A call for intrinsic learning

Andy Kitchen

Keywords: Intrinsic learning, Neuroscience, DishBrain, Hebbian

TL;DR: Natural intelligence learns intrinsically, AI currently overuses extrinsic training algorithms

Abstract:

Current artificial intelligence systems predominantly rely on extrinsic
learning mechanisms, with gradient descent and its variants serving as
the primary means of model optimization. This approach treats learning as
a distinct, external process separate from cognition. However, natural
intelligent systems, such as the human brain, display intrinsic learning
where learning and cognition are inseparable, integrated processes.
We argue for a shift of focus toward intrinsic learning in AI
systems, moving away from the heavy reliance on extrinsic optimization. We
highlight the limitations of current AI methods, including their extreme
sample inefficiency and dependence on vast amounts of human-generated
data. By examining the shortcomings of current scaling approaches and
proposing alternative pathways, we emphasize that genuine advancements in
artificial general intelligence require systems that learn and adapt
intrinsically. We encourage renewed attention to AI architectures
that embed learning within the dynamics of the system itself, drawing
inspiration from natural intelligence to foster more robust, efficient,
and adaptive AI.

Submission Number: 111