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.
Is there anything that LLMs (or any software for that matter) could imaginably and theoretically do but do humans qualitatively cannot do?
I don't really know what I'm talking about but I imagined the difference was always quantitative (in a nutshell: they need less time).
Or almost any invention for that matter.
Washing machines can’t do anything humans do, they just remove labour. Trucks don’t do anything longboats can’t, it just need less labour and time/effort to build roads rather than canals. Computers can’t calculate anything humans can’t dry run by hand etc.
Everything in reality is about reducing time/effort/material/cost or achieving more with less resource.
Yes. I'd also argue there isn't anything humans couldn't do in theory, other than things strictly prohibited by known laws of physics. It's been pretty conclusively shown in the last 100 years, we're past tipping point of civilizational knowledge and scientific infrastructure.
What stops us from doing any specific thing is always allocation of resources - there's finite amount of time/effort/material/labor available, and past trivial amounts we need more and more people to agree on some allocation. Reduction of time/effort/material/labor costs of any thing is what moves it closer from "infeasible" to "feasible" for us to do. But again, short of violating laws of physics, it was never "impossible".
And then you realize AI was made by humans...
What I mean is that "can be done by humans" is ill defined. Are you allowed to use pencil and paper? That's not part of your body? Do you have to be strapped into a coffin for it to count as human-done? How about wheels, sticks and stones? Allowed to use those tools? How about an abacus? Or is electricity the line to draw?
No, they don't - that implication is in the heads of people who believe AI is magic, or believe AI is advertised as magic, or believe that humans are dumb.
The actual implication is, and always been, different: AI did something humans theoretically could, given enough time, motivation and budget, but they didn't, because it wasn't the best use of time or money. AI therefore demonstrates its value, by opening up problems that were previously uneconomical to solve.
> that implication is in the heads of people who believe AI is magic, or believe AI is advertised as magic, or believe that humans are dumb.
Yes! I believe that class of people are called "investors."
Your point is basically correct, I think, but the illusion of value that a corporate entity offers, and the reality of the value that the corporate entity is creating, are distinct things.
AI demonstrates it's value by solving a problem humans found not worth their time.
This is less valuable than me asking chatgpt to find me a recipe.
Not "not worth their time", but "that they cannot afford to solve".
That's the same sentence.
They cannot afford to lose the worth of their time solving that problem.
Yeah, but then it's clear it's describing a good chunk of most interesting and most important problems there are, for individuals and societies alike.
There's a lot we can't afford to work on simply because living has stupidly high overhead.
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.
> A sufficiently dedicated human analyst could have done this; but they didn't.
I read that as saying that a human typically wouldn't have the time or the patience. But now with a sufficiently smart LLM, the problem becomes tractable.
Sci-fi plot:
We change as a society the definition of intelligence to "what humans can do better than AI".
We end up in a world where AI is only worse than humans in things where we are worse chimpanzees.
Long-term, if we allow AI to manufacture machines, the set of such things is empty.
Immediate-term, it's pretty clear that natural division of labor is that computers do the thinking, and humans do the menial, manual work that requires mixing precision and power movements in field conditions.
‘Thinking’ is rich. They are not thinking.
Ironically the person verifying the output does both the thinking and doing in that regard. For without this - the output of an llm could be of zero value.
Obviously not true, unless you think that "idea" is the only thing, and "execution" is worthless, at arbitrary timescales.
Which would make 99% of us "not thinking" 99% of the time.
You seem to have a poor understanding of how humans operate.
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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.
There is no typo in the key, just a typo in the encrypted message:
S4STEN
The S should probably be a 2 instead.
Is the ?4th a date?
Isn't this just a brute forcing exercise of a (in today's terms) very small key then?
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.
Can we stop using "brute force" for designating "tour de force"?
https://en.wikipedia.org/wiki/Brute-force_attack
I'm aware. Locating a single probable key is exactly not that.
Going through a list of possibilities one by one is bruee forcing.
I think it is legitimate to argue that the process here does not fit the usual definition of "brute forcing". Traditionally, brute forcing would refer to something like a dictionary attack, where an algorithm tries to match all possible combinations of words to eg. find a password. Here, the approach was more common sense based, using historical records and possible error sources to narrow down the possibility space enormously in advance, try out a much more limited set of options within that space until you got a result that made sense, and finally validate those results using historical records. It's the exact same kind of "brute forcing" a human expert would do.
Why? Because dictionary here is smaller than your idea of dictionary for brute force?
Because it wasn't an expert or a heuristic developed by an expert that made it possible to decrease the dictionary size.
If the final set of possibilities is astronomically smaller than a naive one, calling the whole process a "brute force exercise" draws attention to an astronomically insignificant part.
That’s a fair point.
We've moved the goalpost for AI often enough that even being as capable as "a sufficiently dedicated human analyst" is not considered noteworthy.
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]
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.
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.
That’s a lot of words just to say “but modern AIs have access to more data”. Why overcomplicate prose? To sound smarter?
Still, OP’s argument still holds even if AIs today have much more data to rely upon.
The fact that cyc, wikidata, description logics and ontologies have led us nowhere is a pretty good argument against your "simplification".
The internet is at your disposal go write a bunch of rules that make use of that data to do common sense reasoning, I dare you.
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