The concept of a digital twin in healthcare can simulate individuals to assess the impact of various interventions, aiding both personal health and pharmaceutical research.

By training neural networks in diverse simulated environments, we can prepare them to handle real-world scenarios, effectively creating a digital twin of the robot and its environment, as demonstrated by humanoid robots that trained for 10 years in just two hours of simulation time to learn walking, showcasing the efficiency of simulation in robotics training.

The concept of a 'digital cousin' refers to simulations that are not perfect replicas but capture enough detail to be useful, running slower than real-time simulations.

Digital twins can assist the pharmaceutical industry by creating virtual cohorts for clinical trials, reducing the need for large participant numbers while still ensuring safety and efficacy.

To train robots effectively, we must simulate environments at 10,000 times faster than real-time and vary parameters like gravity and friction to ensure adaptability.

By running 10,000 simulations in parallel, we can transfer training to real robots without any fine-tuning, showcasing the power of simulation in robotics.

In simulation, we can replay trajectories and vary motions without needing to demonstrate them repeatedly with a real robot, enhancing efficiency in training.

For robotics to work effectively, it needs to understand the physical environment around it, which is a significant challenge that has historically held back progress.

By prompting the model with different language, we can simulate counterfactuals and complex scenes, allowing for the generation of varied scenarios based on the same initial conditions and enabling a robot to interact with objects as if it were in the real world, even if the hardware doesn't support it.