Trace

Tom McGrath

tommcgrath.github.io · individual · Manifund

Technical AI safety research

Received
$4,025
from 2 funders
Grants
2
Funders
2
Years active
2024

Grants over time

$0$2K$4K$6K2024
Neel NandaLun

Biggest funders

Neel Nanda$4,00099%1
Lun$251%1

Biggest recipients

AI Safety Camp$48100%1

Reviews

Neel Nanda · Manifund · full post · May 9, 2024

Train great open-source sparse autoencoders, May 2024

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**Main points in favor of this grant**

I think that determining the best training setup for SAEs seems like a highly valuable thing to do. Lots of new ideas are arising about how to train these things well (eg Gated SAEs, Prolu, Anthropic's April update), with wildly varying amounts of rigour behind them, and often little effort put into replicating them and seeing how they combine. Having a rigorous and careful effort doing this seems of significant value to the mech interp community.

Tom is a strong researcher, though hasn't worked on SAEs before, I thought the Hydra Effect and Understanding AlphaZero were solid papers. Joseph is also solid and has a lot of experience with SAEs. I expect them to be a good team.

**Donor's main reservations**

The Google DeepMind mech interp team has been looking somewhat into how to combine the Anthropic April Update methods and Gated SAEs, and also hopes to open source SAEs at some point, which creates some concerns for duplicated work. As a result, I'm less excited about significant investment into open source SAEs, though having some out (especially soon!) would be nice.

This is an engineering heavy project, and I don't know too much about Tom's engineering skills, though I don't have any reason to think they're bad.

**Process for deciding amount**

As above, I'm less excited about significant investment into open source SAEs, which is the main reason I haven't funded the full amount. $4K is a fairly small grant, so I haven't thought too hard about exactly how much compute this should reasonably take. If the training methods exploration turns out to take much more compute than expected, I'd be happy to increase it.

**Conflicts of interest**

Please disclose e.g. any romantic, professional, financial, housemate, or familial relationships you have with the grant recipient(s).

Tom and I somewhat overlapped at DeepMind, but never directly worked together.

Joseph is one of my MATS alumni, and currently doing my MATS extension program. I consider this more of a conflict of interest, but my understanding is that Tom is predominantly driving this project, with Joseph helping out where he can.

I expect my MATS scholars to benefit from good open source SAEs existing and for both my scholars and the GDM team to benefit from better knowledge on training SAEs, but in the same way that the whole mech interp ecosystem benefits.

Grants received

Date ↓FunderViaAmountCauseSourcePurpose
Aug 29, 2024LunManifund$25InterpretabilitymanifundTrain great open-source sparse autoencoders
May 10, 2024Neel NandaManifund$4,000InterpretabilitymanifundTrain great open-source sparse autoencoders

Grants made 1 · $48 · $48 average

Date ↓RecipientViaAmountCauseSourcePurpose
Jun 7, 2018AI Safety Camp$48Training pipelinesvipul_donationsThe actual donation probably happened sometime between February and June 2018.