Continuous Learning and the Evolution of Mental Models

Continuous learning involves being open to missing crucial evidence, such as limited research releases from DEI labs hindering insights into building AGI. The absence of valuable publications has led to assumptions about shortened timelines due to unknown factors. The speaker, a podcaster, acknowledges limitations in understanding certain topics, impacting scaling implications. The concept of grokking, akin to human learning processes, involves evolving mental models through observation and intuition. Training large models to be overfit on data leads to development of more efficient learning methods. Gradient descent on diverse data fosters the selection of general circuits, culminating in grokking and eventual insight-based learning.