One must imagine Sisyphus vibing. | #ethereum | #ai | #gaming

Washington, DC
Joined April 2022
illuminary retweeted
I don't recommend anyone scramble to move their funds to new wallets today. But we should take the risks to cryptography from AI-accelerated math seriously, and minimize our exposure to not just quantum-vulnerable cryptography, but also potentially AI-vulnerable cryptography. The core new area of risk from this viewpoint is, unfortunately, ML-DSA / FHE / lattices. (and it's also another reason, along with quantum, why ECDSA might fall even faster than expected, hence the "fresh address" recommendation) So far most people have been in the mode of thinking "elliptic curves broken, hashes safe, lattices safe". But there is a good chance that the concrete security of lattices will take serious hits from the next two years of AI math. The basic threat model is: factoring is something that naively takes 2^(n/2) time, but over decades smart people have found and optimized number field sieves, and degraded that to 2^O(n^(1/3)), which is why RSA keys and signatures need to be ~400 bytes (and not 64 bytes). What if there are skeletons in the closet like that, both for elliptic curves and lattices, that we are simply not smart enough to discover - but bots soon will be? This is a major part of the reason why for the past year ethereum's lean roadmap has been going in the "hash-only" direction: no lattices, no ML-DSA, no Falcon, no lattice-based commitments inside ZK proofs, etc. Signatures in lean ethereum are all hash-based, either WOTS or SPHINCS-. For signatures and proofs, we already know how to go hash-only. The bigger challenge is for *public-key encryption* - and this goes far beyond blockchains. Secure communication, anonymizing protocols, lots of things need public-key encryption. And unfortunately there are long-standing mathematical theorems showing why public-key encryption cannot be done with hashes alone. You have to have some kind of trapdoor object that has at least one form of usable "structure" - either group theory (incl. isogenies) or lattices or code-based or potentially in the future even more newfangled and spooky things (local mixing?). But for anything that has structure, you should assume that AI will make at least some progress in breaking that structure. Here, one reasonable inference is that if you want to make something plausibly long-term secure, multiply the key sizes by 10. To me that's a very plausible world and something not at all extreme to predict. If AI will bring us 50 years of math in 2 years, then that 50 years of math may very plausibly include a "naive factoring -> GNFS" level of improvement to our ability to break lattices. In that world, lattices will still exist, but they will have to be significantly bigger to guarantee the same level of safety. And at those new larger sizes, hash-based constructions will beat lattice-based constructions on concrete efficiency in every use case where hash-based constructions are possible at all. Theoretically, of course it's possible that hashes are broken too (eg. P = NP would imply that). But I think P = NP is very unlikely. And intuitively, it's much more likely that a mathematical object has exactly no exploitable structure (like hashes are intended to), than that a mathematical object has exactly ~3 forms of exploitable structure (for elliptic curves: associativity, Schoof, pairings) and not some secret fourth form of structure we have not yet discovered that greatly degrades its security (for elliptic curves, ECDLP and pairing security). Similar for LWE, SVP, RLWE and the zoo of lattice problems. For this reason, we do not yet see any reason to worry and start padding the byte size of hashes (if we start to worry more, we would pad the round count first before doing anything to the byte size). Concrete TLDR, my own personal views: * Hash-based > lattice-based, in those situations where hash-based is possible at all * For anything lattice-based, be much more paranoid on param sizes. Remember that blockchains are only a small portion of the cryptography story; this point goes far beyond blockchains and applies to eg. access to websites, secure messaging, Tor / VPNs ... * For privacy protocols, strongly favor NOT putting encrypted notes onchain. Instead, send them offchain through some third-party mechanism. * If it's not difficult for you, keeping your funds in addresses which have not yet been used to make a transaction is a good idea. If it's easy for you, do it. **But be careful about migrations; I personally have lost more money in botched migrations than I have lost in all hacks combined**. * For multisig wallets, doing confirmations offchain is better than onchain, because this way the signatures of signer wallets do not get exposed to the public, so if ECDSA falls to AI much faster than expected, at least the multisig "gracefully degrades" to a 1-of-1 where the 1 is whoever was gathering the signatures - a much better place to be than "anyone can take the money" firefly.social/post/x/210783…
1,247
1,906
890
11,204
3,904,670
illuminary retweeted
there’s so much confusion about what’s happening in AI because if you don’t use coding models every day it’s hard to understand how quickly progress is accelerating but if you do, you likely have AI psychosis to a degree that it’s impossible to explain it in an understandable way
340
605
111
10,149
466,302
illuminary retweeted
In all seriousness, this is a startling achievement for GPT-6 Astra. kenforthewin.github.io/blog/… (This is GPT-6 Astra beating Nethack on its 3rd try. Nethack is the original roguelike and one of the most famously hard games of all time. I have played a lot, and I've never ascended)
50
66
19
1,053
98,762
illuminary retweeted
First LLM NetHack ascension?
2
1
4
142
illuminary retweeted
i trained the fly to play Super Smash Bros Melee
48
352
31
4,359
188,886
illuminary retweeted
I cannot stand when people like Hunter Biden come and make a mockery of our industry and siphon liquidity and mindshare from serious projects like boner and fatcoin and bike tyson
75
177
15
2,195
74,437
illuminary retweeted
Will GPT-6 Astra be the first model to beat NetHack? It's gotten significantly further than any model I've seen ..
1
1
1
75
My feed is entirely micro ducks and omarchy and I’m totally ok with that
1
63
illuminary retweeted
BIG ANNOUNCEMENT FROM HUGGING FACE TODAY: We're unveiling Microduck 🐥🤖 It's a tiny $399 open-source robot you can teach new tricks with reinforcement learning. It can walk, pick things up, get back up when it falls, and even roller-skate. Welcome to the era of open-source affordable robots to democratize physical AI and world models! 🤗🤗🤗
627
972
427
11,959
4,580,865
illuminary retweeted
Replying to @MTSlive
Agent A : extend the module with new features Agent B : delete the module for optimization Agent A :
1
2
76
5,765
illuminary retweeted
114
2,921
38
63,241
1,910,998
illuminary retweeted
86
1,178
63
35,141
1,143,791
illuminary retweeted
zoomers watching their boomer coworker use his brain to formulate an original thought from scratch during a claude outage
🤝 Paid partnership
JUST IN: 40% of US employees reportedly say AI is making them "less intelligent"
143
3,720
124
84,704
3,764,270
I think this is the most important concept to understand right now: LLMs can’t jump. OpenAI says an internal version of Astra, its next major model, has solved ten major open problems in mathematics and theoretical computer science. This is extraordinary. And I don’t use this word lightly. During my PhD in mathematics, I worked on a problem closely related to Gromov’s conjecture. For decades, constructing a non-sofic group—an infinite group whose finite pieces cannot be approximated by finite groups—was a dream shared by many of us. Now Astra appears to have done it. This is not a toy result. This is serious mathematics. But it is not “the most significant day in the history of mathematics”, as some have claimed. This claim confuses scale with kind. Astra has solved difficult problems inside existing conceptual worlds. Calculus, topology, and scheme theory created new conceptual worlds. They did not merely answer questions. They changed which questions mathematics could ask. Induction finds patterns. LLMs are extraordinary at it. Deduction follows implications. Using symbolic AI tools, LLMs are becoming formidable at traversing chains of logic that humans missed, abandoned, or could never afford to search. But abduction is different. Abduction invents the right concept, the right representation, the right question. That is the jump. Current LLMs can fill the gaps of knowledge left by humans. But they can’t jump beyond the external boundary of existing knowledge. * Full paper in the first reply
222
397
128
3,095
451,773
illuminary retweeted
this model saturates the will smith eating spaghetti benchmark so hard, people are making spaghetti eat will smith instead (which is also saturated) source: v.redd.it/6elfdqs9k3hh1
18
153
30
1,916
328,196
illuminary retweeted
Unbelievably embarrassing for Google that Gemini hasn’t committed any cybercrimes yet
261
1,148
149
18,124
832,790
illuminary retweeted
i think it's time to consider turning off memory for these tools
132
125
43
8,640
732,853
illuminary retweeted
Ender's game, but it's Claude who learns at the end that all of the hacking was real
32
173
8
2,936
76,519