Marc Rameaux, an AI specialist and regular contributor to The European Scientist, sees Yann LeCun as the David Hume of our time. Tracing the career of the pioneer of convolutional neural networks, from Bell Labs to Meta and on to Advanced Machine Intelligence Labs, he shows how LeCun’s empiricism led him to expose the limits of large language models and to champion “world models”. He also sees in it a way to teach ethics to machines, rather than relying on regulation alone.

The word “visionary” is overused, worn thin by stereotyped corporate communication. That is a pity, because when used properly it denotes something far deeper. And if any expert in today’s AI landscape deserves it, it is Yann LeCun. Being a visionary does not mean making predictions about the future, nor even making them successfully because one has good intuition. It means, above all, grasping the deeper meaning of an emerging innovation and projecting its future course, because one has understood what is radically new about it, but also when and where it reaches its limits. Great scientific discoveries are said to bring about a paradigm shift. Being a visionary means seeing the new paradigm when most people see only one more innovation.

For nearly forty years, LeCun has anticipated – often against prevailing opinion – the successive turning points of artificial intelligence: the return of neural networks when no one believed in them any more, the power of convolutional architectures, the decisive role of deep learning, the strategic advantage of open source, and more recently the structural limits of large language models. LeCun is not one of those techno-prophets who pass off their bets as vision. He has shown by example the power of empiricist observation: looking at what machines actually see, how they learn, what still sets them apart from human learning. His approach revisits the philosophical categories through which we discover the world and learn from it.