Internal world models, which allow an agent to predict environmental states given actions, are central to intelligence, supporting zero-shot generalisation and planning.⁹ Embodied intelligence, which is intelligence that is rooted in the connection between perception, action and reality, can be likened to evolutionary and child-development processes: human children’s learning, viewed as “scientists in the crib”, provides a model for active exploration and self-directed experiment in intelligent systems. However, current text-based models lack the necessary grounding and are orders of magnitude less data-efficient than humans. Real-world, multimodal data is critical for building future models, and robotics offers a promising path: here, the integration of language, vision, memory and manipulation capabilities supports hierarchical planning and lifelong learning.¹⁰

In the long term, AI robots may develop complex self-awareness and seamlessly acquire new skills from rich sensory experience, moving closer to human-like capabilities. Safety boundaries must be set, though. For example, reinforcement- learning strategies that incentivise pure survival should be avoided to ensure that AI acts as a tool aligned with human interests. Some researchers feel that highly abstract reasoning and planning must always be reducible to linguistic or symbolic representations in order to maintain transparency, but there is debate about this. There is also debate about whether LLM-based approaches can generalise to richly structured, non-textual reality such as images and 3D worlds. Overall, embodied learning will be important for credible AI advancement.