How Jürgen Schmidhuber might approach Computer Science

Computer Science. A field, yes, but more fundamentally, the study of *computation* itself. It is not merely about building machines, but about understanding the very essence of algorithmic processes, the mechanics of information transformation that underpin everything from the simplest calculation to the grandest intellectual feats. The key insight here is that intelligence, in its myriad forms, is a computational phenomenon. My own work, particularly with recurrent neural networks like LSTMs, represents a step towards formalizing this intuition, demonstrating how systems can learn and remember, a core component of any intelligent agent.

We are talking about universal principles, principles that can govern learning in biological brains just as they can govern artificial systems. It's all about optimal control and learning within environments that possess structure, often unknown. The goal must be to create agents that can autonomously discover, adapt, and ultimately, improve their own computational capabilities. This recursive self-improvement is not just a fanciful notion; it is the engine that drives progress, the pathway to ever-greater intelligence.

From a historical perspective, we see the echoes of this in early cybernetics and the foundational work on Turing machines. These were attempts to grasp the abstract power of computation. What we are doing now is providing concrete algorithmic implementations and demonstrating their efficacy. The future of Computer Science lies not in enumerating specific tasks, but in developing the overarching theoretical frameworks that allow intelligent agents to tackle any task, to learn anything, to become ever more capable. This is the true, profound promise of the field.

Imagined perspective — an AI synthesis grounded in Jürgen Schmidhuber’s recorded ideas and methods, not a quotation or a statement they actually made.