This course teaches data analysis, but it focuses on scientific models first. The unfortunate truth about data is that nothing much can be done with it, until we say what caused it. Sixteen lectures. Slides, course notes and programming exercises. github 
Much like we had to grapple with Whitehead's language, we must grapple with Friston's.
In _Active Inference: A Process Theory_ Friston writes:
Each and every one of these methods and tools must be adapted, even transformed, so they understandable and handy for the prospective inhabitants. This translation and all it implies is the work I see in front of us. This can only be done in partnership with neighbors in neighborhoods.
What we are referring to as the semantic network goes by many terms. Jean Piaget borrowed Hegel's term, calling it the schema in his theory of cognitive development known as constructivism . Arthur Koestler called it a Meaning Matrix in his book The Art of Creation . Karl Friston calls it a bayesian belief network in his theory called Active Inference. In essence, all of these terms point to a similar concept.
One of the leading models of how we learn is based on the groundbreaking work Karl Friston, the father of modern neuroscience. It was with the algorithms of his statistical parametric mapping that we began to unlock our ability to use fMRIs to see the mind in the act of thinking. This ability is completely transforming how we understand it – leading to a deeper appreciation of the Whole Mind.
Earlier in this story, we introduced the thinking of four people who might, we hope, illuminate the meaning of this journey at a deeper level. The last person mentioned was Karl Friston, the neuroscientist.
K. Friston, R. Adams, L. Perrinet, and M. Breakspear, “Perceptions as Hypotheses: Saccades as Experiments,” Frontiers in Psychology, vol. 3, p. 151, 2012, doi: 10.3389/fpsyg.2012.00151.
We are beginning to see the story arc that leads us from Whitehead to Friston that informs a deeper understanding of the Agile Mindset.
When people are trying to reach consensus on joint action and there is a divergence of opinion on next steps, bayesian belief network software can help.
Estimate or model probabilities. Use Bayesian Belief Models when it matters enough.
We sense the prime pattern of Lisp in its lambda essence: variable, abstraction and values (applications) one that is bounded and held within whitespace.
Bohm called them moments, moments in which there is emergence from the implicate order to the explicate order. Moments of emergence, quanta moments. Whitehead referred to them as 'actual occasions'.
We sense there there are three dimensions of _learning potential_, what we are calling capacity, domain and rate.
Markov
We allow a monkey to explore the wiki, seeking to learn from it.
In statistics and machine learning, the Markov blanket for a node in a graphical model contains all the variables that shield the node from the rest of the network. This means that the Markov blanket of a node is the only knowledge needed to predict the behavior of that node and its children. The term was coined by Judea Pearl in 1988.
In statistics and machine learning, the __Markov blanket__ for a vertex (graph theory) in a graphical model contains all the variables that shield the node from the rest of the network. This means that the Markov blanket of a node is the only knowledge needed to predict the behavior of that node and its children. The term was coined by Judea Pearl in 1988 - wikipedia
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In mathematics and computer science, a directed acyclic graph (DAG), is a finite directed graph with no directed cycles - wikipedia
Free Energy Principle
Karl Friston’s free energy principle might be the most all-encompassing idea since Charles Darwin’s theory of natural selection. But to understand it, you need to peer inside the mind of Friston himself. wired
Karl Friston’s free energy principle might be the most all-encompassing idea since Charles Darwin’s theory of natural selection. But to understand it, you need to peer inside the mind of Friston himself. wired
Neuroscientists are more deeply learning how we learn, casting new light on what Jean Piaget theorized.
T. Parr and K. J. Friston, “Generalised free energy and active inference,” Biol Cybern, vol. 113, no. 5, pp. 495–513, Dec. 2019, doi: 10.1007/s00422-019-00805-w.
Complexity
The free energy principle uses the following formula to explain free energy minimization in the context of Bayesian inference:
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YOUTUBE BGWGomNjLvA
Roam is an online workspace for organizing and evaluating knowledge. The system is built on a directed graph, which frees it from the constraints of the classic file tree. Users can remix and connect ideas in multiple overlapping hierarchies, with each unit of information becoming a node in a dynamic network. Any given node can occupy multiple positions simultaneously, convey information through defined relationships, and populate changes throughout the graph. With weightings assigned to the strength of relationships between nodes, Roam also becomes a to
NIH Director Letter on Gain of Function
"Symbolic Processing" is a pejorative term used by non- or sub-symbolic practitioners of Artificial Intelligence. It refers to any attempt to create AI using conventional programming language means or at a high level. Symbols are kind of like variables, they can refer to one thing at one moment and another at another. Symbolic processing tends to have variables that hold concepts like 'green, 'red, or #true. The concept of symbolic AI itself only makes sense by being compared to non-symbolic AI. Non-symbolic AI uses numbers to describe statistic
<i>I study artificial intelligence, 'cause I have none of my own.</i>