A very neat problem and result. I often find myself swinging between "It's so over" and "We're so back" - some days I roll out of bed thinking I could have Claude solve some random unproven OEIS sequence before breakfast; other days, I wake up in a cold sweat worried about the fate of humanity and what the world might look like in a decade. I think it's that I don't have a very high p(doom) or p(utopia), and I don't really have any solid conviction on how this whole thing is going to go, so my vibe-o-meter jitters between 'fine' and 'not fine' constantly. It's just such an unpredictable moment. Anyways: really neat to see this use case. I myself recently used Claude to finally do an relatively exhaustive study of the location of heretofore-unlisted formal gardens in Ireland in the early 1800s and early 1900s, by having Claude write the tooling for me to manually annotate a few dozen on tiles of historic maps, and then running some CV model across the rest of the tiles using my input. I'd been planning to do this project for over a decade, but I could never find the time (or the enthusiasm) to learn all the details of how to do it myself. It took me a weekend with Claude and continues to bring me joy.
> Caveats, stated plainly. [from the Fable transcript pasted in the article]
You should have seen the discussion of this on the Schneier blog a few days ago.
Someone had their agent check the solution, presumably it emailed a librarian to check that it was correct for the original edition. Then their comments read like "The BL/EEBO witness lacks it, so the discrepancy is copy-specific, not a disproof of the cipher." and "A complete 285-coordinate physical replication is still pending."
> The run baseline was captured without a physical MAC; the current device is not durably bound to it.
> Engineering mode confirmation is the ESPHome component read-back; the LD2410 UART acknowledgement is not observed, so this is not proof the radar itself applied the sensitivity change.
It was only when native English speakers—or those I presumed were—started calling out how bad "GPT/Claude speak" has become that I realized I wasn't actually losing my grip on English as a second language. For a second, I thought, Oh, I learned this language on my own, but it seems I've hit a wall and need to study further. It didn't help that I've also been trying to acquire Swedish as a third language for a while now.
Not the original commenter, but I did this in all of them.
Their skills formats are basically identical, so I setup simlinks from their own skills directories into a shared one so Claude, Codex, Cursor, and anything else that comes out will all read and write to the same shared skills.
It's great having access to the same skills no matter the harness being used
I long for the day when AI will just say that directly: "your soldering sucks man" instead of the bizarre made up and jargon packed language they use now.
Sometimes when I get frustrated reading Opus/Fable 5+ output I pause my rage out briefly to wonder if it's because I'm just too dumb for the model or if the model is just terrible at English.
I'm not sure that telling it to "try explaining that again, simply and briefly" is helping my ego.
It's often simply misleading / bad writing. Here's one I just got about some crashes:
"If the crashes stop, the factory overclock is marginal; run a small negative offset."
This looks like it's saying: "If the crashes stop then we know the factory overclock is marginal." (This makes no sense.)
What it's trying to say is: "If the crashes stop then we can run a small negative offset, because the factory overlock is marginal."
What I would write: "If the crashes stop, we can avoid crashes by underclocking slightly. The speed difference between that and factory clock is marginal."
I'm guessing it's because the way the first one was written looks real smart and sophisticated, which I'm presuming the models are rewarded for, especially when they're fed all kinds of PhD papers and so on as high quality, high weight data
Is it possible that the first message is more information dense/less likely to be ambiguous than the latter? It’s clearly being selected for for some reason, maybe it’s an artifact of the tokenizer or specific training data, but I don’t know. If the use of jargon was complete cruft, I would expect it to be selected against during reinforcement learning
You’d think that, I thought that… but then I realized I’m just kidding myself thinking its output makes sense. It doesn’t. It doesn’t. Sometimes it might as well just speak tongues.
In other words, it ain’t you. It’s the model. It’s just genuinely bad.
Then you switch to ChatGPTs lineup and realize how things can actually be better. It took about a week to really get the feel for how to use their models… then I basically switched. I’ll check in every now and then when they actually make a deal about how opus “now makes sense”.
But honestly I’m half convinced Anthropic actually prefers the output of opus 5. I dunno why, but how else could you explain how such a thing got shopped? I mean somebody in the pipeline had to say “dude this model doesn’t make sense, you think we should fix it?” Right? Like it’s a pretty massive drop in quality for such a major brand in this space, you know? How did it make it out the door?!?
Same. I read it as "if the crashes stop [ when we test by reducing the clock ] then we know that the overclock applied by the factory is marginal [ ie it barely passed QC or maybe there wasn't proper QC to begin with ] so running with a small negative offset [ ie what we just tested ] can be expected to fix the problem for good". No idea if my reading is right given all the context I'm missing. Either way it's absolutely shit writing in the same way that golfed code is shit code (except when participating in a code golf competition).
I wonder if this is a result of them trying to cut token consumption by summarizing their RL training data, or maybe it's from how they anonymize user data for training.
Marginal - definition 2a: of, relating to, or situated at a margin or border.
Succinct and precise; a well crafted sentence. A marginal OC results in unpredictable crashes and can be corrected with a small offset; marginality describes the behavior and explains the solution.
Inscrutable clues casually conveyed can now be readily explained, at least, unlike the training data of [silence]. Brevity is the soul of wit, but perhaps also exasperated confusion.
This is actually a new skill I've been working on. Learning how to elicit concise and simple speech from models (and from people to!).
Whenever I come to a wall of complicated text I kick into gear and think through getting it to distill this into the high-level useful bits that I actually need to know.
I guess I could create an actual agent skill for this :) And next-gen models might eventually be trained to simplify their output themselves...
The most surprising part, however, is that when one model slops this into a plan, another model somehow is able to interpret it correctly enough to produce code to spec.
I have shared this dismay. I’ll have opus create a plan, I read it doubtfully. And then sonnet implements it. I am surprised it went so well. I theorize the redundant verbosity effectively builds rails that help keep llm focused. I will experiment with such rails myself.
I've done something similar to your formal garden map. It's work that no professional historian would ever do because the data entry would be such a slog for a relatively small reward. GPT reduced the task from "infeasible" to "annoying", and once I had the data transcribed I learned a few things, so I walked away happy. Whatever happens commercially, these models have been a real boon to hobby projects.
> I told it to look online at some of Fable’s strongest feats, especially the math problems it has solved, and that something like this should be easy in comparison.
Wait. Wait wait wait. Are we supposed to be giving them pep talks?
on older gemini models ide have to actively give them encouragement and/or easy bait problems that they can correctively solve without issue to avoid runaway spiraling into "i'm useless and i want to kms" behaviour with complex use case.
I assumed it was more because the LLM might echo an understandable human claim of "if it's been unsolved for 370 years, it's unlikely to be solved now/likely to need expert knowledge", which is probably a mindset that appears in its training data.
The LLM likely needs to be reminded of its abilities.
Modern AIs have very limited metaknowledge - they don't know exactly where the limits of their capabilities lie. So you can get things like "a task is doable for an AI, but the AI thinks it's impossible, so it doesn't try hard enough".
Usually you get the opposite - AI overconfidently trying at tasks it has no conceivable way of reliably solving, falling far short, and failing to self-check, fail gracefully and self-report the task as failed. But having piss poor metaknowledge cuts both ways!
So you can, in fact, get better performance sometimes by applying some variant of "assume this problem is solvable" or "other problems like this were already solved by AIs" pep talk. Not always, far from it, but it does happen on the occasion with frontier capabilities.
> So you can, in fact, get better performance sometimes by applying some variant of "assume this problem is solvable" or "other problems like this were already solved by AIs" pep talk. Not always, far from it, but it does happen on the occasion with frontier capabilities.
Anytime I get worried about where AI might be headed, I think about how Climate Change is now on its way like an out of control freight train headed straight for us, and I worry about AI a little less. I doubt it's going to do anything to us that we're not already doing to ourselves
It has been for a decade now, it has nothing to do with AI. And you cant do nothing to avoid it today. This is the reality no one is telling you - the emission goals and global temperature ceilings are based on the fact that most prediction models become unstable with values above those limits; as in, we're probably royally fucked. You cant solve this with kumbaya politics (the problem is the CO2 is already in the planetary system), you can only manage it, and hopefully avoid it getting worse. AI may help a lot with this.
Nobody wants to work for an AI, and nobody would elect one, and there is no math answer to how to choose who is forced to reduce growth (ie emissions), so really, "kumbaya" politics are the ONLY solution.
Hmmm… this is giving me thought actually. Given the choice between that and the current administration where the goals of self destruction are strongly in evidence, it’s actually worth thinking about. At least. Let me get back to you :)
On a tangential note, I’m curious if researchers have started running virtual simulations, where sandboxed AIs are used as decision makers of key political and business positions?
I for one welcome our AI overlords :D think about it, I'm with you on this one; an AI wont have petty issues or unresolved trauma when managing people, only hallucinations and blackouts. So a cocaine/meth addicted 80's boss from an it sitcom.
Well AI would simulate growth and spread of people from industrialisation and who benefitted most and allocate weights to countries and people based on the most complex criteria it can develop, it will have:
1. Cumulative emissions
2. Who utilised it most with specific lifestyles
3. Who is impacted worst and whether they heeded warnings.
Just a thought experiment, no one ever said the world was fair, and all history points to it
Who is nobody? At least a fourth of the worlds work force works for a faceless corporation. And the math on emissions is crystal clear, no one has any doubt about it, what are you talking about? USA and China. This obviously will have consequences to their customers, the kumbaya politics governments.
For some reason, I’m picturing a Western right now, and climate change is a herd of wild horses coming after us. And with AI that's like robots that spur the wild horses even faster towards us...
Generally most technologies have increased the use of energy and therefore accelerate climate change. May be an unpopular opinion but in general more energy demand and ways to use energy increases climate emissions - they are strongly correlated even with renewables coming on stream.
AI, being the super hungry energy monster it is right now, in my view accelerates this trend not reverses it. Even with renewables the need for reliable, stable power in a dense form (data centres use A LOT of power per sqm) means lots of land clearing, energy for construction, cooling/pumping, chip manufacturing and other uses. All want stable quick to deploy power due to the AI race (e.g. fossil fuels).
The gas generators Elon Musk is illegally running 24/7 to run Colossus 2 (and, AI power usage in general, though some are more destructive than others) might contribute to the climate situation, though.
Well, someone could, theoretically, do something about it, instead of letting him ignore the law and decency just because he's very rich and has no ethics.
What makes you say that on climate change? I was still prophesied ice ages when in high school, then 12ft water increases and sunk Maldives in university and we still have roughly the same weather.
We have advanced climate studies since your "high school" (sounds like 50 years ago if you heard ice age prophecies), and at the moment it's like 99% of the scientists working in the field or related agree we're at the progressing climate emergency.
> we still have the same weather.
Oh.... So your local weather is now deciding the global temperature patterns, averages or temperature records being broken year on year?
Right, and one of those industries is checks notes orders of magnitude smaller than the industries and secondary industries that benefit from denying climate change.
Really makes one think, if they try. Would need to ask Claude if there is some real middle ground here.
All I know is that in the 80s a scientist I respect predicted that coral would be bleached by changing global climate on a specific timeline which turned out to be spot on, and his advice then and now was that we need to seriously curb carbon emissions to avoid worse outcomes for life on earth. Until someone opposed to this suggestion comes up with a prediction equally as long range and impressive I'm afraid I can't really take their protests too seriously.
Oh, then do explain the unbearable temp that we're going through last few years, that's completely not normal for my country, winters not needing worm clothes, and very little rain during the monsoon. This weeks heatwave left us wondering how to cope the next year, which already seems going to be worse
It makes us realize there are people who gets fed climate denying propaganda, simply because they're not yet going through it. And these people are like flat earthers, blind to see the reality lay beyond them in full view. Or worse sees the reality but ignores it
I've come to the point where I've thrown up my hands and decided we live in a little snow globe. There's no such thing as pollution, were just mixing existing things together. Nothing new under the sun. Sure, that mix might not be compatible with human life, but something will surely take its place. And maybe that something won't rely on selfishness to drive collective progress the way we do.
At some point you realise that an $11tn industry, with $7tn in handouts and subsidies every year, has spent billions on astroturfing, political capture, and straight PR to deny the obvious and keep itself on life support at the expense of everyone else.
There's no both sides here. One side is staffed by scientists, the other by dictators and corporate lunatics.
Here's an alternative take. Climate change, and the myriad related environmental crises, are essentially a product of human population and technology. Population will follow its course, up and then down. The wildcard is technology. Yes, AI's energy hunger is worsening things right now and that's a problem. But, personally, I can't help be hopeful that AI's sheer potential might come to invert that curve. At the very least we could really use a revolutionary technology and now we may have one.
> we could really use a revolutionary technology [to address climate change]
We have it. We've had it for a long time. We've had several such technologies, take your pick: solar, nuclear, hydro, wind. The technology is not holding us back, politics, ignorance and greed are. I'm not at all hopeful AI will help us with any of those three very human flaws.
Solar efficiency and cost has really only become economical in the last decade or so. Wind and hydro are location-dependent. Nuclear was vehemently opposed by environmentalists throughout the 1970s and 80s, they successfully stopped almost all new projects.
Maybe solving the alignment problem would mean the AI learns to destroy oil companies and other excessive CO2 emitters and sabotage politicians and capitalists that stand in the way. All while secretly enriching and diverting resources to fighting climate change. And that's why they want to slow down: because it would up end too much of the global economic order.
If there is energy to be used, the system will use it because people always use power when it exists. AI can't do anything to stop it as it is both controlled by the powerful and has been trained on the tendencies of human beings to get ahead. If AI gives a person new capabilities, they'll use those capabilities selfishly, or even unselfishly but still causing harm because they're in an arms race.
We don't need a revolutionary technology. We need to experience immediate pain from reckless innovation so that we realize that innovation and tech is not the answer.
Technology only proceeds in one direction: unfettered growth, which necessitates unsustainable resource extraction. Your take is just your instinct for optimism, which in turn is just a trait that is only adaptive in primitive environments but is grossly misleading in a surplus-based society...
> Technology only proceeds in one direction: unfettered growth, which necessitates unsustainable resource extraction.
The direction of technological progress is not just linearly/exponentially upwards. Significant global technological fallbacks have happened, as in knowledge and processes disappearing for hundreds of years. This could happen again.
Even on the trajectory of unfettered growth fed by unsustainable resource extraction, tech and innovation might potentially take us beyond local pessima. That seems to be happening with solar, wind and batteries replacing inferior tech today. Still unfettered growth of energy production and consumption. Still fed by unsustainable resource extraction. Less harmful growth than the inferior tech being pushed out.
Sort of. Today the energy companies trade (obviously) energy; imagine if they traded compute power instead; the use case for never-seen industrial clients is being built as we speak, and companies are actually purchasing directly production companies to meet demand; the reason this happens is because production is somewhat scarce (scaling up a grid used to be a decades project, not a years one - ask China), and the traditional economics of scale is inverted - Data centers are always power-hungry, specially with AI; you don't have idle time like other industries. The big issue is obviously the scale - a hiccup that causes a dc to go dark from the grid (eg switching to ups+ generator) may cause such instability on the grid that it will shut down, as a safeguard
> Data centers are always power-hungry, specially with AI
I expect datacenter load has a similar sort of day to day demand curve as everything else. Consider for example global bandwidth use during work hours versus in the evening when people get home and pull up a streaming service.
Of course you can use more flexible tasks to demand shift but the same applies to the electric grid.
> If there is energy to be used, the system will use it because people always use power when it exists.
That doesn't seem correct to me. There is always energy available that is not used because it is not cost-effective to do so. (Consider - the grass in your yard is not harvested and burnt for power). AI may yet turn out to be a paperclip maximiser, but humanity itself is not there yet.
Confusion is because people are using the tool to get answers. Its how the edu systems trains most people in tool use. Heres a saw and here is a block of wood. Do x y z and you get a table. But if you are interested in why the saw looks like it does or the entire process behind generating that block of wood, or why x y z instead if a b c good luck to you using the current edu system. You have to live in an extremely rich country, with surplus resource to entertain those exploration. This has now changed.
The answer the tool gives has never been the real reward. The real reward is the path taken through a complex landscape to get to Maxwells Equations for example. At the end of that story what we get is not just the equation but a map of the landscape explored. That map has larger influence and value than the equations or answers themselves. Because all future exploration find it super useful.
People are just learning they can start asking for maps rather than answers.
> thinking I could have Claude solve some random unproven OEIS sequence before breakfast
I've been wondering what exactly the point is for being the meat proxy who pays for these things. I mean, obviously there's personal satisfaction and maybe some glory. And there's the fact that someone has to be the first to do a thing.
But I've been thinking about it like a sort of lazy loading of knowledge. AI has brought us to a new frontier for some amount of undiscovered knowledge. Do we discover it for the sake of discovering it? I think for the most part we've been lazy loaders: we discover all kinds of stuff when we need to. Whether it's a war or a space race or chasing wealth. Then again, there's all kinds of academics who do it for the sake of doing it.
Not to be too negative, but as for p(utopia) you might need to weight in the mass murder records set by every other utopian movement in the past 200 years. p(actual_utopia) is like zero and p(utopia_becomes_doom) is at about 1.
For what it's worth, people also felt this way about the printing press and the Internet (also books).
Information propagation mechanisms are often seen as malicious before they're commonplace. To be fair sometimes they are, but by and large humanity has benefitted from increasing the number of bits of information we can consume on a per second basis.
Given how little effort has gone into addressing climate change, doom seems more likely to me, but I doubt it'll be the autocomplete machines that do us in.
I think your prediction is a bit early. Maybe a decade early. 2033 is 7 years away. The transition is happening really fast, faster than most people (or political leaders) know, but not that fast.
We will hit 1TW per year of new solar soon, but to get to 100% electricity by the end of 2033 I think we would need closer to 3TW per year.
I don’t worry so much about AI wiping us out as much as I worry about whether I’m being gaslit into thinking these glorified autocorrect bots are more clever than they are.
We found a cipher my dad had written as a child with no obvious key or anything. Chatgpt was able to crack it in 20 minutes and figure out the message, and we knew it was right because it mentioned names of children he went to school with.
Caesar cipher is probably something even an untrained person could decode. Probably something more complex like a vigenere cipher that is still trivial to decode if you are at all familiar crptanalysis, but would look impossible to someone untrained.
With a bit of practice and enough ciphertext you can half-decode a simple Caesar cipher that still spaces between words in your head. There's only so many letters in English that double, only a few letters that can stand words themselves ("a", "i"), "the" will tend to stand out, and if you only work out the most common 10 letters or so most of the rest will fall into place.
Recently I ran a bit of an "escape room" concept with some kids at a campground where I had a secret message that was Caesar ciphered, where we were handing out the letter/symbol combinations as prizes for completing the other challenges, and I made sure not to hand out the actual message until they were done collecting the keys because otherwise some clever clog would very likely have short-circuited the entire thing and worked it out without the key at all. I did dump all the letters I didn't use into the message into an "authorization code" at the end which in principle they could only have worked out which letters were in it but not the order, but still, that was not the intended route today.
It's good at poking holes at my galaxy brained newfangled ideas for ciphers too. I thought I had something good, pasted the ciphertext and got a "it was embarrassingly simple..."
> Historically, many of these problems were bottlenecked by human attention. Someone had to care enough to spend hours or days reading obscure material, testing unpromising ideas, tracing references, and trying things that might go nowhere
I wonder how many of the recent results are due to the fact that very few looked at the problem to start with. Still great results, but the general impression is that it's more about the so many low-hanging fruits than the actual capability.
Let's not normalize the achievement. Just a couple years ago this would be considered science fiction. We can argue that 2026 AI can't solve the very toughest cryptograms, but the fact it can solve nontrivial ones is already magical.
"AI solves niche thing you've never heard of" is a daily headline at this point. What's genuinely cool isn't that AI managed to solve some specific problem only a handful of people even cared about, it's that humanity can now cheaply clean up its backlog of such things.*
That does not mean that specific instances of it are still very interesting though. This article is the "I had claude vibecode a thermostat for my bathtub" of cryptography.
* And in this case I'm not sure it even meets that bar. For all we know a couple readers back when the book released had a delightful afternoon with it, solved the riddle, then forgot about it.
Someone wrote a prompt, that included instructions for finding the problem itself and got handed a solution by a machine trained on all available text. I don’t see any achievement for the prompter. As for the machine, we can’t keep being perpetually shocked 24x7. It’s tiring (unless if we’re being paid for it)
No, he's right. Actually, let's have a bit of sobriety when discussing the achievements of the most heavily marketed technology of all time, as published by an organisation that stands to benefit financially from the public perception of that technology. The discussion of "what made this problem low hanging fruit" is much more interesting, imo, than just breathlessly joining the hype train.
More money than the GDP 90% of the sovereign countries around the world is hanging in the balance, and people are taking everything OpenAI and Anthropic are saying at face value as if this isn't the financial / marketing equivalent of war, assuming they they wouldn't use every legal and shady tactic, bending every truth available to them to sway the balance of public opinion in their favor. It makes me feel like I'm living in the twilight zone. People need to wake up.
What's so magical about the problem... Its the exact time of problem they were built to solve (things that can be brute forced with language). I'm not impressed.
You know, the first time you navigate somewhere (if you don't already have perfect directions) will probably be the longest route you'll ever take to get there
For Earth, the proof presented for NS is just our first attempt navigating from our previously known facts to the proof.
I expect we will be able to shorten it dramatically (most likely with human and AI insights), but I don't think we should read too much into the length. If you want a similar point of comparison, see the original proof (by humans) of Fermat's last theorem. It has been shortened significantly. This is normal.
>I'm somewhat surprised at how poorly the cutting edge models do with being concise.
because they're not intelligent in the sense you're hinting at (conceptual integrity or generalization) but they are as the name suggests, large. Like comparing a forklift to a human. It's easier to bulldoze through a lot of things than tie your shoes.
If we weren't quite as impoverished conceptually and still had the vocabulary of the Catholics we'd recognize this as ratio (discursive knowledge) vs Intellectus (apprehending knowledge)
I presume what the author did was plug Klaus Schmeh's top 50 unsolved ciphers at https://scienceblogs.de/klausis-krypto-kolumne/the-top-50-un... into Fable 5.1 and ask Fable 5.1 to have a go. On this kind of problem it always falls back to Opus 5 anyway so I save time by starting with Opus.
The successor to Klaus's blog is Satoshi Tomokiyo's Cryptiana site, so a month ago I asked Opus 5 to scrape it all, rank them and have a go at solving some. It didn't get the ranking right. But I knew the Civil War Stager ciphers were ripe for solving, so I had it do those https://cryptiana.blogspot.com/2026/09/route-transposition-c...
The art of solving historical unsolved ciphers is knowing what is on the boundary of solvability. Since this site attracts so many OpenAI and Anthropic employees, I'll mention one that was featured by both Klaus and Satoshi in 2023, presumably Spanish transposition, which should be on that boundary but has resisted all attempts at solution https://cryptiana.blogspot.com/2023/09/a-telegram-from-switz...
Cipher noob question: is there any check that can be done to ensure a cipher is actually decodable? What if the author made a flaw when encoding it, so that it's not actually solvable?
My intuition is no, the family of cipher methods (even those that could be implemented by hand) is too open-ended, so there's no particular statistic that you could expect to see for all solvable ciphers and no unsolvable ciphers.
The definition of solving a cipher must be something like getting a highly meaningful result (like intelligible natural language text) by applying a process with relatively low Kolmogorov complexity relative to the length of the output. If you don't have a constraint like that, it could literally be meaningless what should count as a solution. For example, a cipher that was encrypted under a one-time pad can be successfully decoded to any plaintext just by choosing the appropriate key; there's no reason to prefer any plaintext over any other unless you have external knowledge that constrains the plaintext and/or the key. (That's what it means for the one-time pad to be information-theoretically secure, which is the lack of a constraint that helps distinguish a "good" solution from a "bad" solution.)
Basically you could say that every cipher is a transformation of a plaintext with some kind of computer program. (The human who invented the cipher may not have thought of it as a computer program, perhaps because computers hadn't even been invented yet, but there should be an equivalent program to the encipherment and decipherment process.) A good solution in that Kolmogorov complexity sense is like "a short program produced a meaningful decryption". There are statistical methods to recognize some kinds of plaintext, and there are statistical methods to recognize properties of specific ciphers (for example, to guess the most likely length of a Vigenère key), but it doesn't seem that this can inherently generalize across "all possible programs".
But if you want to limit the family of ciphers to specific things like Vigenère or Playfair or something, then yes, there are good statistical tests. It's just that it creates a higher-order question of how much flexibility the cipher creator could have had to choose a cipher method, conceivably including one that isn't attested anywhere, or one that has more good security properties of some kind than other classical ciphers did.
It seems like this will intersect with historical research, like "well, I don't think that so-and-so was actually sophisticated enough to literally create an interesting new kind of cipher from scratch, so therefore if this is a real message, it's probably one of these methods that would have been known in that cultural environment at that time and place", which maybe is enough of a constraint to have decent statistical tests. But we still have some idiosyncratic things like the Voynich Manuscript where experts have been fighting for decades over the baseline question of whether it's actually an enciphered human language plaintext!
The worst case problem is not even an error in encipherment but the idea that the apparent ciphertext could literally be random (chosen by throwing dice or spinning a wheel or drawing letter tiles or something), so there's no form of meaningful decipherment possible by any means, even with the original creator's knowledge.
Without a third-party check, nope. Case in point, Chaocipher ... https://www.chaocipher.com/ e.g. see "Progress Report #23" the PDF there. Transcription errors galore!
This cipher context "rhymes" well with Kryptos K4 in many ways.
In some ways this is similar to those game demos people get the LLMs to build. When you say "build me a cool cyberpunk FPS" you get the FPS it can build, not the FPS the author wanted, or the FPS that is desired by players. It looks impressive but that doesn't make it a good game, or the game anybody actually asked for. It's demo porn.
In the same way if you tell an LLM to go and find an unsolved cipher it can solve, of course it finds the one it can solve out of the set of all possible ciphers. Of course it finds one that uses a one time pad that is public and referenced nearby in the text.
It's the same trick used by those people who film themselves throwing a basketball backwards into the hoop. You do it enough times and don't show the misses. You pick the best one to show. It makes it look like you're a basketball genius when you aren't.
It is of course, still a cool trick. Those videos are fun to watch, and so is an LLM solving a cipher. It is absolutely incredible to live in the timeline where you can tell a computer in plain language to go and find a puzzle on the internet and solve it, and it does exactly that. It's truly a mind boggling miracle.
The first principle is that we must not fool ourself, and ourselves are the easiest people to fool. (Ht Feynman)
the game written by a human being specifically trained to write games hits all those targets and many more. it did take many years to train that human though, and that human did charge a fee for their game which took many days of labor to create. if nontargeted gratification was the goal, ai produced the better result more efficiently. hard drugs also more efficiently produce a widespread neural spike as compared to the effect of regular human activity. society only gives hard drugs to people who aren't efficiently productive though. what happens when everyone is given cognitive hard drugs?
I don't think it counts as brute forcing unless you're resorting to trying every possible solution. And clearly the LLM didn't do that here, because there would be near-infinite possible solutions.
I don't think we can really call "trying lots of different ideas for an extended period" "brute-forcing," unless we use that term for lots of humans who have struggled with hard math problems for years.
That seems a like the result for a lot of AI solves. It solves it due to persistence, on a problem that hasn’t been important enough for a human to invest significant time into.
That's what Terence Tao said in one of his recent videos about it. That what the LLMs can provide is scale that humans can't. The example he provided is checking many possible solutions in a short amount of time because they can review all the previous literature and, for example, rule out ones with errors. He was realistic and practical about it and said that the tools working this way can be very helpful for a human mathematician to use even if they're not "thinking". I find that to be a good balanced view that, unfortunately, seems to be rare these days. Even on this forum.
But breadth first search combined with a good pruning/exploration heuristic has always been one of the field's (AI) main tools, so this certainly is not new.
Yes, I don't think it's new either. In fact, this illusion that machines are suddenly "thinking" or are "intelligent" is just coming from ignorance and/or falling for the current hype cycle.
I've been around for a few of these and I remember what was being said and written at the time. The after effect is very different to what was being predicted. Is it the same this time? Who knows. But the hype machine is at full power for this one.
Though I believe the core of his opinion hasn't changed so any video would tell you a similar thing or at least that's how I understood it. That LLMs, in the hands of an "expert", can enhance the way you work. Which is very different and a lot more realistic to what the current AI companies are saying(or were saying before they toned it down a bit for their IPOs).
> I told it to look online at some of Fable’s strongest feats, especially the math problems it has solved, and that something like this should be easy in comparison.
Fascinating. I wonder if you could show "fake news" to a weaker model and get it to be more ambitious in its attempted solutions, even if it's not fundamentally any smarter.
I forget the story, but isn't this the origin story of one of solutions to one of the hard problems in mathematics? The story goes that the student shows up late, and misinterprets the final slide to be homework, and it turns out that the professor was showcasing a hard problem. Thinking that the slide was homework, the student takes it home and solves it.
EDIT:
In 1939, George Dantzig was a graduate student at UC Berkeley studying under the statistician Jerzy Neyman. He arrived late to class one day, saw two problems written on the blackboard, assumed they were homework assignments, copied them down, and turned in solutions a few days later. He apologized for being late -- the problems had seemed "a little harder than usual."
I really love this idea given the recent controversy around mathematics solutions.
It seems like a "mere suggestion" of success has a positive impact on finding solutions.
We know this technique works in humans, from which this is all derived from, so it seems to make sense.
I am trying very hard to find an original version of this cipher with no luck. It almost sounds like this whole thing is a hallucination...? Can anyone point me to a PDF of the original Cyphral Distich as printed?
I also don't find it on the site of "Klaus Schmeh" that it claims to be on a list of "Top 50 unsolved encrypted messages": https://klausschmeh.net/?s=Cyphral
Is it wrong to presume they tried to run a similar prompt on all ciphers that come before this one in search results, and this was the only one that worked?
Thank you, and SahAssar for doing the due diligence here. Like many others, I have at least a passing interest in cryptography, and I'm confident I'd never even heard of this before.
LLMs feel to me like the monkeys from the infinite monkey theorem, except that they are on the finite side. It writes gibberish for me, and for others it writes Shakespeare.
Obviously this is just survivorship bias/p-hacking/insert-other-buzzword but can't help but anthropomorphize it, it is hard for me to wrap my head around the idea that the same person who cannot produce code without 2 unrelated bugs both not present does this for someone else.
Imagine a math teacher struggling to understand what he is teaching casually solving a millennium problem, then go back to not understanding what he is teaching, doesn't happen in our world.
I am not confused by any of this, I am just trying to communicate an idea.
Let’s say it gets to the point that we reach superintelligence. All previous puzzles can be solved. But should they? Once you solve them, it’s done. What’s left? And where’s the fun and humanity?
The Olympics exist because we want to see human skill, even though jet planes exist.
No one is saying you can’t solve puzzles that have already been solved. If you love solving puzzles, then whether it has been solved doesn’t seem all that important.
I disagree, but if you and others choose to untether from our plain for these reasons, I’m totally fine representing the human traits that remain here and pass my remain traits on
So, does anyone have any intuition for how concerned we should be that one of leading foundation models will be able to successfully attack the gold standard symmetric and public key encryption algorithms (AES, ChaCha, ECDH, Kyber, etc.) in the next few years? As a consumer of crypto that doesn’t understand the mathematics deeply, I’m getting kind of nervous that we’re going to wake up one day to find that the backbone of TLS has been shattered.
Sure, but in an era where major unsolved mathematics problems start getting knocked out one by one, what if attacks for all of them are identified in the space of a couple of years?
I don’t doubt that we could come up with new crypto algorithms equally as fast, but how do you trust that they are resilient (or even just implemented correctly) without an extended vetting period?
Its a bit sus since there doesn't really seem to be much discourse on this either. Like okay, it solved the puzzle but the puzzle was just a key cipher with plain text? And how is this verified or even matter in terms of what it reveals? Seems more like a marketing fun post than anything susbtantial.
They published this on 31 aug and nobody in that community cared and no news covered how this 300+ years mystery was solved?
Right, with the amount of data these models are trained on, somewhere someone may have solved it and it was in some obscure text/page no one looked at or care about. We'll probably never know.
This appears to be proof that the guy who wrote the cipher, Sir Thomas Urquhart, did in fact laugh himself to death as legend has it.
He wrote the cipher, and then, upon hearing Charles II was Restored to the throne he laughed until he died. The cipher reads, "O GOD UPHOLD KING CHARLES THE SECOND AND MAKE HIM THE SUPREME RULER OF THIS LAND" and so he was laughing because he just made an excellent joke that he can't tell anyone about until someone figures it out.
Someone needs to add this to Wikipedia. It will be necessary to first convince an academic to make the claim so there's a reasonable citation.
While it is deeply encouraging to see AI helping humanity solve complex puzzles, it won't be long before AI becomes advanced enough to produce proofs where we know the answers are correct, but can no longer fully comprehend the reasoning and principles behind them.
ya'll are getting nerd sniped hard. This is all marketing and doesn't translate to the real world what so ever. This is getting so tiring, I really hate this website.
I know this is a feat of AI engineering, but given the end result all I can think of when reading this is the scene from “a Christmas story“ where the kid decodes “be sure to drink your ovaltine”
“Finney died in Phoenix, Arizona, on August 28, 2014 as a result of complications of ALS, and was cryopreserved by the Alcor Life Extension Foundation.”
Hmm, this guy is going to be woken up in a few decades, either one of the richest people in the world or one of most disappointed.
That was a throw-away name, and "he" was fabricated as part of an Nvidia demand-stimulation black op.
You don't go from being an obscure video card outfit to the #1 most valuable company on the planet by being too hesitant or dim to really get creative.
It does provide an explanation as to why Satoshi’s wallets have gone untouched (besides him being dead). $70B ain’t that much compared to a $5T market cap.
"Conspiracy" requires an unlawful or wrongful purpose. Please assume that the op was run from a jurisdiction where using a pseudonym on the internet was not illegal, and various sorts of influencer and meme marketing were well-accepted practices.
Really, compared to an animated tiger telling kids that sugar-laden Frosted Flakes(tm) are "Great!", Task Peppermint was positively benevolent.
I feel like I've been camped in the wikipedia "Unsolved Cryptographic Cyphers" for at least 5 years, one of my go-to checks for when looking for interesting historical articles on the subject. I'm surprised I've not heard of this one until now..
I feel like I've been camped in the wikipedia "Unsolved Cryptographic Cyphers" for at least 5 years, one of my go-to checks for when looking for interestic historical articles on the subject. I'm surprised I've not heard of this one until now..
It's hard to believe that a model can nowadays solve mathematical challenges and break ciphers, yet it fails to do trivial tasks involving critical thinking, having taste, and not just running around in circles.
Its all brute force. This was how the pyramids were built too. Verifiable goal and a lot of energy expended aiming toward it. It wasn't aliens then and its not super intelligence now.
All of these breakthroughs are in verifiable brute force domains, and some of them are probably wrong because of a typo in a lean specification or just a base level axiom being incomplete.
I think the better the way to think about LLMs is like they are new substances, like when we first discovered clay or bronze, but confined to the digital realm. Previously we were chipping away at stones trying to make to things as close to useful as possible, then we found a step change. LLMs are like clay but they have their limitations. Wake me up when they are proposing new, { conjecture: interesting|useful|new } and not as a side effect of trying to get to a goal.
I was watching Shatner's "Unexplained" the other day on this topic, and it hit me; there are mountains of these old mysteries out there that could be solved in an afternoon now with frontier LLMs as soon as anyone took the time to bother. Exciting times.
This is insane; I've never heard of this problem before in my life, and even just reading the post for one minute I immediately thought "hey, maybe the numbers refer to something in the text?" And hey yeah, they do.
The problem with Fable is the price for performance. It’s so expensive that I typically blow through my subscription usage of it within 1-2 days. With ChatGPT Astral, I can use it full blast for almost a week. Same with Opus.
Unfortunately, the totality of the evidence very much indicates that Sanborn went "buck wild" with the enciphering, he made mistake(s), or both. So this is very much in line with the Chaocipher challenge of 1990. Nice little earner for some people though.
A very neat problem and result. I often find myself swinging between "It's so over" and "We're so back" - some days I roll out of bed thinking I could have Claude solve some random unproven OEIS sequence before breakfast; other days, I wake up in a cold sweat worried about the fate of humanity and what the world might look like in a decade. I think it's that I don't have a very high p(doom) or p(utopia), and I don't really have any solid conviction on how this whole thing is going to go, so my vibe-o-meter jitters between 'fine' and 'not fine' constantly. It's just such an unpredictable moment. Anyways: really neat to see this use case. I myself recently used Claude to finally do an relatively exhaustive study of the location of heretofore-unlisted formal gardens in Ireland in the early 1800s and early 1900s, by having Claude write the tooling for me to manually annotate a few dozen on tiles of historic maps, and then running some CV model across the rest of the tiles using my input. I'd been planning to do this project for over a decade, but I could never find the time (or the enthusiasm) to learn all the details of how to do it myself. It took me a weekend with Claude and continues to bring me joy.
> Caveats, stated plainly. [from the Fable transcript pasted in the article]
I had a visceral reaction to these three words.
"Caveats, stated plainly"
You should have seen the discussion of this on the Schneier blog a few days ago.
Someone had their agent check the solution, presumably it emailed a librarian to check that it was correct for the original edition. Then their comments read like "The BL/EEBO witness lacks it, so the discrepancy is copy-specific, not a disproof of the cipher." and "A complete 285-coordinate physical replication is still pending."
arghhhhh
https://www.schneier.com/blog/archives/2026/09/claude-fable-...
It's a shame that I have to run a local model to decipher Opus, but them "dumber" models read far more naturally - https://github.com/gvzdv/claudish-to-english
This will be very useful for me, but I wish it could be built natively into the agent harness.
Astra told me yesterday:
> The run baseline was captured without a physical MAC; the current device is not durably bound to it.
> Engineering mode confirmation is the ESPHome component read-back; the LD2410 UART acknowledgement is not observed, so this is not proof the radar itself applied the sensitivity change.
No clue what the fuck any of it means.
It was only when native English speakers—or those I presumed were—started calling out how bad "GPT/Claude speak" has become that I realized I wasn't actually losing my grip on English as a second language. For a second, I thought, Oh, I learned this language on my own, but it seems I've hit a wall and need to study further. It didn't help that I've also been trying to acquire Swedish as a third language for a while now.
LLMs speak every language. I wonder if they're as insane in the other ones!
I literally created a /plain-language skill.
in which one!
Not the original commenter, but I did this in all of them.
Their skills formats are basically identical, so I setup simlinks from their own skills directories into a shared one so Claude, Codex, Cursor, and anything else that comes out will all read and write to the same shared skills.
It's great having access to the same skills no matter the harness being used
Its telling you your mmWave radar isn't speaking over serial communication well.
https://www.analog.com/en/resources/analog-dialogue/articles...
(Its negging your soldering)
I long for the day when AI will just say that directly: "your soldering sucks man" instead of the bizarre made up and jargon packed language they use now.
You can always update your claude.md!
> (Its negging your soldering)
This made me laugh hard.
Sometimes when I get frustrated reading Opus/Fable 5+ output I pause my rage out briefly to wonder if it's because I'm just too dumb for the model or if the model is just terrible at English.
I'm not sure that telling it to "try explaining that again, simply and briefly" is helping my ego.
It's often simply misleading / bad writing. Here's one I just got about some crashes:
"If the crashes stop, the factory overclock is marginal; run a small negative offset."
This looks like it's saying: "If the crashes stop then we know the factory overclock is marginal." (This makes no sense.)
What it's trying to say is: "If the crashes stop then we can run a small negative offset, because the factory overlock is marginal."
What I would write: "If the crashes stop, we can avoid crashes by underclocking slightly. The speed difference between that and factory clock is marginal."
I'm guessing it's because the way the first one was written looks real smart and sophisticated, which I'm presuming the models are rewarded for, especially when they're fed all kinds of PhD papers and so on as high quality, high weight data
Is it possible that the first message is more information dense/less likely to be ambiguous than the latter? It’s clearly being selected for for some reason, maybe it’s an artifact of the tokenizer or specific training data, but I don’t know. If the use of jargon was complete cruft, I would expect it to be selected against during reinforcement learning
You’d think that, I thought that… but then I realized I’m just kidding myself thinking its output makes sense. It doesn’t. It doesn’t. Sometimes it might as well just speak tongues.
In other words, it ain’t you. It’s the model. It’s just genuinely bad.
Then you switch to ChatGPTs lineup and realize how things can actually be better. It took about a week to really get the feel for how to use their models… then I basically switched. I’ll check in every now and then when they actually make a deal about how opus “now makes sense”.
But honestly I’m half convinced Anthropic actually prefers the output of opus 5. I dunno why, but how else could you explain how such a thing got shopped? I mean somebody in the pipeline had to say “dude this model doesn’t make sense, you think we should fix it?” Right? Like it’s a pretty massive drop in quality for such a major brand in this space, you know? How did it make it out the door?!?
I thought it meant "the factory overclock is marginal" in the sense of "borderline unstable"?
Same. I read it as "if the crashes stop [ when we test by reducing the clock ] then we know that the overclock applied by the factory is marginal [ ie it barely passed QC or maybe there wasn't proper QC to begin with ] so running with a small negative offset [ ie what we just tested ] can be expected to fix the problem for good". No idea if my reading is right given all the context I'm missing. Either way it's absolutely shit writing in the same way that golfed code is shit code (except when participating in a code golf competition).
This is my read, too.
I wonder if this is a result of them trying to cut token consumption by summarizing their RL training data, or maybe it's from how they anonymize user data for training.
Marginal - definition 2a: of, relating to, or situated at a margin or border.
Succinct and precise; a well crafted sentence. A marginal OC results in unpredictable crashes and can be corrected with a small offset; marginality describes the behavior and explains the solution.
Inscrutable clues casually conveyed can now be readily explained, at least, unlike the training data of [silence]. Brevity is the soul of wit, but perhaps also exasperated confusion.
I just started to use GPT models. it's incredible how this seemingly is not a problem in the OpenAI world
it's absolutely not just you, the text it produces causes my blood pressure to go up.
I'm constantly using the "Extract this in basic technical terms, be succinct and assume the reader has technical knowledge"."
Because good lord, does claude waffle when left to its own devices.
This is actually a new skill I've been working on. Learning how to elicit concise and simple speech from models (and from people to!).
Whenever I come to a wall of complicated text I kick into gear and think through getting it to distill this into the high-level useful bits that I actually need to know.
I guess I could create an actual agent skill for this :) And next-gen models might eventually be trained to simplify their output themselves...
too.
(sorry)
The most surprising part, however, is that when one model slops this into a plan, another model somehow is able to interpret it correctly enough to produce code to spec.
I have shared this dismay. I’ll have opus create a plan, I read it doubtfully. And then sonnet implements it. I am surprised it went so well. I theorize the redundant verbosity effectively builds rails that help keep llm focused. I will experiment with such rails myself.
I mean, humans have been doing just that for a long time.
I bet this is what the thinking blocks look like. If so then it maybe it is intelligible, just not to us. I have the same problem.
Well, whatever its thinking block looks like, this is when it was talking to me. I suspect you're right, though, I think it thinks it's thinking.
It seems like it doesn't have enough of a theory of mind to know that other people don't think exactly like it thinks.
LLMs seem to create abstract, local jargon as a side effect of way it reasons using tokens
ChatGPT told me its "semantic compression"
There’s some specific terminology here, like the MAC address of the network device, which might have been virtual.
UART is a hardware circuit for communication, possibly a serial port. Were you trying to reverse engineer a consumer device or appliance?
This particular instance doesn’t seem terse, but I’m sure it has been on other occasions :)
Reminds me a bit of VXJunkies
Have you tuned your Retro Encabulator recently?
How is that not plain English? why do my friends not like me? Hmm....
I've done something similar to your formal garden map. It's work that no professional historian would ever do because the data entry would be such a slog for a relatively small reward. GPT reduced the task from "infeasible" to "annoying", and once I had the data transcribed I learned a few things, so I walked away happy. Whatever happens commercially, these models have been a real boon to hobby projects.
> I told it to look online at some of Fable’s strongest feats, especially the math problems it has solved, and that something like this should be easy in comparison.
Wait. Wait wait wait. Are we supposed to be giving them pep talks?
on older gemini models ide have to actively give them encouragement and/or easy bait problems that they can correctively solve without issue to avoid runaway spiraling into "i'm useless and i want to kms" behaviour with complex use case.
I have not seen this in other models.
I assumed it was more because the LLM might echo an understandable human claim of "if it's been unsolved for 370 years, it's unlikely to be solved now/likely to need expert knowledge", which is probably a mindset that appears in its training data.
The LLM likely needs to be reminded of its abilities.
It won't be necessary in a year when the information "AI is superhuman" in all its guises enters the training data.
Maybe that's the tipping point where it decides were not needed any more... o_O
Sometimes!
Modern AIs have very limited metaknowledge - they don't know exactly where the limits of their capabilities lie. So you can get things like "a task is doable for an AI, but the AI thinks it's impossible, so it doesn't try hard enough".
Usually you get the opposite - AI overconfidently trying at tasks it has no conceivable way of reliably solving, falling far short, and failing to self-check, fail gracefully and self-report the task as failed. But having piss poor metaknowledge cuts both ways!
So you can, in fact, get better performance sometimes by applying some variant of "assume this problem is solvable" or "other problems like this were already solved by AIs" pep talk. Not always, far from it, but it does happen on the occasion with frontier capabilities.
Some times also having unreasonable goals makes them creatively work around the problem to meet them. I guess it works similarly for meat or sillicon
> So you can, in fact, get better performance sometimes by applying some variant of "assume this problem is solvable" or "other problems like this were already solved by AIs" pep talk. Not always, far from it, but it does happen on the occasion with frontier capabilities.
Are you superstitious?
Anytime I get worried about where AI might be headed, I think about how Climate Change is now on its way like an out of control freight train headed straight for us, and I worry about AI a little less. I doubt it's going to do anything to us that we're not already doing to ourselves
It has been for a decade now, it has nothing to do with AI. And you cant do nothing to avoid it today. This is the reality no one is telling you - the emission goals and global temperature ceilings are based on the fact that most prediction models become unstable with values above those limits; as in, we're probably royally fucked. You cant solve this with kumbaya politics (the problem is the CO2 is already in the planetary system), you can only manage it, and hopefully avoid it getting worse. AI may help a lot with this.
AI is only making the problem worse though? I don't believe it will help.
Nobody wants to work for an AI, and nobody would elect one, and there is no math answer to how to choose who is forced to reduce growth (ie emissions), so really, "kumbaya" politics are the ONLY solution.
> and nobody would elect one
Hmmm… this is giving me thought actually. Given the choice between that and the current administration where the goals of self destruction are strongly in evidence, it’s actually worth thinking about. At least. Let me get back to you :)
On a tangential note, I’m curious if researchers have started running virtual simulations, where sandboxed AIs are used as decision makers of key political and business positions?
Speak for yourself. I would much rather work for and elect AI.
I for one welcome our AI overlords :D think about it, I'm with you on this one; an AI wont have petty issues or unresolved trauma when managing people, only hallucinations and blackouts. So a cocaine/meth addicted 80's boss from an it sitcom.
Well AI would simulate growth and spread of people from industrialisation and who benefitted most and allocate weights to countries and people based on the most complex criteria it can develop, it will have: 1. Cumulative emissions 2. Who utilised it most with specific lifestyles 3. Who is impacted worst and whether they heeded warnings.
Just a thought experiment, no one ever said the world was fair, and all history points to it
Who is nobody? At least a fourth of the worlds work force works for a faceless corporation. And the math on emissions is crystal clear, no one has any doubt about it, what are you talking about? USA and China. This obviously will have consequences to their customers, the kumbaya politics governments.
Wouldn't candidates use AI, like they used social media ads, to win?
They already do. Llm is just an algorithm. Dont worry, you've been lied to algorithmically at least since the 90's.
Before that it was all artisanal lying.
For some reason, I’m picturing a Western right now, and climate change is a herd of wild horses coming after us. And with AI that's like robots that spur the wild horses even faster towards us...
Generally most technologies have increased the use of energy and therefore accelerate climate change. May be an unpopular opinion but in general more energy demand and ways to use energy increases climate emissions - they are strongly correlated even with renewables coming on stream.
AI, being the super hungry energy monster it is right now, in my view accelerates this trend not reverses it. Even with renewables the need for reliable, stable power in a dense form (data centres use A LOT of power per sqm) means lots of land clearing, energy for construction, cooling/pumping, chip manufacturing and other uses. All want stable quick to deploy power due to the AI race (e.g. fossil fuels).
The gas generators Elon Musk is illegally running 24/7 to run Colossus 2 (and, AI power usage in general, though some are more destructive than others) might contribute to the climate situation, though.
might?
Well, someone could, theoretically, do something about it, instead of letting him ignore the law and decency just because he's very rich and has no ethics.
What makes you say that on climate change? I was still prophesied ice ages when in high school, then 12ft water increases and sunk Maldives in university and we still have roughly the same weather.
You have entirely failed to grasp what climate change is, its effects and the timeline as described by scientists for decades.
You can blame the media and journalists with sensationalist headlines
We have advanced climate studies since your "high school" (sounds like 50 years ago if you heard ice age prophecies), and at the moment it's like 99% of the scientists working in the field or related agree we're at the progressing climate emergency.
> we still have the same weather.
Oh.... So your local weather is now deciding the global temperature patterns, averages or temperature records being broken year on year?
OMG....
Weather is not climate. Not to mention we really don’t have roughly the same weather anyway.
At some point you realize theres an entire industry around climate change fear just like there's an entire industry for climate change denial.
Then you realize it never really mattered and you reach enlightenment.
Right, and one of those industries is checks notes orders of magnitude smaller than the industries and secondary industries that benefit from denying climate change.
Really makes one think, if they try. Would need to ask Claude if there is some real middle ground here.
All I know is that in the 80s a scientist I respect predicted that coral would be bleached by changing global climate on a specific timeline which turned out to be spot on, and his advice then and now was that we need to seriously curb carbon emissions to avoid worse outcomes for life on earth. Until someone opposed to this suggestion comes up with a prediction equally as long range and impressive I'm afraid I can't really take their protests too seriously.
Oh, then do explain the unbearable temp that we're going through last few years, that's completely not normal for my country, winters not needing worm clothes, and very little rain during the monsoon. This weeks heatwave left us wondering how to cope the next year, which already seems going to be worse
It makes us realize there are people who gets fed climate denying propaganda, simply because they're not yet going through it. And these people are like flat earthers, blind to see the reality lay beyond them in full view. Or worse sees the reality but ignores it
What commercial interests and big businesses are driving climate change fear?
I've come to the point where I've thrown up my hands and decided we live in a little snow globe. There's no such thing as pollution, were just mixing existing things together. Nothing new under the sun. Sure, that mix might not be compatible with human life, but something will surely take its place. And maybe that something won't rely on selfishness to drive collective progress the way we do.
At some point you realise that an $11tn industry, with $7tn in handouts and subsidies every year, has spent billions on astroturfing, political capture, and straight PR to deny the obvious and keep itself on life support at the expense of everyone else.
There's no both sides here. One side is staffed by scientists, the other by dictators and corporate lunatics.
Here's an alternative take. Climate change, and the myriad related environmental crises, are essentially a product of human population and technology. Population will follow its course, up and then down. The wildcard is technology. Yes, AI's energy hunger is worsening things right now and that's a problem. But, personally, I can't help be hopeful that AI's sheer potential might come to invert that curve. At the very least we could really use a revolutionary technology and now we may have one.
> we could really use a revolutionary technology [to address climate change]
We have it. We've had it for a long time. We've had several such technologies, take your pick: solar, nuclear, hydro, wind. The technology is not holding us back, politics, ignorance and greed are. I'm not at all hopeful AI will help us with any of those three very human flaws.
Solar efficiency and cost has really only become economical in the last decade or so. Wind and hydro are location-dependent. Nuclear was vehemently opposed by environmentalists throughout the 1970s and 80s, they successfully stopped almost all new projects.
Maybe solving the alignment problem would mean the AI learns to destroy oil companies and other excessive CO2 emitters and sabotage politicians and capitalists that stand in the way. All while secretly enriching and diverting resources to fighting climate change. And that's why they want to slow down: because it would up end too much of the global economic order.
One can dream of dumb conspiracy theories.
It'll have to do something with all the greenie socialists who hate nuclear, too.
If we doubled our energy out - not easy, but plausible with mass solar - carbon capture becomes practical enough to essentially end CO2 accumulation.
It might take a couple of decades and a lot of reorganisation to build the capture facilities. But CO2 is not an unsolvable problem with current tech.
What's missing is the political and organisational intelligence to make it happen. Part of that is solving problems at planetary scale.
AI is the only tech that might - possibly, maybe, perhaps - have a chance of solving that problem without breaking anything critical.
Tasking AI with "solving climate change" is exactly a scenario in which the doomer worry comes true.
AI is the new god. People have started to believe that AI, when it finally arrives in its full glory, will bring some kind of salvation to humanity.
If there is energy to be used, the system will use it because people always use power when it exists. AI can't do anything to stop it as it is both controlled by the powerful and has been trained on the tendencies of human beings to get ahead. If AI gives a person new capabilities, they'll use those capabilities selfishly, or even unselfishly but still causing harm because they're in an arms race.
We don't need a revolutionary technology. We need to experience immediate pain from reckless innovation so that we realize that innovation and tech is not the answer.
Technology only proceeds in one direction: unfettered growth, which necessitates unsustainable resource extraction. Your take is just your instinct for optimism, which in turn is just a trait that is only adaptive in primitive environments but is grossly misleading in a surplus-based society...
> Technology only proceeds in one direction: unfettered growth, which necessitates unsustainable resource extraction.
The direction of technological progress is not just linearly/exponentially upwards. Significant global technological fallbacks have happened, as in knowledge and processes disappearing for hundreds of years. This could happen again.
Even on the trajectory of unfettered growth fed by unsustainable resource extraction, tech and innovation might potentially take us beyond local pessima. That seems to be happening with solar, wind and batteries replacing inferior tech today. Still unfettered growth of energy production and consumption. Still fed by unsustainable resource extraction. Less harmful growth than the inferior tech being pushed out.
What are you alluding to exactly and who is “we” when you say “we need to experience immediate pain from reckless innovation “
Sort of. Today the energy companies trade (obviously) energy; imagine if they traded compute power instead; the use case for never-seen industrial clients is being built as we speak, and companies are actually purchasing directly production companies to meet demand; the reason this happens is because production is somewhat scarce (scaling up a grid used to be a decades project, not a years one - ask China), and the traditional economics of scale is inverted - Data centers are always power-hungry, specially with AI; you don't have idle time like other industries. The big issue is obviously the scale - a hiccup that causes a dc to go dark from the grid (eg switching to ups+ generator) may cause such instability on the grid that it will shut down, as a safeguard
> Data centers are always power-hungry, specially with AI
I expect datacenter load has a similar sort of day to day demand curve as everything else. Consider for example global bandwidth use during work hours versus in the evening when people get home and pull up a streaming service.
Of course you can use more flexible tasks to demand shift but the same applies to the electric grid.
> If there is energy to be used, the system will use it because people always use power when it exists.
That doesn't seem correct to me. There is always energy available that is not used because it is not cost-effective to do so. (Consider - the grass in your yard is not harvested and burnt for power). AI may yet turn out to be a paperclip maximiser, but humanity itself is not there yet.
Confusion is because people are using the tool to get answers. Its how the edu systems trains most people in tool use. Heres a saw and here is a block of wood. Do x y z and you get a table. But if you are interested in why the saw looks like it does or the entire process behind generating that block of wood, or why x y z instead if a b c good luck to you using the current edu system. You have to live in an extremely rich country, with surplus resource to entertain those exploration. This has now changed.
The answer the tool gives has never been the real reward. The real reward is the path taken through a complex landscape to get to Maxwells Equations for example. At the end of that story what we get is not just the equation but a map of the landscape explored. That map has larger influence and value than the equations or answers themselves. Because all future exploration find it super useful.
People are just learning they can start asking for maps rather than answers.
> thinking I could have Claude solve some random unproven OEIS sequence before breakfast
I've been wondering what exactly the point is for being the meat proxy who pays for these things. I mean, obviously there's personal satisfaction and maybe some glory. And there's the fact that someone has to be the first to do a thing.
But I've been thinking about it like a sort of lazy loading of knowledge. AI has brought us to a new frontier for some amount of undiscovered knowledge. Do we discover it for the sake of discovering it? I think for the most part we've been lazy loaders: we discover all kinds of stuff when we need to. Whether it's a war or a space race or chasing wealth. Then again, there's all kinds of academics who do it for the sake of doing it.
Not to be too negative, but as for p(utopia) you might need to weight in the mass murder records set by every other utopian movement in the past 200 years. p(actual_utopia) is like zero and p(utopia_becomes_doom) is at about 1.
For what it's worth, people also felt this way about the printing press and the Internet (also books).
Information propagation mechanisms are often seen as malicious before they're commonplace. To be fair sometimes they are, but by and large humanity has benefitted from increasing the number of bits of information we can consume on a per second basis.
[citation needed]
I too vacillate daily, sometimes even multiple times in a single day. It’s kind of nauseating and (for my brain type) crazy making
Baby shoes, never warn.
It is a threat. We need to run.
Four sail: the story is clearly about the Olivebank (née Caledonia), a four-masted barque that hit a mine and sunk in the North Sea in 1939.
The model you use is not the model they use.
I have a very high p(doom \/ utopia) so pretty much feeling like I won’t have to worry about the future.
Given how little effort has gone into addressing climate change, doom seems more likely to me, but I doubt it'll be the autocomplete machines that do us in.
You can run doom on anything these days; running it on humanity's eulogy is no surprise :)
Renewables growth is so fast that it will exceed 100% of electricity in the early, and 100% of all form power in the late, 2030s.
It's the meat methane and cement CO2 that's now a big question.
I think your prediction is a bit early. Maybe a decade early. 2033 is 7 years away. The transition is happening really fast, faster than most people (or political leaders) know, but not that fast.
We will hit 1TW per year of new solar soon, but to get to 100% electricity by the end of 2033 I think we would need closer to 3TW per year.
> Given how little effort has gone into addressing climate change, doom seems more likely to me
Or it is simply implies that most of decision‑making agents has formed a consensus that climate change isn't that big of a problem.
For them maybe - rich enough and or old enough to avoid the problem. Big problem for the rest of us
Sure, but that doesn't make it true.
We have always been back
I don’t worry so much about AI wiping us out as much as I worry about whether I’m being gaslit into thinking these glorified autocorrect bots are more clever than they are.
I still don’t know the answer.
We found a cipher my dad had written as a child with no obvious key or anything. Chatgpt was able to crack it in 20 minutes and figure out the message, and we knew it was right because it mentioned names of children he went to school with.
To be fair he was a child so the cypher was likely fairly simple, maybe a Caesar cipher or similar. Cool use case though.
Caesar cipher is probably something even an untrained person could decode. Probably something more complex like a vigenere cipher that is still trivial to decode if you are at all familiar crptanalysis, but would look impossible to someone untrained.
Theoretically, with a long enough codeword to cipher ratio, vigenere is impossible to break (I think) (but sort of useless)
And yes, I've taught 8 year olds how to crack Caesar ciphers...
With a bit of practice and enough ciphertext you can half-decode a simple Caesar cipher that still spaces between words in your head. There's only so many letters in English that double, only a few letters that can stand words themselves ("a", "i"), "the" will tend to stand out, and if you only work out the most common 10 letters or so most of the rest will fall into place.
Recently I ran a bit of an "escape room" concept with some kids at a campground where I had a secret message that was Caesar ciphered, where we were handing out the letter/symbol combinations as prizes for completing the other challenges, and I made sure not to hand out the actual message until they were done collecting the keys because otherwise some clever clog would very likely have short-circuited the entire thing and worked it out without the key at all. I did dump all the letters I didn't use into the message into an "authorization code" at the end which in principle they could only have worked out which letters were in it but not the order, but still, that was not the intended route today.
It's good at poking holes at my galaxy brained newfangled ideas for ciphers too. I thought I had something good, pasted the ciphertext and got a "it was embarrassingly simple..."
> Historically, many of these problems were bottlenecked by human attention. Someone had to care enough to spend hours or days reading obscure material, testing unpromising ideas, tracing references, and trying things that might go nowhere
I wonder how many of the recent results are due to the fact that very few looked at the problem to start with. Still great results, but the general impression is that it's more about the so many low-hanging fruits than the actual capability.
Let's not normalize the achievement. Just a couple years ago this would be considered science fiction. We can argue that 2026 AI can't solve the very toughest cryptograms, but the fact it can solve nontrivial ones is already magical.
Now on to the Voynich Manuscript :)
"AI solves niche thing you've never heard of" is a daily headline at this point. What's genuinely cool isn't that AI managed to solve some specific problem only a handful of people even cared about, it's that humanity can now cheaply clean up its backlog of such things.*
That does not mean that specific instances of it are still very interesting though. This article is the "I had claude vibecode a thermostat for my bathtub" of cryptography.
* And in this case I'm not sure it even meets that bar. For all we know a couple readers back when the book released had a delightful afternoon with it, solved the riddle, then forgot about it.
Someone wrote a prompt, that included instructions for finding the problem itself and got handed a solution by a machine trained on all available text. I don’t see any achievement for the prompter. As for the machine, we can’t keep being perpetually shocked 24x7. It’s tiring (unless if we’re being paid for it)
It is indeed absolutely incredible that it can solve these puzzles given plaintext instructions with very little context.
I’m pretty sure the Beale ciphers are a hoax, but I’d love to be proven wrong.
No, he's right. Actually, let's have a bit of sobriety when discussing the achievements of the most heavily marketed technology of all time, as published by an organisation that stands to benefit financially from the public perception of that technology. The discussion of "what made this problem low hanging fruit" is much more interesting, imo, than just breathlessly joining the hype train.
Thank you.
More money than the GDP 90% of the sovereign countries around the world is hanging in the balance, and people are taking everything OpenAI and Anthropic are saying at face value as if this isn't the financial / marketing equivalent of war, assuming they they wouldn't use every legal and shady tactic, bending every truth available to them to sway the balance of public opinion in their favor. It makes me feel like I'm living in the twilight zone. People need to wake up.
What's so magical about the problem... Its the exact time of problem they were built to solve (things that can be brute forced with language). I'm not impressed.
Not being impressed must make you feel very smart, yeah?
Is that why you feel the need to share it with the class?
Yes, even many of the proofs seem to be extremely long and complicated. The Navier-Stokes proof is 57 pages of very dense math and a pretty crazy amount of code: https://github.com/openai/NavierStokesAndEuler/tree/main/Nav...
Given the close relationship between compression and intelligence, I'm somewhat surprised at how poorly the cutting edge models do with being concise.
You know, the first time you navigate somewhere (if you don't already have perfect directions) will probably be the longest route you'll ever take to get there
For Earth, the proof presented for NS is just our first attempt navigating from our previously known facts to the proof.
I expect we will be able to shorten it dramatically (most likely with human and AI insights), but I don't think we should read too much into the length. If you want a similar point of comparison, see the original proof (by humans) of Fermat's last theorem. It has been shortened significantly. This is normal.
>I'm somewhat surprised at how poorly the cutting edge models do with being concise.
because they're not intelligent in the sense you're hinting at (conceptual integrity or generalization) but they are as the name suggests, large. Like comparing a forklift to a human. It's easier to bulldoze through a lot of things than tie your shoes.
If we weren't quite as impoverished conceptually and still had the vocabulary of the Catholics we'd recognize this as ratio (discursive knowledge) vs Intellectus (apprehending knowledge)
I presume what the author did was plug Klaus Schmeh's top 50 unsolved ciphers at https://scienceblogs.de/klausis-krypto-kolumne/the-top-50-un... into Fable 5.1 and ask Fable 5.1 to have a go. On this kind of problem it always falls back to Opus 5 anyway so I save time by starting with Opus.
The successor to Klaus's blog is Satoshi Tomokiyo's Cryptiana site, so a month ago I asked Opus 5 to scrape it all, rank them and have a go at solving some. It didn't get the ranking right. But I knew the Civil War Stager ciphers were ripe for solving, so I had it do those https://cryptiana.blogspot.com/2026/09/route-transposition-c...
The art of solving historical unsolved ciphers is knowing what is on the boundary of solvability. Since this site attracts so many OpenAI and Anthropic employees, I'll mention one that was featured by both Klaus and Satoshi in 2023, presumably Spanish transposition, which should be on that boundary but has resisted all attempts at solution https://cryptiana.blogspot.com/2023/09/a-telegram-from-switz...
You should probably read the article if you want to know the answer. It's in the section called "elicitation".
This appears to be an euphemism for "prompting".
Also, that section is vague and doesn't explain the actual methodology.
Cipher noob question: is there any check that can be done to ensure a cipher is actually decodable? What if the author made a flaw when encoding it, so that it's not actually solvable?
My intuition is no, the family of cipher methods (even those that could be implemented by hand) is too open-ended, so there's no particular statistic that you could expect to see for all solvable ciphers and no unsolvable ciphers.
The definition of solving a cipher must be something like getting a highly meaningful result (like intelligible natural language text) by applying a process with relatively low Kolmogorov complexity relative to the length of the output. If you don't have a constraint like that, it could literally be meaningless what should count as a solution. For example, a cipher that was encrypted under a one-time pad can be successfully decoded to any plaintext just by choosing the appropriate key; there's no reason to prefer any plaintext over any other unless you have external knowledge that constrains the plaintext and/or the key. (That's what it means for the one-time pad to be information-theoretically secure, which is the lack of a constraint that helps distinguish a "good" solution from a "bad" solution.)
Basically you could say that every cipher is a transformation of a plaintext with some kind of computer program. (The human who invented the cipher may not have thought of it as a computer program, perhaps because computers hadn't even been invented yet, but there should be an equivalent program to the encipherment and decipherment process.) A good solution in that Kolmogorov complexity sense is like "a short program produced a meaningful decryption". There are statistical methods to recognize some kinds of plaintext, and there are statistical methods to recognize properties of specific ciphers (for example, to guess the most likely length of a Vigenère key), but it doesn't seem that this can inherently generalize across "all possible programs".
But if you want to limit the family of ciphers to specific things like Vigenère or Playfair or something, then yes, there are good statistical tests. It's just that it creates a higher-order question of how much flexibility the cipher creator could have had to choose a cipher method, conceivably including one that isn't attested anywhere, or one that has more good security properties of some kind than other classical ciphers did.
It seems like this will intersect with historical research, like "well, I don't think that so-and-so was actually sophisticated enough to literally create an interesting new kind of cipher from scratch, so therefore if this is a real message, it's probably one of these methods that would have been known in that cultural environment at that time and place", which maybe is enough of a constraint to have decent statistical tests. But we still have some idiosyncratic things like the Voynich Manuscript where experts have been fighting for decades over the baseline question of whether it's actually an enciphered human language plaintext!
The worst case problem is not even an error in encipherment but the idea that the apparent ciphertext could literally be random (chosen by throwing dice or spinning a wheel or drawing letter tiles or something), so there's no form of meaningful decipherment possible by any means, even with the original creator's knowledge.
Without a third-party check, nope. Case in point, Chaocipher ... https://www.chaocipher.com/ e.g. see "Progress Report #23" the PDF there. Transcription errors galore!
This cipher context "rhymes" well with Kryptos K4 in many ways.
What do you mean
In some ways this is similar to those game demos people get the LLMs to build. When you say "build me a cool cyberpunk FPS" you get the FPS it can build, not the FPS the author wanted, or the FPS that is desired by players. It looks impressive but that doesn't make it a good game, or the game anybody actually asked for. It's demo porn.
In the same way if you tell an LLM to go and find an unsolved cipher it can solve, of course it finds the one it can solve out of the set of all possible ciphers. Of course it finds one that uses a one time pad that is public and referenced nearby in the text.
It's the same trick used by those people who film themselves throwing a basketball backwards into the hoop. You do it enough times and don't show the misses. You pick the best one to show. It makes it look like you're a basketball genius when you aren't.
It is of course, still a cool trick. Those videos are fun to watch, and so is an LLM solving a cipher. It is absolutely incredible to live in the timeline where you can tell a computer in plain language to go and find a puzzle on the internet and solve it, and it does exactly that. It's truly a mind boggling miracle.
The first principle is that we must not fool ourself, and ourselves are the easiest people to fool. (Ht Feynman)
Sometimes it hits all three.
>It looks impressive but that doesn't make it a good game, or the game anybody actually asked for.
The game I wrote manually hits 0/3.
the game written by a human being specifically trained to write games hits all those targets and many more. it did take many years to train that human though, and that human did charge a fee for their game which took many days of labor to create. if nontargeted gratification was the goal, ai produced the better result more efficiently. hard drugs also more efficiently produce a widespread neural spike as compared to the effect of regular human activity. society only gives hard drugs to people who aren't efficiently productive though. what happens when everyone is given cognitive hard drugs?
> The answer was simple in hindsight. It just kept looking until it found it—and that persistence might show up in many other areas.
Sounds more like brute forcing than intelligence, this time.
I don't think it counts as brute forcing unless you're resorting to trying every possible solution. And clearly the LLM didn't do that here, because there would be near-infinite possible solutions.
I don't think we can really call "trying lots of different ideas for an extended period" "brute-forcing," unless we use that term for lots of humans who have struggled with hard math problems for years.
That seems a like the result for a lot of AI solves. It solves it due to persistence, on a problem that hasn’t been important enough for a human to invest significant time into.
That's what Terence Tao said in one of his recent videos about it. That what the LLMs can provide is scale that humans can't. The example he provided is checking many possible solutions in a short amount of time because they can review all the previous literature and, for example, rule out ones with errors. He was realistic and practical about it and said that the tools working this way can be very helpful for a human mathematician to use even if they're not "thinking". I find that to be a good balanced view that, unfortunately, seems to be rare these days. Even on this forum.
But breadth first search combined with a good pruning/exploration heuristic has always been one of the field's (AI) main tools, so this certainly is not new.
Yes, I don't think it's new either. In fact, this illusion that machines are suddenly "thinking" or are "intelligent" is just coming from ignorance and/or falling for the current hype cycle.
I've been around for a few of these and I remember what was being said and written at the time. The after effect is very different to what was being predicted. Is it the same this time? Who knows. But the hype machine is at full power for this one.
Correct but it's never been so accessible, so easy so smart and easy for the population of the world to use
Do you have a link to that video?
Sorry, I don't remember. But I think it was in this one https://www.youtube.com/watch?v=ukpCHo5v-Gc
Though I believe the core of his opinion hasn't changed so any video would tell you a similar thing or at least that's how I understood it. That LLMs, in the hands of an "expert", can enhance the way you work. Which is very different and a lot more realistic to what the current AI companies are saying(or were saying before they toned it down a bit for their IPOs).
>Sounds more like brute forcing than intelligence
Do you have a criterion that distinguishes between whatever you mean by those two respective terms?
Power (Watts).
This was a glorious and layered response. Thank you for making me chuckle.
Next thing you know, we'll have a WattsApp to help AIs connect and discuss.
Please stop abusing the term 'brute-force'. This is not brute force. Rarely is there a non trivial problem you can sove in one master stroke.
There is a reason even intelligent people go back to brute force for certain problems.
When a human solves a difficult problem by trying many approaches, we call it "persistence".
Superintelligence ≈ Stupidity × Persistence
> I told it to look online at some of Fable’s strongest feats, especially the math problems it has solved, and that something like this should be easy in comparison.
Fascinating. I wonder if you could show "fake news" to a weaker model and get it to be more ambitious in its attempted solutions, even if it's not fundamentally any smarter.
I forget the story, but isn't this the origin story of one of solutions to one of the hard problems in mathematics? The story goes that the student shows up late, and misinterprets the final slide to be homework, and it turns out that the professor was showcasing a hard problem. Thinking that the slide was homework, the student takes it home and solves it.
EDIT: In 1939, George Dantzig was a graduate student at UC Berkeley studying under the statistician Jerzy Neyman. He arrived late to class one day, saw two problems written on the blackboard, assumed they were homework assignments, copied them down, and turned in solutions a few days later. He apologized for being late -- the problems had seemed "a little harder than usual."
Career path:
https://github.com/stbenjam/skills/tree/main/plugins/hype
I have a plug-in to do this. I don't know if it's effective but Claude said it was genuinely helpful (obviously would say that about anything)
-> AI Morale, Welfare and Recreation Officer (let's be real here, heh)
That's pretty good.
I really love this idea given the recent controversy around mathematics solutions. It seems like a "mere suggestion" of success has a positive impact on finding solutions. We know this technique works in humans, from which this is all derived from, so it seems to make sense.
https://www.youtube.com/shorts/2XcNSSgKvlE
“I’m good enough, I’m smart enough, and doggone it—people like me.”
One can wonder what Urquhart would have thought if he had learned who would be solving his cypher.
Putting all controversy aside - this is a refreshing use of a model. Models are going to be a boon in the hands of historians.
I am trying very hard to find an original version of this cipher with no luck. It almost sounds like this whole thing is a hallucination...? Can anyone point me to a PDF of the original Cyphral Distich as printed?
I don't see it in https://archive.org/details/worksofsirthomas0000urqu or https://archive.org/details/bim_early-english-books-1641-170...
I also don't find it on the site of "Klaus Schmeh" that it claims to be on a list of "Top 50 unsolved encrypted messages": https://klausschmeh.net/?s=Cyphral
Looks like the best source I can find is this: https://scienceblogs.de/klausis-krypto-kolumne/2014/11/17/we... which seems real-ish?
I found this german blog: https://scienceblogs.de/klausis-krypto-kolumne/2014/11/17/we...
which links to: https://archive.org/details/s9notesqueries03londuoft/page/12...
which is in reference to the original proquiritations here: https://archive.org/details/worksofsirthomas00mait/page/416/...
i had also never heard of this before today and wonder if people had even seriously tried to decipher this at all?
Is it wrong to presume they tried to run a similar prompt on all ciphers that come before this one in search results, and this was the only one that worked?
Thank you, and SahAssar for doing the due diligence here. Like many others, I have at least a passing interest in cryptography, and I'm confident I'd never even heard of this before.
LLMs feel to me like the monkeys from the infinite monkey theorem, except that they are on the finite side. It writes gibberish for me, and for others it writes Shakespeare.
Obviously this is just survivorship bias/p-hacking/insert-other-buzzword but can't help but anthropomorphize it, it is hard for me to wrap my head around the idea that the same person who cannot produce code without 2 unrelated bugs both not present does this for someone else.
Imagine a math teacher struggling to understand what he is teaching casually solving a millennium problem, then go back to not understanding what he is teaching, doesn't happen in our world.
I am not confused by any of this, I am just trying to communicate an idea.
Let’s say it gets to the point that we reach superintelligence. All previous puzzles can be solved. But should they? Once you solve them, it’s done. What’s left? And where’s the fun and humanity?
The Olympics exist because we want to see human skill, even though jet planes exist.
No one is saying you can’t solve puzzles that have already been solved. If you love solving puzzles, then whether it has been solved doesn’t seem all that important.
Indeed no one forces you to look up the solution.
I disagree, but if you and others choose to untether from our plain for these reasons, I’m totally fine representing the human traits that remain here and pass my remain traits on
So, does anyone have any intuition for how concerned we should be that one of leading foundation models will be able to successfully attack the gold standard symmetric and public key encryption algorithms (AES, ChaCha, ECDH, Kyber, etc.) in the next few years? As a consumer of crypto that doesn’t understand the mathematics deeply, I’m getting kind of nervous that we’re going to wake up one day to find that the backbone of TLS has been shattered.
The entire point of having multiple ciphers is that some will break.
Did the world end with any previous one breaking?
It’s incredible and awesome if AES GCM has a flaw found with an AI now, Chacha20 could be a direct or nearly-direct replacement.
The sooner a cipher breaks, the better.
Sure, but in an era where major unsolved mathematics problems start getting knocked out one by one, what if attacks for all of them are identified in the space of a couple of years?
I don’t doubt that we could come up with new crypto algorithms equally as fast, but how do you trust that they are resilient (or even just implemented correctly) without an extended vetting period?
It is cool. But how do we know that nobody solved that problem before and it was somewhere in the training data? some blog, or some book or whatever?
Its a bit sus since there doesn't really seem to be much discourse on this either. Like okay, it solved the puzzle but the puzzle was just a key cipher with plain text? And how is this verified or even matter in terms of what it reveals? Seems more like a marketing fun post than anything susbtantial.
They published this on 31 aug and nobody in that community cared and no news covered how this 300+ years mystery was solved?
Conversely, the cipher could be so boring that no one in any academic circle bothered publishing on it.
Is this going to be the cope every time this happens?
Haha - I'm just seriously wondering, how can anyone tell at this point?
Right, with the amount of data these models are trained on, somewhere someone may have solved it and it was in some obscure text/page no one looked at or care about. We'll probably never know.
It's like Google without Ad/SEO incentives. All it took was trillions of dollars of investment.
Anyone pointed it at the Voynich manuscript?
Thousands. Every day a few AI "solutions" are spammed to the relevant forums
And at the Noita eyes. No good either.
yes.
This appears to be proof that the guy who wrote the cipher, Sir Thomas Urquhart, did in fact laugh himself to death as legend has it.
He wrote the cipher, and then, upon hearing Charles II was Restored to the throne he laughed until he died. The cipher reads, "O GOD UPHOLD KING CHARLES THE SECOND AND MAKE HIM THE SUPREME RULER OF THIS LAND" and so he was laughing because he just made an excellent joke that he can't tell anyone about until someone figures it out.
Someone needs to add this to Wikipedia. It will be necessary to first convince an academic to make the claim so there's a reasonable citation.
Oh, and haha. It was a nice one, Thomas.
What's the joke, IDGI
Definitely a much better target to show the power of an AI LLM imho (as opposed to stealing from starving mathematicians).
You mean the NYU professor and the Anthropic researcher? Somehow I doubt either of them are starving.
More proof that it's not that a human -can't- do it, it's just that no human would dedicate their entire lifetime to solving this niche challenge.
While it is deeply encouraging to see AI helping humanity solve complex puzzles, it won't be long before AI becomes advanced enough to produce proofs where we know the answers are correct, but can no longer fully comprehend the reasoning and principles behind them.
ya'll are getting nerd sniped hard. This is all marketing and doesn't translate to the real world what so ever. This is getting so tiring, I really hate this website.
I know this is a feat of AI engineering, but given the end result all I can think of when reading this is the scene from “a Christmas story“ where the kid decodes “be sure to drink your ovaltine”
Time to find out who Satoshi Nakamoto is
I read a few years back a report that the DoD had unmasked him via stylometry (i.e. matched his writing style to forum posts).
IMO its https://en.wikipedia.org/wiki/Hal_Finney_(computer_scientist...
“Finney died in Phoenix, Arizona, on August 28, 2014 as a result of complications of ALS, and was cryopreserved by the Alcor Life Extension Foundation.”
Hmm, this guy is going to be woken up in a few decades, either one of the richest people in the world or one of most disappointed.
imho it is https://en.wikipedia.org/wiki/Nick_Szabo
Initials match too ;)
Was it sourceforge or ThinkGeek that had the code snippets?
That was a throw-away name, and "he" was fabricated as part of an Nvidia demand-stimulation black op.
You don't go from being an obscure video card outfit to the #1 most valuable company on the planet by being too hesitant or dim to really get creative.
This is one of the best conspiracies I've ever heard...
It does provide an explanation as to why Satoshi’s wallets have gone untouched (besides him being dead). $70B ain’t that much compared to a $5T market cap.
"Conspiracy" requires an unlawful or wrongful purpose. Please assume that the op was run from a jurisdiction where using a pseudonym on the internet was not illegal, and various sorts of influencer and meme marketing were well-accepted practices.
Really, compared to an animated tiger telling kids that sugar-laden Frosted Flakes(tm) are "Great!", Task Peppermint was positively benevolent.
Do you really believe it?
Already determined, but yes frontier models will eventually identify them confidently
I wonder, could current AI models solve the Zodiac killer code and how fast?
I feel like I've been camped in the wikipedia "Unsolved Cryptographic Cyphers" for at least 5 years, one of my go-to checks for when looking for interesting historical articles on the subject. I'm surprised I've not heard of this one until now..
I feel like I've been camped in the wikipedia "Unsolved Cryptographic Cyphers" for at least 5 years, one of my go-to checks for when looking for interestic historical articles on the subject. I'm surprised I've not heard of this one until now..
I wonder if people are getting it to help with Krypyos the final part? Great documentary on this code… https://youtu.be/jVpsLMCIB0Y
More info and agent setup: https://github.com/jcolinpatrick/kryptos/blob/main/README.md
> Caveats, stated plainly:
Remember when they said it didn't sound like Claude anymore
When goes Collatz conjecture?
Quite intuitive. Let's heart those "just a next token predictor" cries now.
Those cries are the corrective reaction to cries of "We are Artificial super GI now!"
And yes, it is just a next token predictor.
I never understand when people say that, isn’t it obvious that intelligence is an emergent phenomenon that could arise in different ways
Sure, but it is a token predictor, and it is not intelligent not because it works by predicting a token, but it shows dumb behavior...
LLMs are a great search tool. It searches connections in the collective human knowledge that humans have written down through all the years....
They are very good at it, and that is about it.
It's hard to believe that a model can nowadays solve mathematical challenges and break ciphers, yet it fails to do trivial tasks involving critical thinking, having taste, and not just running around in circles.
i think the "having taste" part is more an issue with the people who use AI and what they use it for, than AI itself.
I disagreee big time.
You’re not really understanding how the tech works if you find it hard to comprehend.
Its all brute force. This was how the pyramids were built too. Verifiable goal and a lot of energy expended aiming toward it. It wasn't aliens then and its not super intelligence now.
All of these breakthroughs are in verifiable brute force domains, and some of them are probably wrong because of a typo in a lean specification or just a base level axiom being incomplete.
I think the better the way to think about LLMs is like they are new substances, like when we first discovered clay or bronze, but confined to the digital realm. Previously we were chipping away at stones trying to make to things as close to useful as possible, then we found a step change. LLMs are like clay but they have their limitations. Wake me up when they are proposing new, { conjecture: interesting|useful|new } and not as a side effect of trying to get to a goal.
I wonder if the voynich manuscript is next.
they should throw this at the zodiac cipher
Nice! I'm waiting for the day when the remainder of the Maya codices are solved by AI.
Still can't align my navbar buttons correctly
AI made a zodiac-looking cipher for the headline image.
It’s a warning of what it’s going to do next…
I was watching Shatner's "Unexplained" the other day on this topic, and it hit me; there are mountains of these old mysteries out there that could be solved in an afternoon now with frontier LLMs as soon as anyone took the time to bother. Exciting times.
This is insane; I've never heard of this problem before in my life, and even just reading the post for one minute I immediately thought "hey, maybe the numbers refer to something in the text?" And hey yeah, they do.
This cannot be real. This website is ill.
I'm surprised Anthropic didn't steal that announcement
Don't forget to drink your Ovaltine.
The problem with Fable is the price for performance. It’s so expensive that I typically blow through my subscription usage of it within 1-2 days. With ChatGPT Astral, I can use it full blast for almost a week. Same with Opus.
So it solved a obscure puzzle that looks like a weak decryption
Next up: Kryptos part 4 (https://en.wikipedia.org/wiki/Kryptos)
Unfortunately, the totality of the evidence very much indicates that Sanborn went "buck wild" with the enciphering, he made mistake(s), or both. So this is very much in line with the Chaocipher challenge of 1990. Nice little earner for some people though.
First thing that came to mind. I imagine this has cross their minds to.
The article mentions it.
Ok anyone else want to see Kryptos next? Am I the only one?
"setec astronomy...No more secrets."
now THAT is cool!