The digital twin paradigm allows for high-speed simulations, but creating a digital twin requires tedious manual work to build the robot and environment.

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.

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.

Digital computation is superior because it allows for multiple copies of the same model to learn from different experiences and share knowledge efficiently.

I believe that AI can learn effectively through simulation and real-life experiences, and it doesn't necessarily need to mimic human physical forms to be effective.

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.

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.

I believe we need to balance technology with real-life experiences, as the digital realm can be more fun while real life becomes less fulfilling.

The smallest object that can simulate a human is the human itself, and simulating complex systems like weather or human behavior is nearly impossible with conventional computers.