Classification of AI Applications
As we embark on our journey through Edge AI Engineering with the Raspberry Pi, it’s essential to understand the fundamental classification of AI applications that form the structure of this book. Our exploration is divided into two parts, each representing a different paradigm in artificial intelligence implementation.
Fixed Function AI vs. Generative AI
AI applications can be broadly categorized into two approaches that represent different capabilities, interaction models, and implementation strategies:
Fixed Function AI (Reactive)
Fixed Function AI, or Reactive AI, operates by analyzing specific inputs according to predetermined patterns and rules and then producing consistent outputs for given scenarios. These systems:
- Respond to specific triggers: They activate only when presented with particular inputs.
- Follow defined patterns: Their behavior is predictable and consistent.
- Excel at structured tasks: They perform exceptionally well at classification, detection, and pattern recognition
- Operate within boundaries: Their capabilities are limited to their specific programming.
In the first part of this book (Chapters 2-4), we explore fixed-function AI through computer vision applications:
- Image classification for identifying objects in images
- Object detection for locating and labeling multiple objects
- Specialized detection applications like counting objects
These applications demonstrate how edge devices can deliver reliable, efficient AI in constrained environments, focusing on specific, well-defined tasks.
Generative AI (Proactive)
Generative AI, also known as Proactive AI, represents a fundamental shift in capability. These systems can:
- Create new content: They generate novel text, images, or solutions.
- Understand context: They interpret and respond to nuanced situations.
- Engage in dialogue: They maintain contextual awareness across interactions.
- Adapt to novel scenarios: They apply knowledge to situations beyond their explicit training.
The second part of this book (Chapters 5-9) explores Generative AI at the edge:
- Small Language Models that bring conversational AI to edge devices
- Vision-Language Models that combine visual and textual understanding
- Physical computing integration that connects AI to the real world
- Advanced techniques to enhance edge AI capabilities
This progression from Fixed Function to Generative AI mirrors the evolution of artificial intelligence itself—from specialized systems designed for specific tasks to more flexible, creative systems capable of addressing a broader range of challenges.
Summary Table
| AI Type | Core Focus | Example Applications | Typical Use Cases |
|---|---|---|---|
| Fixed Function (Reactive) | Data analysis, assessment, automation | Fraud detection, spam filters, image recognition | Banking, healthcare diagnostics, security systems |
| Generative (Proactive) | Content creation, anticipation, dialogue | ChatGPT, DALL·E, predictive maintenance, smart assistants | Content creation, customer support, design, automation |
Conclusion
- Fixed Function (Reactive) AI is ideal for applications requiring efficiency, predictability, and low resource use, where tasks are well-defined and do not require creative output or adaptation.
- Generative (Proactive) AI is suited for scenarios demanding creativity, personalization, and anticipatory actions. It enables richer, more human-like interactions and innovative solutions across industries.
The Edge AI Advantage
Both Fixed Function and Generative AI gain unique benefits when deployed at the edge:
- Reduced latency: Processing happens locally, eliminating network delays
- Enhanced privacy: Sensitive data remains on-device
- Operational reliability: Systems function regardless of network connectivity
- Resource efficiency: Optimized models utilize limited hardware effectively
By understanding these fundamental classifications, you’ll realize how different AI approaches serve distinct purposes and how each can be effectively implemented on edge devices like the Raspberry Pi.
As we progress through this book, this classification framework will help contextualize each project and technique, connecting individual implementations to broader AI concepts and applications.