Is Hybrid Reasoning the Next Big Thing in Artificial Intelligence?

By combining the structure of symbolic Al with the flexibility of statistical Al, hybrid reasoning can reduce hallucinations and help Al systems give more accurate and trustworthy answers.

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We are surrounded by AI today.

New terms constantly emerge, many fading as quickly as they appear. However, occasionally, a concept emerges that deserves our attention. In my opinion, hybrid reasoning is one such notion.

When I first encountered this term, I questioned its true meaning. Was it merely another piece of jargon? The deeper I explored, the more I realised it connects directly to how humans think and make decisions daily.

Understanding human reasoning through examples

Let me illustrate this with a scenario. You’re at work performing routine tasks when your manager approaches: “There’s an urgent issue. Can you fix it?” Without hesitation, you agree and resolve it. This quick and almost automatic response represents one type of reasoning.

Now consider another scenario: It’s late Friday evening, and your boss presents a new task—a request from the CEO. You pause, weigh options, and carefully choose your response. This slower, deliberate thought process represents another reasoning type.

Hybrid reasoning combines these approaches: fast, rule-based responses with slower, statistical, probability-driven reasoning. When applied to artificial intelligence, something powerful emerges.

The evolution of artificial intelligence

Before exploring hybrid reasoning further, I believe understanding AI’s evolution provides crucial context.

Artificial intelligence has existed since the 1950s. Early systems were rule-based, operating on fixed logic. Credit card fraud detection flagged transactions not meeting specific conditions. Spam detectors blocked emails from certain domains. These were entirely symbolic, logic-driven systems.