Artificial Intelligence (AI) is often misunderstood in both French and English definitions. While they describe AI as machines mimicking human intelligence, a more accurate perspective is that AI enables systems to autonomously solve problems without explicit instructions. This autonomy is what differentiates AI from traditional algorithms, whose purpose is to solve complex problems efficiently, not to replicate human thought.
For example, systems like Waze use deterministic algorithms such as A-Star to find routes, but this isn't true AI. In AI-based systems, such as Synapse Postmaster™, the system determines the optimal path through complex, interdependent tasks autonomously, adapting to changing data without predefined workflows.
Lessons from chess engines illustrate the power of AI. Traditional engines like Stockfish relied on brute-force search, while AlphaZero uses deep learning to learn the game. The future of software may lie in combining both deterministic and AI-driven approaches for maximum efficiency.
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While AI has been a focus of attention over the past year, it’s important to note that AI extends far beyond the capabilities of LLMs.
AI’s real distinction, compared to traditional algorithms, is not in mimicking human intelligence but in its ability to autonomously solve problems without being provided with explicit solution paths.
This article delves into the differences between deterministic and AI-driven approaches in problem-solving.
It illustrates the contrast between these two approaches in the development of autonomous agents, showing when each approach is more relevant depending on the specific problem or context.
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Associate Professor: Head of iPain lab & Biostatistics MPH track
9moAlexandra Zhuravlyova