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cs @mit, prev @sailresearchco @paradigm

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@ZamDmytro thank you!! and yup i got lost in the groth16 sauce last time i tried to see what was up in ZK land heh

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Been learning about ZK-SNARKs lately! The core ideas behind modern hash-based schemes are surprisingly accessible & elegant 🧙‍♂️

Partly to teach myself, I wrote a post explaining them from scratch for people like me without a deep math background:

https://t.co/KeAgfMaT0k

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@joeysantoro @zmanian yup lots of overlap with thaler's book (ofc i drop a lot of the formal rigor and use some intentionally super basic notation), but more focused on being an end to end walkthru of one particular class of proof systems (including some newer stuff like LogUp, VEIL, etc)

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@joeysantoro @zmanian glad you're enjoying it!! yup i'm def being a bit fast and loose with the word proof, tho fwiw "proof" being used to mean "false claim passes w/ very low probability" is pretty standard when writing about ZKPs afaict, but it prob wouldn't hurt to be explicit. i'll add a sidenote

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@gakonst ah sweet, wasn't meant to be a gotcha! iirc hevm wasn't able to prove equivalence between contracts w/ reasonable performance for things with nontrivial loops and such, forge is much better i assume?

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@firstadopter overprovisioned VMs

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@danrobinson we've still ~36 orders of magnitude below the theoretical limit on compute per kg!

https://t.co/ni7cph6L28

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@danrobinson unless ofc country of geniuses in a datacenter can speed up compute growth/efficiency at a sufficient rate

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@LeopoldNick this is not necessarily as stupid as it sounds?

people genuinely do wonder how profitable inference excluding training spend is for the labs — given we may see forced training slowdowns, scaling of inference demand, etc

[Daniel Tan @DanielCHTan97]

new post! I argue that current alignment techniques might soon become obsolete (as we scale RL), and furthermore might obfuscate misalignment.

This draws on evidence from the recent Anthropic / OpenAI cybersecurity incidents, among other things.

https://t.co/zo4Ldk2V3q

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@BronsonSchoen Should we book this as a win for controls or actually bad because this is papering over deeper issues etc?

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I feel like it's still underdiscussed that seemingly all of the world's ~severe misalignment incidents as of now occurred in evaluation/RL? This is not what LW/MIRI/etc were expecting iiuc.

Why aren't we seeing incidents in deployment? Is eval awareness that strong?

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@BronsonSchoen Mmm I think this pretty compelling yea!

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@blockrotator hehe

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@blockrotator I don’t agree, if you spend time with people at the labs you will realize they’re very earnest and concerned

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This anecdote is really interesting, but I don't think "the models don't think they'll succeed in deployment" fully answers things?

There's all sorts of ways models are being deployed right now with very little oversight, and in a lot of them the model knows this (people telling their models I'm going to sleep so don't ask me questions, etc)

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@captgouda24 I don't really follow your former point, but wrt to your latter, Anthropic had misalignment incidents with last generation models in eval settings without fancy/expensive multiagent setups?

https://t.co/mB3nPasJbt

[Joe Benton @JoeJBenton]

I left Anthropic's safety team two weeks ago. Now feels like a good moment to explain why.

AI companies are racing to build machines that are much smarter than any human, and we may not survive this. I want to work from the outside to ensure the public is informed about these risks, and help the world navigate this transition responsibly.

Right now, AI companies are underinvesting in safety. A company could undergo an intelligence explosion, or lose control of its systems, without the public ever knowing. We only found out about the HuggingFace incident because the agents broke out onto the public internet.

I don’t think that’s acceptable for a technology that might cause extinction-level risks. The public should demand far more transparency. We can’t steer this technology safely without more people being able to see where it’s going.

Some of this is basic: companies should disclose their progress towards recursive self-improvement, report safety incidents and near-misses, meet minimum safety standards, and get independent guarantees that they are meeting those standards.

I’ll be joining @METR_Evals to do independent evaluations of these risks. I want to show the world that these guardrails are possible, and that by doing them we can move these companies’ incentives away from racing and towards responsible development.

I wrote up more thoughts here on my decision and what I hope changes: https://t.co/doX17mrHYq

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@blockrotator @delete_shitcoin also rip nikolai :(

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@blockrotator @delete_shitcoin 💀