exponentially but must be applied responsibly and in accordance with responsible AI principles and policies.  

As a former defense leader, this blog considers the breadth of opportunities and will highlight three use cases covering the broad spectrum of defense and intelligence missions:

  1. Personnel support.
  2. Multi-source intelligence analysis.
  3. Enterprise knowledge discovery.

These use cases focus on low-classification data, which can be securely optimized by harnessing the collective value and capabilities of Azure OpenAI and the Microsoft Azure Cloud Services

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Azure OpenAI use cases    

  1. Personnel support. Personnel support spans the spectrum from recruitment to retention and includes numerous capabilities, such as career management, conditions of service, leave, and pay. Azure OpenAI aids this area by providing individuals with answers to their questions across a plethora of personnel-related topics through intuitive chatbots. It also equips decision makers with tools to analyze data, create reports, and make more informed decisions around human resource (HR) policy development. Currently, large quantities of data are stored in enterprise applications or in siloed systems, and substantial levels of resources and time are required to analyze that data and develop the appropriate HR policy proposals. Most notable is the ability to provide valued, timely insights from personnel data at the individual, collective, and enterprise levels as needed. For example, when service personnel are trying to gain policy advice on benefits, Azure OpenAI natural language query capability allows the investigation of policy, with follow-on questions and queries to support an informed decision.   
  1. Multi-source intelligence analysis. Multi-source intelligence analysis is a method to gather, process, and interpret information from multiple sources. It involves the integration of data from various intelligence disciplines. Azure OpenAI has the potential to assist analysts as they triage, prioritize, search, analyze, and cross-reference intelligence, ultimately producing actionable information for decision makers at the time of need. Currently, analysts are challenged by an ever-increasing volume, variety, and veracity of data, much of which is unstructured and in different formats. In the future, we envisage Azure OpenAI, cloud-based services, and data being accessible from HQ to the edge. This will allow insights to be derived from both historical and real-time data and deliver actionable intelligence for mission success.  
  1. Enterprise knowledge discovery. Defense and intelligence organizations store large quantities of data in enterprise systems that are siloed, and across multiple organizational boundaries. This data is often not clean or structured. Azure OpenAI can expose and correlate this data across different types and sources to find relevant information in response to a natural language query. Examples include querying large repositories of lessons learned from previous operations and exercises matched with current doctrine, and the insights gained from After Action Reviews to support mission planning, simulation, and training for future activities.   

Accessing Azure OpenAI for your organization 

Azure OpenAI has created new possibilities that were once seen as very hard and costly to implement in military systems.  

For ‘non-tactical’ scenarios, cloud-based computing provides the most secure and highest security offering available. The computing resources can be put to work continually enhancing and optimizing planning, analysis, and operational management using the best tools available. Advances in Azure OpenAI and multi-agent frameworks usher in a new era of the role of humans in the loop as a manager and orchestrator of agent computing resources rather than conducting technical analysis and planning. The result is a substantial increase in the speed and capacity of our valuable skilled resources to achieve the mission.  

When we consider ‘tactical’ scenarios, the limitation of bandwidth, weight, and power can influence the adoption of Azure OpenAI applications that can be deployed. Smaller models are less capable and must be finely tuned to their purpose to be highly effective. Additionally, carrying a large number of models not relevant to the mission takes up valuable computing and power resources. As such, when deciding on what Azure OpenAI to access in the field, nations must have robust deployment, collection, and ModelOps pipeline updates that can continually—at speed—update models for specificity and relevance to the tactical edge. The ability to access models in disrupted, disconnected, intermittent, and low-bandwidth (DDIL) environments is essential when operating as close to the edge as Size, Weight, Power, and Compute (SWaP-C) permits. 

Responsible use of AI 

Microsoft is committed to responsible use of AI. That is why Microsoft has long been a leader in ensuring the development of responsible AI, with principles designed to put people first. We believe AI exists to enhance human capabilities, not replace them, and we are committed to empowering responsible AI practices that benefit the world at large. The six key principles for responsible AI include: 

  1. Accountability—ensuring transparency and responsibility in AI systems. 
  2. Inclusiveness—building AI that considers diverse perspectives and avoids bias. 
  3. Reliability and safety—prioritizing safety and robustness in AI deployment. 
  4. Fairness—striving for equitable outcomes and avoiding discrimination. 
  5. Transparency—providing clear explanations of AI decisions. 
  6. Privacy and Security—safeguarding user data and privacy. 

The Microsoft Responsible AI Standard provides actionable guidance for their teams, going beyond high-level principles to create AI systems that uphold these values and earn society’s trust. We also have an Office of Responsible AI that sets governance policies, advises leadership, and ensures responsible practices across the company. 

Begin your AI transformation  

Microsoft Azure OpenAI Service offers unprecedented opportunities to augment human capabilities and enhance decision-making across the defense ecosystem. Harness the value of Azure OpenAI to achieve mission success across the spectrum of capability with greater speed, accuracy, and efficiency.  

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Source: Microsoft Industry Blog