Chapter
The Role of Simulation in Machine Learning
1:05:25 - 1:13:04 (07:39)
Simulation is a useful tool in machine learning, but in the long run, machines that learn from real data will improve perpetually, as relying solely on simulated data can create a bottleneck. Challenges in reinforcement learning, such as the need to create realistic simulations, become more apparent when running programs in the real world.
Clips
While simulations can be a useful tool for machine learning, relying solely on simulated data can hinder perpetual improvement, making it necessary to eventually incorporate real data to account for real-world limitations and challenges.
1:05:25 - 1:13:04 (07:39)
Machine Learning
Summary
While simulations can be a useful tool for machine learning, relying solely on simulated data can hinder perpetual improvement, making it necessary to eventually incorporate real data to account for real-world limitations and challenges. This is particularly important for developing reliable systems and addressing issues that may not arise in purely simulated environments.