# Projects

# Understanding the free energy principle

- The free energy principle is a theory developed by Karl Friston and others to explain how biological systems tend to avoid disorder by limiting themselves to a small number of favorable states.
- Until recently, the Free Energy Principle has been a constant source of mockery from neuroscientists who misunderstood it and so I hope that by growing a collection of free-energy motivated reinforcement learning examples on Github we may finally have a constructive discussion between scientists.
- Regarding mockery, there is even a satirical Karl Friston account on Twitter.

**code**: Free Energy Experiments

**blog post**: Understanding the free energy principle

# Is Causal Path Entropy an E=mc^2 for intelligence?

- The Causal Entropic Force framework proposes that organisms make decisions which maximise their future optionality.
- Alex Wissner-Gross, the main author of this theory thinks that this is the closest thing to an E=mc^2 for intelligence.
- This repository is designed to help deconstruct this claim by analysing simple problems which provide insight into the limitations of Causal Entropic Forces.

**code**: Is Causal Path Entropy an E=mc^2 for intelligence?

**blog post**: Approximating Causal Path Entropy in Euclidean spaces

# Fractals with TensorFlow:

- Right now, this repository is partly used to explore the symmetries of Mandelbrot variants.
- In the near future, I’ll add a GPU optimised version for investigating quaternion fractals.
- I wrote an introductory blog post for this repository.

**code**: Fractals with TensorFlow

**blog post**: Fractals with TensorFlow

# Deep Rectifiers:

- A set of tools for analysing deep fully-connected recitifer networks i.e. networks with ReLU activation.
- Specific tools include methods for analysing optimisation, variable disentangling, variable size representation, sparsity, and generalisation.
- The ultimate goal is to develop useful mathematical models for deep rectifier networks, a model for supervised learning that is not yet well-understood.

**code**: deep_rectifiers

**blog post**: deep rectifier networks: preliminary observations