Information theory may be useful for understanding active inference. As a minimum it offers some alternative perspectives on the quantities used in the active inference theory such as surprise, KL divergence, and entropy. This post provides a very short introduction to information theory.
In this post we will take a deeper look at how, according to the active inference theory, the brain interprets what it observes.
I have set out to gain some insights into the active inference theory that provides a unified framework for perception, learning, decision making, and action. I will share my “lecture notes” combined with my own comments in this and future posts. My focus is on the mathematical models and the necessary algorithms.
I’m worried about irreversible climate change, nuclear war, war on rationality, isolationism, extreme nationalism, intolerance, pandemics, the declining mental health of the young, religious extremism, bioterrorism, and many other things. AI doesn’t make it to my top 10 list. Why?
This blog has been dormant for a few years because of other priorities. Now I again feel the urge to write as a tool for learning and because some recent developments in the world just need to be commented.
Everything that is not forbidden by the laws of nature is achievable, given then right knowledge.
A piece of old but current wisdom from Hávamál.
Today I make a new prediction: the future of transportation is electric.
Who cares about clean air and safe drinking water anyway?
Colleges should consider socioeconomic situation in admission, not race.