*This is one of my final projects for the **Columbia EA Summer 2022 Project Based AI Safety Reading Group** (special thanks to facilitators Rohan Subramini and Gabe Mukobi). If you're curious you can find my other project **here**.*

**Summary**

In this project, I:

- Derive by hand the optimal configurations (architecture and weights) of "vanilla" neural networks (multilayer perceptrons; ReLU activations) that implement basic mathematical functions (e.g. absolute value, minimum of two numbers, etc.)
- Identify "features" and "circuits" of these networks that are reused repeatedly across networks modeling different mathematical functions
- Verify these theoretical results empirically (in code)

What follows is a brief introduction to this work. For full details, please see:

- The linked video (also embedded at the bottom of this post)
- Or if you prefer to go at your own pace, the slides I walk through in that video

**Motivation**

Olah et al. make three claims about the fundamental interpretability of neural networks:

They demonstrate these claims in the context of image models:

**Features / Circuits:**

**Universality:**

This work demonstrates the same concepts apply in the space of neural networks modeling basic mathematical functions.

**Results**

Specifically, I show that the optimal network for calculating the minimum of two arbitrary numbers is fully constructed from smaller "features" and "circuits" used across even simpler mathematical functions. Along the way, I explore:

- "Positiveness" and "Negativeness" Detectors
- Identity Circuits (i.e. f(x) = x)
- Negative Identity Circuits (i.e. f(x) = -x)
- Subtraction Circuits (i.e. f(x1, x2) = x1 - x2)
- "Greaterness" Detectors
- And More

**Minimum Network:**

I also demonstrate that each of these theoretical results hold in practice. The code for these experiments can be found on the GitHub page for this project.

**Full Details**

For full details, please see the PDF presentation in the GitHub repo or watch the full video walkthrough:

Given that you have just published this on the forum, I have not yet finished watching the video, but it is playing in the background on 1.5x speed.

Your project is valuable to me since I am not up-to-date with my knowledge of the state of interpretability research and suspect that your project and manner of explanations will help slightly in this regard. Beyond the value, interpretability is simply interesting. I would very likely watch more video explanations of this nature on topics in AI Safety, interpretability, alignment, etc... which leads me to my question: Do you intend to continue to upload videos like the one you've uploaded today?

I really wish more EAs included video explanations / tutorials to supplement their work.

Thank you for posting this on the forum, and especially for creating the video.

Thanks for the comment and for watching! I don't currently have any future videos planned, but I'd definitely consider it if there's interest. I'm also a fan of learning via videos, and you're right that there aren't that many in the AI Safety space. (Robert Miles is the only AI Safety YouTuber, I'm aware of. Absolutely worth checking out if your interested in this kind of stuff.)