Explore how thinking of AI as an Associative Memory task simplifies and generalizes many of the desirable aspects of modern Machine Learning.

A demo created with Pluto.jl.

Math and inspiration taken from the Hopfield Networks is All You Need Blog

To use this interactive notebook, you'll have to use Julia >= 1.5. Thankfully, this language is rapidly growing in popularity and is designed to be simple to code in for scientists and mathematicians.

A 6 min video on how to do this here

1. Download the latest version of Julia. Follow the default instructions for MacOS
2. Open the newly installed julia-1.5.x. This should open a terminal with a julia instance.
3. Install Pluto.jl. Follow the instructions on that repo, or below:
   • Press the [ key in the terminal. You are now in the package environment.
   • Type add Pluto. This will take a moment to download.
   • Backspace out of the package manager
   • import Pluto
   • Pluto.run()

This will open a Jupyter-like interface that will allow you to browse to notebook.jl.

Julia has a longer start up time and is slow the first time you run a cell. This is because the code you write is immediately compiled, and this allows it to run at near C-speeds which is important for the interactivity.

The environment is self contained in the notebook. It will take a long time to startup.

If this is the first time you are running the notebook, it is possible that Julia is trying to download MNIST and is asking you for a prompt on the command line. Unfortunately, Pluto has an issue interpreting the STDIN in the workers.

To fix this, exit Pluto, and run the following within Julia:

import Pkg; Pkg.add("MLDatasets");
using MLDatasets
MNIST.traindata()

Start pluto again