Requirements and AI

General Discussions WadeShire
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The Critical Role of Requirements in Foundational AI Models

There is significant excitement around AI and its transformative potential across industries—and rightly so. However, one crucial aspect often overlooked is defining the needs of stakeholders before diving into AI model development.

Much of the focus in AI discussions revolves around foundational models: large, pre-trained systems capable of being fine-tuned for various applications. While these models promise to revolutionize industries, their development and maintenance come with significant costs. Without well-defined requirements, foundational models risk becoming unfocused, inefficient, and overly expensive, either missing key information or hoarding unnecessary data.

Why Requirements Matter

Foundational models are inherently complex, and their success depends on detailed planning. Requirements act as a blueprint, guiding every stage of development, from design to deployment and ongoing use. They ensure the model meets its intended purpose, aligns with ethical standards, and remains scalable and adaptable over time.

Poorly defined requirements can lead to:

  • Increased costs due to excess or irrelevant data.
  • Ethical challenges, such as biased outputs or lack of transparency.
  • Misalignment with organizational goals, rendering the model ineffective.

Key Areas to Address in AI Requirements

To develop a useful foundational model, stakeholders must answer critical questions upfront:

  1. Purpose and Use Case: What problem is the AI solving? Is it designed for image classification, sentiment analysis, or recommendation systems?
  2. Intended Users: Who will interact with the AI, and how will they use it?
  3. Functional Requirements: What tasks must the model perform? What outputs are expected?
  4. Performance Metrics: What accuracy, efficiency, or scalability benchmarks will define success?
  5. Data Requirements: What type, volume, and quality of data are necessary?
  6. Non-Functional Requirements: What constraints (e.g., technical, budgetary, timeline) or ethical considerations must be addressed?

The Path Forward

Given the broad and impactful nature of foundational models, clear and comprehensive requirements are essential. They not only align development with stakeholder goals but also ensure the model is ethical, practical, and cost-effective. By investing in upfront planning, organizations can maximize the transformative potential of AI while mitigating risks.

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How could one visualize the digital threads between these requirements and their relevant foundational model in ELM?

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One way to visualize the digital threads between AI requirements and the foundational model using ELM would be to use Doors Next Gen (Engineering Requirements Manager) or even Rhapsody SE to create graph-based diagrams, traceability matrices, or interactive dashboards. For example, a visualization could use nodes to Represent key requirement categories (e.g., Purpose, Data, Ethics, Performance); or connections (Edges) to show dependencies between requirements and the AI model components they influence; or color coding such as red, green or yellow to indicate various associations such as high-risk areas, fully defined requirements or areas needing more refinement, respectively. These could be interactively presented, such that clicking a node expands related sub-requirements and their dependencies.

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