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Recent advancements in AI have showcased models capable of generating human-like text, audio, and video. However, these models have struggled to transition from the digital realm to the physical world, particularly in applications like self-driving cars. The concept of physical intelligence is emerging, which combines AI's digital capabilities with robotics to navigate real-world environments effectively.
Physical intelligence allows machines to understand and adapt to dynamic situations, making real-time decisions based on physical principles. Research at MIT is pioneering this field with liquid networks, which demonstrate superior adaptability compared to traditional AI models. Companies like Covariant are also making strides in robotics, developing systems that can execute complex tasks in real-world settings.
• AI is evolving from digital to physical intelligence for real-world applications.
• Liquid networks show adaptability beyond traditional AI models in dynamic environments.
Physical intelligence refers to AI systems that can understand and interact with real-world environments.
Liquid networks are AI models that continue to learn and adapt from experiences, similar to human learning.
Reinforcement learning is a method where AI learns to make decisions through trial and error in dynamic environments.
Covariant develops AI-driven robotic systems capable of executing complex tasks in warehouses.
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