Short-Form Thoughts
The Prison AI Cannot See
Scaling makes models more fluent inside a frame they can never question. Why bigger won't fix it, and a mathematical architecture for machines that monitor their own thinking.
The Autocomplete Delusion
We built machines that talk. We declared them intelligent. We might be wrong about what that word means.
Human Learning Verses General Intelligence
We The podcast discusses the differences between human learning and AI models, highlighting the complexities and limitations of current AI technologies. It also touches on the potential and challenges of AI in edge computing, emphasizing the importance of building efficient, scalable, and business-focused models.
Spotify Apple
A Mathematical Approach to Constraining Neural Abstraction and the Mechanisms Needed to Scale to Higher-Order Cognition
Artificial intelligence has made great strides in the last decade but still falls short of the human brain, the best-known example of intelligence. Not much is known of the neural processes that allow the brain to make the leap to achieve so much from so little beyond its ability to create knowledge structures that can be flexibly and dynamically combined, recombined, and applied in new and novel ways. This paper proposes a mathematical approach using graph theory and spectral graph theory, to hypothesize how to constrain these neural clusters of information based on eigen-relationships. This same hypothesis is hierarchically applied to scale up from the smallest to the largest clusters of knowledge that eventually lead to model building and reasoning.
How AI at the edge offers potential to provide valuable, real-time insights to better navigate complex data and opportunities
More soon...