The current artificial intelligence landscape primarily focuses on generative models and large language models, but these approaches violate principles of natural intelligence. This leads to limitations in efficiency, explainability, and trustworthiness. Alternatives, such as the use of principles grounded in biology and physics, may better align with achieving artificial general intelligence. Neuromorphic chips are part of the consideration in the ongoing question of whether to prioritize high-performance computing or to explore natural intelligence principles.
Natural intelligence is fundamentally grounded in the principles of physics and biology, unlike artificial intelligence which often does not adhere to these foundational laws.
Current models of generative AI face limitations in efficiency, explainability, and trustworthiness, which hinder their ability to achieve true artificial general intelligence.
Collection
[
|
...
]