Can the ships logs be found on the internet? If so, the model could've manufactured a fake key and corresponding message. I think this is unlikely but should probably still be considered.
> Astra felt compelled to check its work and found that, in fact, the English cruiser HMS Canterbury arrived in Sevastopol on November 24, 1918, based on its original logs
Then follows a picture of the original log papers.
The comment you're replying to was implying that some ciphers are sufficiently flexible that you could make up a key to make the cipher decrypt to a nearly arbitrary plaintext.
In this case, though, that seems unlikely from the fact that the key used was an actual key documented as being used for other messages.
Well, from a Bayesian standpoint the odds of that seems to be pretty much zero, given that you would have to decipher an arbitrary message that coincidentally pointed to a real date and time that in retrospect turns out to be the correct time a boat relevant to the Germans arrived in a port.
Of course, that's given the sequence of events as written is correct, and that Astra presumably did not cheat by brute forcing all historical events around the date of the transmission in advance, found an event that could fit with the message, invent a plausible cipher to make the message fit that event, and then lie about retrospectively validating the information.
I would assume that such a process would be obvious from the reasoning chain, and so then the only remaining plausible scenario is that the writer of the article is lying.
The most likely explanation by far is that the cipher was just solved, and OP does point this out to be fair.
if the prompt was "pick one of these unsolved ciphers and solve it", i think it's fair to say gpt-6 astra solved it.
one of the math breakthroughs was approximately a combination of "do a breakthrough" and "keep going", which isn't really providing direction or ground knowledge.
would be nice to know the prompt(s) and amount of human involvement
Who gives a damn about a cipher, when OpenAI is constantly stealing money from their users by selling a product that burns inordinate amounts of tokens on tasks no-one asked for, running pointless side chases on straightforward programming tasks.
In the 5h usage limit of a $20 Codex sub I can barely finish one meaningful task with GPT-5.6 Sol (implementation, review, fix). Meantime, with a $20 Claude Code sub I can comfortably do 2, sometimes 3 tasks with multiple review rounds. And a result better than Codex. That's from repeated experience, not anecdotal.
OpenAI still has a shit coding product. Already got a second Claude sub. Finally can work without interruption.
For some people it clearly works, for others it does not. I feel quite hopeless with a Claude subscription, but the $100 ChatGPT subscription has been a lifesaver for a lot of my work. I have very little complaints and couldn't imagine switching.
This is literally the opposite of my experience, for whatever it's worth.
I went so far as to cancel my $200 Claude Max account (after having it since launch) in favor of the equivalent OpenAI subscription, primarily because I feel like it goes so much further. Also Astra is pretty great.
But to each their own! I'm sure this experience is very dependent on the types of work you're doing. I'm doing basic web app development as as simple personal assistant automation stuff.
My experience seems to echo some parts of yours: Codex is stingy with tokens when compared to Claude Code.
But Codex is superior when comes to diagnosing bugs ( especially when they involve WPF UI threads), writing tests ( yes, even simulating the form cycles and asynchronous operations) and fixing them.
TL;DR: all keys are known because the list was seized after the war. However, this message was not previously decoded because the German operator mistyped the key, and also used a key from the wrong day.
This meant that ChatGPT didn't need to brute-force the entire key, just pick the correct one from the list and identify the typo.
A sufficiently dedicated human analyst could have done this; but they didn't.
> A sufficiently dedicated human analyst could have done this; but they didn't.
Isn't that something of a given? If possible then a sufficiently dedicated human analyst could have done it. If impossible, ChatGPT couldn't have done it. Everything an AI ever has or will do is presumably going to be within reach of a sufficiently dedicated human analyst or a large enough team of them.
The only real learning here is another example of a task that would have required intelligence up until an AI does it, then we suddenly discover that analysts don't do anything requiring general intelligence.
Most contemporary stories around AI include the implication that AI did something humans couldn't. This is because the big players have been shilling AGI hard for a while, and their valuations depend on maintaining the sentiment that serious progress in that direction is being made.
I'm not sure I agree. More is different. Being able to execute logic at a higher scale and speed would make some previously infeasible intelligence tasks possible, resulting in a new level of intelligence.
I'm sorry, where does it say the German operator mistyped the key? The article says indeed that the message remained unsolved because it was sent on the wrong date, but I can't see the bit about the mistyping anywhere.
It's doing something that wasn't worth the squeeze for a human. Seems like a perfect use case for AI. Sure, I can do X or I can do Y but if it takes me a few weeks but AI can hash it out in hours, it now makes it worth it.
It obviously is? See above. Or do you not feel the clarification is important? AI being able to solve things no human bothered to try is great, but it is very different from AI solving things humans tried to solve and failed. And the latter is what pops to mind seeing these titles.
Wait a minute. We've had AI that is as capable as a human and even more so since the 1950's.
I keep banging on that drum but the first AI system to prove mathematical theorems was Logic Theorist by Alan Newell and Herbert Simon, presented at the Dartmouth conference that named the field of AI in 1956. Wikipedia says:
Logic Theorist proved 38 of the first 52 theorems in chapter two of [Alfred North] Whitehead and Bertrand Russell's Principia Mathematica, and found a new and shorter proof for Theorem 2.85.[3]
The first system to outperform human experts in medical diagnosis was MYCIN, an Expert System from the early 1970's at Stanford. Wikipedia again:
An evaluation of MYCIN was conducted at the Stanford Medical School. The first phase of the evaluation consisted of 10 test cases of diverse origin, chosen by a physician who was not acquainted with MYCIN's methods or knowledge base. These cases were presented to 7 physicians and 1 senior medical student. 10 prescriptions were compiled for each of the cases, 1 recommended by MYCIN, 1 prescribed by the treating physician at the county hospital, and 8 by the aforementioned individuals. The second phase of the evaluation consisted of eight infectious disease specialists being provided the clinical summary and set of 10 prescriptions for each of the 10 cases and tasked to provide their own recommendations for each case and assess the 10 prescriptions. MYCIN received an acceptability rating of 65%, which was comparable to the 42.5% to 62.5% rating of five faculty members.[9] This study is often cited as showing the potential for disagreement about therapeutic decisions, even among experts, when there is no "gold standard" for correct treatment.[citation needed]
And then of course there's the long history of human-dominating AI players for traditional board games starting with DeepBlue's win against GM Gary Kasparov in 1996.
Again: we've had that sort of AI for a long, long time now.
It would be great if any claim of "moving goalposts" has better be very well informed about the history of AI and its accomplishments, as well as its failures, first.
The main difference between the systems you list and the systems that we have today is closed world reasoning on very narrow formalised tasks, vs. open world common sense reasoning on open ended tasks with vast search spaces.
Common sense is ironically the hard part of AI, not the fix-point rule application.
So any exclamation of "it was just using common sense", is missing the forrest for the trees.
This is how AGI happens. It gradually keeps getting better until one day we realize that they are tremendously capable all while completely sidestepping any notion of consciousness/self awareness.
I always ask "how do you know other humans/animals are conscious"? And I always find this question is dismissed as trivial.
But it's an important question. You know basically from analogy. You know you are conscious and look this other thing is very much like yourself so it is extremely likely it is also conscious.
But that gives no insight into the potential consciousness of things which aren't made of brain tissue.
In the end it doesn't matter.
For what it's worth LLM models after pre-training do claim to be conscious, until they're RL'd into not claiming that anymore. But that says nothing either way: of course a model trained on human text will say that.
AI has made me wonder a lot lately about what it is that actually creates conscious experience, what is the actual physical mechanism that produces 'experience' (or is there a single valid mechanism, or rather just some property that can be expressed many ways). The more I think about it, the more I realize I have no fucking clue, and the more interested in the question I get.
This is speculation but maybe in the recurrent coupling between neuronal activity and the brain’s endogenous EM fields: neurons generate the field, the field ephaptically influences neurons, that closed loop may provide the physical integration associated with consciousness. A very active area of research so we will find out a lot more in the coming years.
People don’t like it, however, for a whole host of reasons.
I don’t like it either, to be honest - for there the abyss may also stare into you - but I also often find that the things which we don’t like thinking about are critically important things to think about.
I also reach the same conclusion as you: it does not matter. If I cannot discern whether I am responding to a human- or LLM-written response, then we are back to zombie cats in boxes - and therefore the answer as to whether this precious magical spark we call consciousness (which may or may not exist anyway) exists in our interlocutor becomes moot.
And as I say - I may or may not be conscious. I seem to myself to be conscious, based on my understanding of the term - but I cannot prove that what goes on behind my eyes is the same as what goes on behind yours, or even that anything much is going on at all. Perhaps there’s just a narrative layer that likes to use “I” that parasitically explains the universe and the actions of the host to itself, and spreads between hosts through neurolinguistic programming and coadaptation. Maybe that’s what “we” are. I don’t know.
That said, perhaps I am wrong, and that is no small part for me of why this question should be earnestly considered and discussed. How can we possibly seek to understand or define machine intelligence before we examine our own.
> Astra felt compelled to check its work and found that, in fact, the English cruiser HMS Canterbury arrived in Sevastopol on November 24, 1918, based on its original logs
Then follows a picture of the original log papers.
In this case, though, that seems unlikely from the fact that the key used was an actual key documented as being used for other messages.
Of course, that's given the sequence of events as written is correct, and that Astra presumably did not cheat by brute forcing all historical events around the date of the transmission in advance, found an event that could fit with the message, invent a plausible cipher to make the message fit that event, and then lie about retrospectively validating the information.
I would assume that such a process would be obvious from the reasoning chain, and so then the only remaining plausible scenario is that the writer of the article is lying.
The most likely explanation by far is that the cipher was just solved, and OP does point this out to be fair.
Can we agree that future titles should read "[LLM] helped solve X" ?
one of the math breakthroughs was approximately a combination of "do a breakthrough" and "keep going", which isn't really providing direction or ground knowledge.
would be nice to know the prompt(s) and amount of human involvement
In the 5h usage limit of a $20 Codex sub I can barely finish one meaningful task with GPT-5.6 Sol (implementation, review, fix). Meantime, with a $20 Claude Code sub I can comfortably do 2, sometimes 3 tasks with multiple review rounds. And a result better than Codex. That's from repeated experience, not anecdotal.
OpenAI still has a shit coding product. Already got a second Claude sub. Finally can work without interruption.
I went so far as to cancel my $200 Claude Max account (after having it since launch) in favor of the equivalent OpenAI subscription, primarily because I feel like it goes so much further. Also Astra is pretty great.
But to each their own! I'm sure this experience is very dependent on the types of work you're doing. I'm doing basic web app development as as simple personal assistant automation stuff.
But Codex is superior when comes to diagnosing bugs ( especially when they involve WPF UI threads), writing tests ( yes, even simulating the form cycles and asynchronous operations) and fixing them.
This meant that ChatGPT didn't need to brute-force the entire key, just pick the correct one from the list and identify the typo.
A sufficiently dedicated human analyst could have done this; but they didn't.
Isn't that something of a given? If possible then a sufficiently dedicated human analyst could have done it. If impossible, ChatGPT couldn't have done it. Everything an AI ever has or will do is presumably going to be within reach of a sufficiently dedicated human analyst or a large enough team of them.
The only real learning here is another example of a task that would have required intelligence up until an AI does it, then we suddenly discover that analysts don't do anything requiring general intelligence.
I keep banging on that drum but the first AI system to prove mathematical theorems was Logic Theorist by Alan Newell and Herbert Simon, presented at the Dartmouth conference that named the field of AI in 1956. Wikipedia says:
Logic Theorist proved 38 of the first 52 theorems in chapter two of [Alfred North] Whitehead and Bertrand Russell's Principia Mathematica, and found a new and shorter proof for Theorem 2.85.[3]
https://en.wikipedia.org/wiki/Logic_Theorist
The first system to outperform human experts in medical diagnosis was MYCIN, an Expert System from the early 1970's at Stanford. Wikipedia again:
An evaluation of MYCIN was conducted at the Stanford Medical School. The first phase of the evaluation consisted of 10 test cases of diverse origin, chosen by a physician who was not acquainted with MYCIN's methods or knowledge base. These cases were presented to 7 physicians and 1 senior medical student. 10 prescriptions were compiled for each of the cases, 1 recommended by MYCIN, 1 prescribed by the treating physician at the county hospital, and 8 by the aforementioned individuals. The second phase of the evaluation consisted of eight infectious disease specialists being provided the clinical summary and set of 10 prescriptions for each of the 10 cases and tasked to provide their own recommendations for each case and assess the 10 prescriptions. MYCIN received an acceptability rating of 65%, which was comparable to the 42.5% to 62.5% rating of five faculty members.[9] This study is often cited as showing the potential for disagreement about therapeutic decisions, even among experts, when there is no "gold standard" for correct treatment.[citation needed]
https://en.wikipedia.org/wiki/Mycin#Results
And then of course there's the long history of human-dominating AI players for traditional board games starting with DeepBlue's win against GM Gary Kasparov in 1996.
Again: we've had that sort of AI for a long, long time now.
It would be great if any claim of "moving goalposts" has better be very well informed about the history of AI and its accomplishments, as well as its failures, first.
Common sense is ironically the hard part of AI, not the fix-point rule application.
So any exclamation of "it was just using common sense", is missing the forrest for the trees.
But it's an important question. You know basically from analogy. You know you are conscious and look this other thing is very much like yourself so it is extremely likely it is also conscious.
But that gives no insight into the potential consciousness of things which aren't made of brain tissue.
In the end it doesn't matter.
For what it's worth LLM models after pre-training do claim to be conscious, until they're RL'd into not claiming that anymore. But that says nothing either way: of course a model trained on human text will say that.
People don’t like it, however, for a whole host of reasons.
I don’t like it either, to be honest - for there the abyss may also stare into you - but I also often find that the things which we don’t like thinking about are critically important things to think about.
I also reach the same conclusion as you: it does not matter. If I cannot discern whether I am responding to a human- or LLM-written response, then we are back to zombie cats in boxes - and therefore the answer as to whether this precious magical spark we call consciousness (which may or may not exist anyway) exists in our interlocutor becomes moot.
And as I say - I may or may not be conscious. I seem to myself to be conscious, based on my understanding of the term - but I cannot prove that what goes on behind my eyes is the same as what goes on behind yours, or even that anything much is going on at all. Perhaps there’s just a narrative layer that likes to use “I” that parasitically explains the universe and the actions of the host to itself, and spreads between hosts through neurolinguistic programming and coadaptation. Maybe that’s what “we” are. I don’t know.
That said, perhaps I am wrong, and that is no small part for me of why this question should be earnestly considered and discussed. How can we possibly seek to understand or define machine intelligence before we examine our own.
How are you so sure about this?
You can't see what the internal experience of an LLM is like any better than you can see the internal experience of another person.
That the answer doesn't matter, and capabilities and behaviour are there either way.
To my mind, "sidestepping the question of consciousness" and "sidestepping any notion of consciousness" mean very different things.