Large Language Models can mimic human output with stunning accuracy, but they lack the fundamental ability to reason about relationships and constraints. Instead of adding more parameters or bolting on reasoning capabilities, we need to build logical understanding into their foundations.
Billions of parameters, trillions of calculations, all poised to generate something remarkable. And they do. Today's Large Language Models produce text so compelling, so human-like, that it's easy to believe they understand what they're saying.But they don't. Not really. And that's the problem.We've built incredible pattern matchers, systems that can mimic human output with stunning fidelity. They can complete our sentences, draft our emails, even engage in seemingly deep conversations. But beneath the surface, something crucial is missing. These models float in an ocean of probability, never touching solid ground.What they lack isn't more parameters or bigger datasets. What they lack is something far more fundamental: the ability to understand and manipulate the logical structure of facts and relationships. They can learn patterns of how things relate, can reproduce them beautifully, but they can't reason about these relationships.We don't float in probability space — we build logical frameworks of facts and relationships and work within themThink about how you solve problems. Whether you're debugging code, composing music, or creating a new recipe, you're not just pattern matching — you're actively working with a web of relationships. Each decision you make establishes facts, creates connections, and gives you something concrete to build upon. Your first line of code creates relationships with what can follow. Your opening chord relates to every note that comes after. Your choice of main ingredient creates relationships with everything else on the plate.These aren't just metaphors. They're examples of how human intelligence actually works. We don't float in probability space — we build logical frameworks of facts and relationships and work within them. We understand them. We can explain them. We can modify them when they don't serve our needs.This isn't theoretical. We already have tools like Answer Set Programming (ASP) that can express and manipulate complex webs of facts and relationships. From database queries to interactive fiction, from deadlock detection to music understanding, ASP shows us what's possible when we make logical connections explicit and manipulable.Instead of treating logic as an afterthought, we need to build it into the foundation of how these models learnMany researchers recognize this need for deeper reasoning. Some propose having models generate and test code to verify their logic. Others add explicit "thinking steps" to force models to show their work. These are valuable experiments, but they treat logical reasoning as something to bolt on after the fact — an external process rather than a core capability.We need to go deeper. Instead of treating logic as an afterthought, we need to build it into the foundation of how these models learn. And for the first time, we have the tools to do this. Today's language models are powerful enough to help us generate the training data we need — examples of explicit logical reasoning, of how facts relate and combine, of how conclusions follow from premises. We can show these models not just what to output, but how to think.But the real breakthrough will come when we combine this explicit logical reasoning with the pattern-learning capabilities of neural networks. Imagine AI that doesn't just mimic human output but understands the logical structure behind it. That can explain how facts relate, adapt its understanding, and work with us to solve problems in fundamentally new ways.The future of AI isn't just about getting better output — it's about building systems that understand how facts and relationships fit together, just like we do. Systems that don't just float in probability space but build solid logical frameworks we can understand, verify, and trust.We don't need bigger models. We need smarter ones. Ones that understand the logic of how things relate to each other. Because at the end of the day, that's not just how we create — it's how we think.
Creativity doesn’t end with the first decision; that’s simply the spark. The true artistry lies in what comes next: the process of refining, shaping, and understanding. Constraints don’t limit us — they guide us toward clarity, revealing the solutions hidden in the chaos.3 January 2025
Intelligence isn't about absorbing patterns. It emerges from testing boundaries and discovering constraints. Whether physical or abstract, all understanding comes from having something to push against.7 January 2025
