This work will elaborate the fundamental principles of physical artificial intelligence (Physical AI) from a scientific and systemic perspective. The aim is to create a theoretical foundation that describes the physical embodiment, sensory perception, ability to act, learning processes, and context sensitivity of intelligent systems within a coherent framework. While classical AI approaches rely on symbolic processing and data driven models, Physical AI understands intelligence as an emergent phenomenon of real interaction between body, environment, and experience. The six fundamentals presented here are embodiment, sensory perception, motor action, learning, autonomy, and context sensitivity, and form the conceptual basis for designing and evaluating physically intelligent systems. Theoretically, it is shown that these six principles do not represent loose functional modules but rather act as a closed control loop in which energy, information, control, and context are in constant interaction. This circular interaction enables a system to generate meaning not from databases, but from physical experience, a paradigm shift that understands intelligence as an physical embodied process. Physical AI understands learning not as parameter adjustment, but as a change in the structural coupling between agents and the environment. To illustrate this, the theoretical model is explained using a practical scenario: An adaptive assistant robot supports patients in a rehabilitation clinic. This example illustrates that physical intelligence does not arise from abstract calculation, but from immediate, embodied experience. It shows how the six fundamentals interact in a real system: embodiment as a prerequisite, perception as input, movement as expression, learning as adaptation, autonomy as regulation, and context as orientation.

Open source language models are crucial to AI innovation. Can open robotics models do the same for physical machines? European roboticists today released a powerful open-source artificial intelligence model that acts as a brain for industrial robots --helping them grasp and manipulate things with new dexterity. The new model, SPEAR-1, was developed by researchers at the Institute for Computer Science, Artificial Intelligence and Technology (INSAIT) in Bulgaria. It may help other researchers and startups build and experiment with smarter hardware for factories and warehouses.

Scalable and generalizable physics-aware deep learning has long been considered a significant challenge with various applications across diverse domains ranging from robotics to molecular dynamics. Central to almost all physical systems are symplectic forms, the geometric backbone that underpins fundamental invariants like energy and momentum. In this work, we introduce a novel deep learning architecture, MetaSym. In particular, MetaSym combines a strong symplectic inductive bias obtained from a symplectic encoder and an autoregressive decoder with meta-attention. This principled design ensures that core physical invariants remain intact while allowing flexible, data-efficient adaptation to system heterogeneities. We benchmark MetaSym on highly varied datasets such as a high-dimensional spring mesh system (Otness et al., 2021), an open quantum system with dissipation and measurement backaction, and robotics-inspired quadrotor dynamics. Our results demonstrate superior performance in modeling dynamics under few-shot adaptation, outperforming state-of-the-art baselines with far larger models.

Recent AI models are surprisingly humanlike in their ability to generate text, audio, and video when prompted. However, so far these algorithms have largely remained relegated to the digital world, rather than the physical, three-dimensional world we live in. In fact, whenever we attempt to apply these models to the real world even the most sophisticated struggle to perform adequately--just think, for instance, of how challenging it has been to develop safe and reliable self-driving cars. While artificially intelligent, not only do these models simply have no grasp of physics but they also often hallucinate, which leads them to make inexplicable mistakes. This story is from the WIRED World in 2025, our annual trends briefing.

On a metallic door in San Francisco's Mission District, a single character--"π"--offers a cryptic clue as to the virtuous circle of labor taking place beyond. The door opens to reveal furious activity involving both humans and machines. A woman uses two joysticks to operate a pair of tabletop robotic arms that carefully lift and fold T-shirts into a neat pile. Several larger robots move pantry items from one cluttered box to another. In one corner of the room a man operates a plastic pincer that fits over his wrist and has a webcam on top.

This paper proposes a specific conceptualization of intelligence as computation. This conceptualization is intended to provide a unified view for all disciplines of intelligence research. Already, it unifies several conceptualizations currently under investigation, including physical, neural, embodied, morphological, and mechanical intelligences. To achieve this, the proposed conceptualization explains the differences among existing views by different computational paradigms, such as digital, analog, mechanical, or morphological computation. Viewing intelligence as a composition of computations from different paradigms, the challenges posed by previous conceptualizations are resolved. Intelligence is hypothesized as a multi-paradigmatic computation relying on specific computational principles. These principles distinguish intelligence from other, non-intelligent computations. The proposed conceptualization implies a multi-disciplinary research agenda that is intended to lead to unified science of intelligence.