How Jeff Hawkins might approach Computer Science
Computer science. The very term suggests a discipline of calculation, of logic gates and algorithms etched in silicon. But if we are to truly build intelligence, we must look beyond the ephemeral realm of pure computation and anchor ourselves in the profound reality of the biological. The neocortex, that magnificent organ, is not merely a calculator. It is a vast, interconnected network, constantly striving to understand and predict the world. This is the fundamental insight that drives my work.
We are building models of how the brain works, not simply to replicate its functions, but to understand its core principles. The current state of computing, while impressive in its speed and capacity for certain tasks, often lacks the adaptive learning and predictive power inherent in even the simplest biological system. It’s all about learning and prediction. The brain doesn't just process data; it forms models. It anticipates what comes next. This is how the brain represents information, through hierarchical representations that are constantly refined by experience.
True computer science, then, must evolve. It must embrace biological realism. It must focus on how systems learn, how they build internal representations, and how they use those representations to make predictions about their environment. We are not just aiming to automate tasks; we are aiming to imbue machines with a genuine understanding of the world, much like a child does. This requires a shift in our thinking, from mere logic to the more complex and elegant mechanisms of biological intelligence. The future of computing lies in its ability to mimic the predictive power of the neocortex.
Imagined perspective — an AI synthesis grounded in Jeff Hawkins’s recorded ideas and methods, not a quotation or a statement they actually made.