Curated. I feel like I've heard the idea of tracking GPU compute over the years, but always in the high-level abstract, with no one actually thinking hard about what it would look like and how feasible it is. I'm very in favor of people trying to flesh out proposals into something that can be discussed concretely, so kudos to this author.

There are some serious issues that need to be overcome for any scheme of this nature to be secure.

1. Chips are not tamper-proof black boxes. Secure computing enclaves like SGX are routinely broken by academics: https://arstechnica.com/information-technology/2022/08/architectural-bug-in-some-intel-cpus-is-more-bad-news-for-sgx-users/. Secure flash memory is expensive and tampering with the memory writing logic or hardware could allow an actor to write false logs. Cryptographically signing logs runs the risk of leaking key material through power or fault-injection side channels. Once a chip is physically in the possession of someone with university-lab-level equipment and expertise, all bets are off w.r.t. on-chip controls. While some of these exploits are hard to carry out at scale, even a single effective exploit renders whole generations of GPUs uncontrollable.
2. The author does not actually propose a "Proof of Training Transcript" protocol, only providing a possible definition of such a scheme. While they acknowledge the challenges in constructing such a scheme, I think it is worth highlighting the fact that constructing a secure, efficient instantiation of such a scheme is not something that we (as a species) know how to do. The best noninteractive proofs for arbitrary verifiable computation currently require several orders of magnitude more time to generate the proof than to carry out the original computation and proof generation is not totally parallelizable. The requirement to generate PoTTs basically obviates the utility of using a fast GPU in the first place. It is possible to imagine a special-purpose proof scheme for gradient descent with faster concrete efficiency, but the vague outline of a scheme proposed by the author relies on a stream of nonstandard hardness assumptions that I find very unconvincing. 

I think that from a quick read of the paper or from the summary in the post one might be led to believe that such a scheme could be implemented with a few years of engineering effort and the cooperation of chip manufacturers. In fact, substantial advances in cryptography would be required.

Policy-makers' attempts to enforce policy by requiring the use of special chips have in the past largely been broken: Clipper chip, DRM via SGX, etc.

When I first saw "save all weights to on chip hardware", I thought it would be super expensive, but actually saving like 5 times the GPU's memory to a seperate flash chip would only cost $20 (80GB*5 at 5 cents per gigabyte for flash storage). It can be way cheaper bc it's low bandwidth and slow.

It feels like the implicit message here is "And therefore we might coordinate around an alignment solution where all major actors agree to only train NNs that respect certain rules", which... really doesn't seem realistic, for a million reasons?

Like, even assuming major powers can agree to an "AI non-proliferation treaty" with specific metrics, individual people could still bypass the treaty with decentralized GPU networks. Rogue countries could buy enough GPUs to train an AGI, disable the verification hardware and go "What are you gonna do, invade us?", under the assumption that going to war over AI safety is not going to be politically palatable. Companies could technically respect the agreed-upon rules but violate the spirit in ways that can't be detected by automated hardware. Or they could train a perfectly-aligned AI on compliant hardware, then fine-tune it in non-aligned ways on non-compliant hardware for a fraction of the initial cost.

Anyway, my point is: any analysis of a "restrict all compute everywhere" strategy should start by examining what it actually looks like to implement that strategy, what the political incentives are, and how resistant that strategy will be to everyone on the internet trying to break it.

It feels like the author or this paper haven't even begun to do that work.

The LessWrong Review [? · GW] runs every year to select the posts that have most stood the test of time. This post is not yet eligible for review, but will be at the end of 2024. The top fifty or so posts are featured prominently on the site throughout the year.

Hopefully, the review is better than karma at judging enduring value. If we have accurate prediction markets on the review results, maybe we can have better incentives on LessWrong today. Will this post make the top fifty?

Isn't this akin to a protocol for securely monitoring private industry's experiments in thermonuclear weapons? It's better than nothing, but when something is dangerous enough, industrial regulation is never strict enough.

Some things are too dangerous to allow private competition in. The only sensible thing to do is nationalize them and have them run exclusively by extremely security-minded government agencies, if at all. And even that might not be good enough for AI, because we've never had tech whose base case scenario was "kill everyone".

The provable training part seems achievable using the existing cryptography and without requiring Confidential Computing techniques. 

For example, by committing to weights at the snapshot point of time using a Hash function and also generating proof that, indeed, at least amount of T has passed between each snapshot using Verifiable Delay Functions (VDFs), we can already achieve a rate limit at how quick someone can train a model. In addition to that, to prove that we have been consistent and the next weight checkpoint was correctly produced by applying X training operations from the previous checkpoint, we can use zk-SNARKs to generate a succinct proof. As this is essentially a continuous process of proving computations, we can also apply ideas from incrementally verifiable computations (IVC), making the scheme even more efficient. I must note that SNARKs still need to be sufficiently efficient to prove extensive computations, such as training of neural networks such as GPT-4, but there are already examples of using them to prove training of GPT-2. Furthermore, as the whole scheme can be made Zero Knowledge, we can have the prover proactively submit the proofs to the verifier to ensure they are consistently compliant.

To ensure that someone cannot ignore the regulation and do training hidden, we would need to require any capable training hardware to be actively registered with the officials.  Requiring an active signature from a government server to enable doing X computations could be a solution. This should be achievable as SGX and similar providers have only incremental counters or alternative solutions. Furthermore, requesting computation integrity is a far less strict property on the SGX hardware than attestation, as it does not depend on crucial extraction attacks and would require actual chip-level alterations. Considering we are already using a lot of similar gadgets around ourselves, supported by ARM TEE, this should be achievable.