Thing in itself

Karl Friston on the FEP, cognition, life, agency, enactivism

Oct 27, 2022

01:37:32

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Highlights

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Episode notes

The Limits of Self-Evidancing in Machine Learning

02:52

Um Self-Organizing Predictive Predictional Processing and Machine Learning

02:54

The Best Plan Is That Which Minimizes the Surprise Expected

02:49

Introduction

3min

Predictive Processing Is a Concept of Sensitivity and Sensing

4min

The Action Perception Cycle

3min

The Free Energy Principle

2min

The Imperatives for Good Predictive Processing

5min

In Machine Learning You Want to Minimize the Free Energy

2min

The Maximum Entropy Principle Is the Free Energy Principle

3min

The Free Energy Principle

5min

The Imperative to Minimizing Surprise or Expected Free Energy

5min

10.

So Radical in Activism in a Nuanced Way?

6min

11.

Evolution Is an Example of the Free Energy Principle and It's an Example of the Adaptive Fitness Maximization

4min

12.

The Free Energy Principle in Machine Learning Is Applied to Scaling

4min

13.

The Free Energy Principle in Application

3min

14.

Is There a Markov Blank?

5min

15.

Theology Principles in Engineering - Partitioning the World

3min

16.

The Evolution Stable State of Predictive Processing in a Minimal Environment

4min

17.

The Best Plan Is That Which Minimizes the Surprise Expected

3min

18.

Is There an Opportunity to Be Curious?

4min

19.

The Pragmatic Affordance

4min

20.

The Ability to Plan

2min

21.

Is There a Bright Line Between Different Kinds of Things?

5min

22.

The Limits of Self-Evidancing in Machine Learning

3min

23.

The Meta Problem

5min

24.

How to Maximize the Marginal Probability of All of Your Sensations

3min

25.

The Idea of Extended Cognitive Capacity

3min

26.

Um Self-Organizing Predictive Predictional Processing and Machine Learning

3min

27.

The Process of Scientific Inquiry

2min

Karl Friston is a theoretical neuroscientist at University College London, authority on brain imaging, and proponent of the free energy principle.

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