This jam is now over. It ran from 2023-01-20 16:00:00 to 2023-01-23 03:15:00. View results

We're currently live! To participate, use this report template to submit your projects to this page. Check out the live stream from Friday as well.

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Machine learning is becoming an increasingly important part of our lives and researchers are still working to understand how neural networks represent the world.

Mechanistic interpretability is a field focused on reverse-engineering neural networks. This can both be how Transformers do a very specific task and how models suddenly improve. Check out our speaker Neel Nanda's 200+ research ideas in mechanistic interpretability.


Alignment Jam hackathons

Join us in this iteration of the Alignment Jam research hackathons to spend 48 hour with fellow engaged researchers and engineers in machine learning on engaging in this exciting and fast-moving field!

Join the Discord where all communication will happen. Check out research project ideas for inspiration and the in-depth starter resources.


Using mechanistic interpretability, we will dive deep into how neural networks think and do. We work towards reverse-engineering the information processing of artificial intelligence!

We provide you with the best starter templates that you can work from so you can focus on creating interesting research instead of browsing Stack Overflow. Check the resources out here. You're very welcome to check out some of the ideas already posted!  

Where can I join?

Schedule

The schedule runs from 5PM CET / 8AM PST Friday to 7PM CET / 10AM PST Sunday. We start with an introductory talk and end with an awards ceremony. Join the public ICal here.

Local groups

If you are part of a local machine learning or AI safety group, you are very welcome to set up a local in-person site to work together with people on this hackathon! We will have several across the world (list upcoming) and hope to increase the amount of local spots. Sign up to run a jam site here.

You will work in groups of 2-6 people within our hackathon GatherTown and in the in-person event hubs.

Judging criteria

Everyone who submits projects will review each others' projects on the following criteria from one to five stars. Informed by these, the judges will then select the top 4 projects as winners!

ML SafetyHow good are your arguments for how this result informs alignment and understanding of neural networks? How informative are the results for the field of ML and AI safety in general?
Mechanistic InterpretabilityHow informative is it in the field of mechanistic interpretability? Have you come up with a new method or found completely new results?
NoveltyHave the results not been seen before and are they surprising compared to what we expect?
GeneralityDo your research results show a generalization of your hypothesis? E.g. if you expect language models to overvalue evidence in the prompt compared to in its training data, do you test more than just one or two different prompts and do proper interpretability analysis of the network?
ReproducibilityAre we able to easily reproduce the research and do we expect the results to reproduce? A high score here might be a high Generality and a well-documented Github repository that reruns all experiments.


Submissions(15)

All submissions
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Maybe (8)
Yes (5)
No (2)
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Israel (1)
Budapest (1)
Oxford (1)
London EA Hub (2)
Online (6)
Copenhagen (1)
Edinburgh (2)

No submissions match your filter

We found a single MLP neuron in GPT2-Large that encodes for the word 'an'
✍️ Compiling RASP code to Transformers and comparing them to trained networks
https://simple-trafo-mech-int.streamlit.app/
We expand Neel Nanda's Interactive Neuroscope to view an entire layer.
Experiment on Iterative Summarisation, potentially linked to Natural Abstraction. Made for the Alignment jam #4.
We found a behavior where a toy SoLU model succeeds as well as a few edge cases where it fails.
Investigating whether BERT's context vectors correspond to human-interpretable linguistic concepts
This report presents different RL to see agent behavior in different models