Jürgen Schmidhuber: World Models, RL and Year That Changed AI
type: NewsArticle
description: Jürgen Schmidhuber, a pioneer in artificial intelligence whose lab developed foundational ideas like LSTM, world models, and artificial curiosity decades before they became mainstream, argued in a new interview that large language models are merely powerful prediction machines that imitate the web, not true decision-making systems. Schmidhuber, speaking on The Information Bottleneck podcast, emphasized that acting in the world requires a controller using a world model to plan, a distinction he has made since his 1990 work on artificial curiosity and adversarial setups predating GANs. The conversation also covered credit assignment disputes, the need for sub-goals over millisecond-by-millisecond planning, and his prediction about self-replicating machines in space.
date published: 2026-06-06 05:00:53+00:00
date modified: 2026-06-06 05:16:05.704524+00:00
language: en
is accessible for free: true
article body: # Jürgen Schmidhuber - World Models, RL, and the Year that changed AI (Part 1) In this episode, we host Jürgen Schmidhuber - the man, the legend, one of the godfathers of modern AI. His lab worked out many ideas behind today’s systems (LSTM, world models, artificial curiosity, Transformer variants, and even GAN-style setups) decades before they became fashionable, and he’s just as well known for making sure people remember who did what first. This is the first of two conversations with him. We go back to his lab in the early 90s and ask how one small group came up with so many of the ideas that are now being scaled to a thousand billion dollars, back when compute was ten million times more expensive. A lot of the episode comes down to one distinction he keeps making: prediction vs. decision-making. His take is that LLMs are very good prediction machines that imitate the web, but that’s only half the problem. To actually act in the world, you need a controller that uses a world model to plan. He talks about his 1990 work on world models and artificial curiosity, where the controller gets rewarded for running experiments that improve its own model (an adversarial setup years before GANs), why planning millisecond by millisecond doesn’t scale, and why you need sub-goals instead. We also talk about compression as the core of understanding, from falling apples to Kepler to Einstein, and why we still don’t have a robot that can do what a plumber does, even though the AI behind the screen keeps getting better. Then the conversation moves to credit assignment: how “to Schmidhuber” became a verb, what he thinks is broken about the award system, and a long exchange on PMAX vs. JEPA. He ends on the real origins of deep learning and a prediction about self-replicating machines in space. **Timeline** 00:00 Intro 00:55 1991 in Munich, and why that lab mattered 02:38 "I'm not very smart" and why compute getting 10× cheaper every 5 years changed everything 04:25 Chess as an AI prox…
publisher: Web Pulse
keywords: artificial-intelligence, machine-learning, large-language-models, neural-networks, ai-research
mentions: Jürgen Schmidhuber
mentions: LSTM
mentions: world models
mentions: artificial curiosity
mentions: Transformer
mentions: GAN
citation: CreativeWork