The field of robotics and AI relies on established technologies such as file systems, virtual memory, and caching. However, building physically intelligent machines for the real world presents complex challenges such as dealing with rough terrain and vegetation.
The challenge of making autonomous driving and robotic products more economical is not just about solving the basic problems of driving, computer vision, and control, but also about creating a product that is affordable for consumers with economy of scale. However, the idea of robotics being a trillion-dollar service industry is still not clear.
Rocket Lab's CEO, Peter Beck, explains how they use machine learning to improve the materials and cost efficiency of rocketry, similar to how Moore's law has advanced over time.
The field of robotics is split between perception and decision-making, and while there is a general understanding that worst-case scenarios can be difficult, it is baffling how little is truly known about the field and its underlying mathematics.
This podcast episode helps to understand artificial intelligence and how it differs from machine learning and robotics. It also explains neural networks and its working based on the structure of the human brain.
This episode delves into the different terms and words used in the field of artificial intelligence, including the concept of artificial general intelligence, and the debate on whether to use the term "human level intelligence." The discussion also touches on the separation of cognition and perception and whether they are truly distinct.