The French version of Wikipedia defines Artificial Intelligence as a "set of theories and techniques..." but this definition is particularly lacking. We propose a more useful definition: artificial intelligence represents systems capable of solving problems without explicit solution paths.
The real value of AI lies in finding effective solutions through methods that would be impractical or impossible using deterministic approaches alone.
Defining the Difference
Let's take Waze's A-Star algorithm as an example. Though it appears intelligent - finding optimal routes through complex road networks - it remains deterministic and thus doesn't qualify as true AI. Given the same inputs, it will always produce the same output following a predefined algorithm.
True AI, by contrast, can adapt and learn from experience, potentially producing different (and improved) outputs over time for similar inputs.
Two Approaches Compared
Deterministic/Workflow Approach
- Predetermined, explicit steps
- Straightforward implementation
- Difficult to modify
- Predictable but inflexible
- Works well for known scenarios
AI/Expert System Approach
- Objectives and constraints defined
- System autonomously determines optimal paths
- More complex implementation
- Adaptable and scalable
- Handles novel situations
Synapse Postmaster Implementation
Postmaster leverages AI specifically to handle complex data dependencies across documents and external systems. The combinatorial complexity of real-world document processing makes deterministic workflows impractical.
Consider a typical scenario:
- An email arrives referencing a contract
- The contract links to multiple parties
- Each party has different CRM records
- Various ERP systems hold related data
- The request type determines which systems to query
A deterministic workflow would need to anticipate every possible combination. An AI system can navigate this complexity autonomously through inference.
The Chess Engine Analogy
Traditional chess engines like Stockfish used brute force evaluation of millions of positions. They were deterministic - given the same position, they'd always choose the same move.
AlphaZero introduced deep learning, developing intuition about positions rather than exhaustively calculating. It could beat Stockfish despite evaluating far fewer positions because it learned what mattered.
The Hybrid Future
Modern chess engines now combine both approaches - deep learning for position evaluation with deterministic search for tactical verification. This hybrid approach represents the future for many AI applications, including document processing.
When to Use Each Approach
Choose Deterministic When:
- The problem space is well-defined and bounded
- Regulatory compliance requires explicit logic
- Performance predictability is critical
- The solution path is known and optimal
Choose AI When:
- Input variations are too numerous to enumerate
- The optimal path isn't known in advance
- Natural language understanding is required
- The system needs to improve over time
Choose Hybrid When:
- You need AI's flexibility with deterministic reliability
- Auditability is required alongside adaptability
- Some decisions must be traceable while others can be inferred
Conclusion
The distinction between AI and deterministic algorithms isn't about intelligence - it's about problem-solving approach. Deterministic algorithms excel at problems with known solution paths. AI excels when the path must be discovered.
At SynapseDX, we believe the future lies in hybrid systems that leverage the strengths of both approaches. Our inference engine provides deterministic, auditable decision-making while LLMs handle the language understanding that deterministic systems cannot.