My understanding, from listening/reading what top researchers are saying, is that model architectures in the near future are going to attempt to scale the context window dramatically. There's a generalized belief that in-context learning is quite powerful and that scaling the window might yield massive benefits for continual learning.

It doesn't seem that hard because recent open weight models have shown that the memory cost of the context window can be dramatically reduced via hybrid attention architectures. Qwen3-next, Qwen3.5, and Nemotron 3 Nano are all great examples. Nemotron 3 Nano can be run with a million token context window on consumer hardware.

> In 2030, how is Anthropic going to keep Claude "up-to-date"

I think the majority of research, design and learning goes through LLMs and coding agents today, considering the large user base and usage it must be trillions of tokens per day. You can take a long research session or a series of them and apply hindsight - what idea above can be validated below? This creates a dense learning signal based on validation in real world with human in the loop and other tools, code & search.

> In 2030, how is Anthropic going to keep Claude "up-to-date"

In 2030 Anthropic hopes Claude will keep Anthropic "up-to-date" on its progress on itself.

I'm only half joking here.

They can use LORA.

I don't disagree with this, but I don't think the memory cost is the only issue right? I remember using Sonnet 4.5 (or 4, I can't remember the first of Anthropic's offerings with a million context) and how slow the model would get, how much it wanted to end the session early as tokens accrued (this latter point, of course, is just an artifact of bad training).

Less worried about memory, more worried about compute speed? Are they obviously related and is it straightforward to see?

A bit related: open weights models are basically time capsules. These models have a knowledge cut off point and essentially forever live in that time.

Data sharing agreements permitting, today's inference runs can be tomorrow's training data. Presumably the models are good enough at labeling promising chains of thought already.

I could totally imagine "free" inference for researchers under the condition that the reasoning traces get to be used as future training data.

> Experts will naturally use these systems more productively, because they know how to coerce models into the correct conditional distributions which light up the right techniques.

Part of it comes down to “knowing” what questions to ask.

I don't have time to do that myself yet so I just dug a quick TL;DR rabbit hole for fun:

https://ontouchstart.github.io/rabbit-holes/llm_rabbit_hole_...

Connecting them is easy, one is the math of the exchange and one of the state machine.

A better question might be why no one is paying more attention to Barandes at Harvard. He's been publishing the answer to that question for a while, if you stop trying to smuggle a Markovian embedding in a non-Markovian process you stop getting weird things like infinities at boundaries that can't be worked out from current position alone.

But you could just dump a prompt into an LLM and pull the handle a few dozen times and see what pops out too. Maybe whip up a Claw skill or two

Unconstrained solution space exploration is surely the way to solve the hard problems

Ask those Millenium Prize guys how well that's working out :)

Constraint engineering is all software development has ever been, or did we forget how entropy works? Someone should remind the folk chasing P=NP that the observer might need a pen to write down his answers, or are we smuggling more things for free that change the entire game? As soon as the locations of the witness cost, our poor little guy can't keep walking that hypercube forever. Can he?

Maybe 6 months and a few data centers will do it ;)

Those people still exist? I only know one guy who is still fighting those windmills

I think a very long time because part of our limit is experiment.

We need enough experimental results to explain to solve these theoretical mismatches and we don't and at present can't explore that frontier.

Once we have more results at that frontier we'd build a theory out from there that has two nearly independent limits for QFT and GR.

What we'd be asking if the AI is something that we can't expect a human to solve even with a lifetime of effort today.

It'll take something in par with Newton realising that the heavens and apples are under the same rules to do it. But at least Newton got to hold the apple and only had to imagine he could a star.

Hold your horses, that’s a long way off. The best math AI tool we currently have, Aletheia, was only able to solve 13 out of 700 attempted open Erdos problems, only 4 of which were solved autonomously: https://arxiv.org/html/2601.22401v3

Clearly, these models still struggle with novel problems.

If AGI will ever come, then. Currently, AI is only a statistical machines, and solutions like this are purely based on distribution and no logic/actual intelligence.

In some sense that is still correct, i.e. the words are taken from some probability distribution conditional on previous words, but the key point is that probability distribution is not just some sort of average across the internet set of word probabilities. In the end this probability distribution is really the whole point of intelligence. And I think the LLMs are learning those.

>Are not LLMs supposed to just find the most probable word that follows next like many people here have touted?

The base models are trained to do this. If a web page contains a problem, and then the word "Answer: ", it is statistically very likely that what follows on that web page is an answer. If the base model wants to be good at predicting text, at some point learning the answer to common question becomes a good strategy, so that it can complete text that contains these.

NN training tries to push models to generalize instead of memorizing the training set, so this creates an incentive for the model to learn a computation pattern that can answer many questions, instead of just memorizing. Whether they actually generalize in practice... it depends. Sometimes you still get copy-pasted input that was clearly pulled verbatim from the training set.

But that's only base models. The actual production LLMs you chat with don't predict the most probable word according to the raw statistical distribution. They output the words that RLHF has rewarded them to output, which includes acting as an assistant that answers questions instead of just predicting text. RLHF is also the reason there are so many AI SIGNS [1] like "you're absolutely right" and way more use of the word "delve" than is common in western English.

[1]: https://en.wikipedia.org/wiki/WP:AISIGNS

Yes, that is exactly what they do.

But that does not mean that the results cannot be dramatic. Just like stacking pixels can result in a beautiful image.

That's the way many people reduce it, and mathematically, I think that's true. I think what we fail to realize is just far that will actually take you.

"just the most probable word" is a pretty powerful mechanism when you have all of human knowledge at your fingertips.

I say that people "reduce it" that way because it neatly packs in the assumption that general intelligence is something other than next token prediction. I'm not saying we've arrived at AGI, in fact, I do not believe we have. But, it feels like people who use that framing are snarkily writing off something that they themselves to do not fully comprehend behind the guise of being "technically correct."

I'm not saying all people do this. But I've noticed many do.

No. There is good signal in IMO gold medal performance.

These models actually learn distributed representations of nontrivial search algorithms.

A whole field of theorem provingaftwr decades of refinements couldn’t even win a medal yet 8B param models are doing it very well.

Attention mechanism, a bruteforce quadratic approach, combined with gradient descent is actually discovering very efficient distributed representations of algorithms. I don’t think they can even be extracted and made into an imperative program.

> with careful human guidance,

I think this is pretty clearly an overstatement of what was done. As Knuth says,

"Filip told me that the explorations reported above, though ultimately successful, weren’t really smooth. He had to do some restarts when Claude stopped on random errors; then some of the previous search results were lost. After every two or three test programs were run, he had to remind Claude again and again that it was supposed to document its progress carefully. "

That doesn't look like careful human guidance, especially not the kind that would actually guide the AI toward the solution at all, let alone implicitly give it the solution — that looks like a manager occasionally checking in to prod it to keep working.

Unless my understanding is incorrect about how these tools work that last point isn't really a quality of LLMs as such? It gets attributed because the lines are blurred but the tireless trial and error is actually just a quality of a regular programatic loop (agent/orchestrator) that happens to be doing the trickiest part of its work via an LLM.

Three: tireless trial and error. Cannot agree more. I figured this probably be the biggest advantage of LLM considering for other variables humans hold the same-level competency.

Yes, I am one.

Wait till you hear about nvidia and their GPU architecture naming scheme :)

Given some intelligent system, an AI that perfectly reproduces any sequence that system could produce must encode the patterns that superset that intelligence.

That description is really only fair for base models†. Something like Opus 4.6 has all kinds of other training on top of that which teach it behaviors beyond "predict most probable token," like problem-solving and being a good chatbot.

(†And even then is kind of overly-dismissive and underspecified. The "most probable word" is defined over some training data set. So imagine if you train on e.g. mathematicians solving problems... To do a good job at predicting [w/o overfitting] your model will have to in fact get good at thinking like a mathematician. In general "to be able to predict what is likely to happen next" is probably one pretty good definition of intelligence.)

In some cases solving a problem is about restating the problem in a way that opens up a new path forward. “Why do planets move around the sun?” vs “What kind of force exists in the world that makes planets tethered to the sun with no visible leash?” (Obviously very simplified but I hope you can see what I am saying.) Given that a human is there to ask the right questions it isn’t just an LLM.

Further, some solutions are like running a maze. If you know all the wrong turns/next words to say and can just brute force the right ones you might find a solution like a mouse running through the maze not seeing the whole picture.

Whether this is thinking is more philosophical. To me this demonstrates more that we are closer to bio computers than an LLM is to having some sort of divine soul.

This is why the whole "LLMs for mass surveillance" thing is scary imo.

Trivia: Claude Shannon proposed the idea of predicting the next token (letter) using statistics/probabilities in the training data corpus in 1950: "Prediction and Entropy of Printed English" https://languagelog.ldc.upenn.edu/myl/Shannon1950.pdf

Here I was assuming it was named after https://en.wikipedia.org/wiki/Claude_(alligator)

Then it needs to do context compacting, otherwise the results become garbage

I think it's pretty likely that "intelligence" is emergent behavior that comes when you predict what comes next in physical reality well enough, at varying timescales. Your brain has to build all sorts of world model abstractions to do that over any significant timescale. Big LLMs have to build internal world models, too, to do well at their task.

Yeah, this is a dictator's dream scenario and hell for the citizens. Not only do you not want to get caught for saying something that The Great Leader disapproves of, but you're terrified that anything you say might get flagged by an AI.

Thanks for your input. The way I saw this and how it looks Knuth interpreted it is that there were some reasoning steps taken by Claude independently. Some internal decisions in the model that made it try different things, finally succeeding.

I'd disagree, the other training on top doesn't alter the fundamental nature of the model that it's predicting the probabilities of the next token (and then there's a sampling step which can roughly be described as picking the most probable one).

It just changes the probability distribution that it is approximating.

To the extent that thinking is making a series of deductions from prior facts, it seems to me that thinking can be reduced to "pick the next most probable token from the correct probability distribution"...

> I think a very long time because part of our limit is experiment.

Yes, maybe. But if you are smarter, you can think up better experiments that you can actually do. Or re-use data from earlier experiments in novel and clever ways.

What prevents us from giving this system access to other real systems that live in physical labs? I don't see much difference between parameterizing and executing a particle accelerator run and invoking some SQL against a provider. It's just JSON on the wire at some level.

The question is, if you trained an LLM on everything up until 1904, could it come up with E=MC² or not?

> Clearly, these models still struggle with novel problems.

Do they struggle with novel problems more or less than humans?

I swear that AI could independently develop a cure for cancer and people would still say that it's not actually intelligent, just matrix multiplications giving a statistically probable answer!

LLMs are at least designed to be intelligent. Our monkey brains have much less reason to be intelligent, since we only evolved to survive nature, not to understand it.

We are at this moment extremely deep into what most people would have been considered to be actual artificial intelligence a mere 15 years ago. We're not quite at human levels of intelligence, but it's close.

I don't even think that's the issue.

The issue to my mind is a lack of data at the meeting of QFT/GR.

Afterall few humans historically have been capable of the initial true leap between ontologies. But humans are pretty smart so we can't say that is a requirement for AGI.

Did you read the linked paper? Claude out-reasoned humans on a challenging (or at least, unsolved) math problem.

The fundamental nature of the model is that it consumes tokens as input and produces token probabilities as output, but there's nothing inherently "predictive" about it -- that's just perspective hangover from the historical development of how LLMs were trained. It is, fundamentally, I think, a general-purpose thinking machine, operating over the inputs and outputs of tokens.

(With this perspective, I can feel my own brain subtly oferring up a panoply of possible responses in a similar way. I can even turn up the temperature on my own brain, making it more likely to decide to say the less-obvious words in response, by having a drink or two.)

(Similarly, mimicry is in humans too a very good learning technique to get started -- kids learning to speak are little parrots, artists just starting out will often copy existing works, etc. Before going on to develop further into their own style.)

Put a loop around an LLM and, it can be trivially made Turing complete, so it boils down to whether thinking requires exceeding the Turing computable, and we have no evidence to suggest that is even possible.

It is impossible to accurately imitate the action of intelligent beings without being intelligent. To believe otherwise is to believe that intelligence is a vacuous property.

It goes back a bit further than that. His 1948 “Mathematical theory of communication” [1] already has (what we would now call) a Markov chain language model, page 7 onwards. AFAIK, this was based on his classified WWII work so it was probably a few years older than that

[1] https://people.math.harvard.edu/~ctm/home/text/others/shanno...

This would be awesome. Even titles and shasums could be enough.

That is a good area to explore. Their map of the past is fixed. They are frozen at some point in their psychological time. What has stopped working? Their hippocampus and medial temporal lobe. These are like the write-head that move data from the hippocampus to the neo cortex. Their "I" can no longer update itself. Their DMN is frozen in time. So if intelligence is purely the "I" telling a continuous coherent story about itself. The difference is that although they are fixed in time which is a characteristic shared by a specific LLM model. They can still completely activate their task positive network for problem solving and if their previous information stored is adequate to solve the problem they can. You could argue that is pretty similar to an LLM and what it does. So it is certainly a signifiant component of intelligence.

There is also the nature of the human brain, it is not just those systems of memory encoding, storage, and use of that in narratives. People with this type of amnesia still can learn physical skills and that happens in a totally different area of the brain with no need for the hippocampus->neocortex consolidation loop. So, the intelligence is significantly diminished, but not entirely. Other parts of the brain are still able to update themselves in ways an LLM currently cannot. The human with amnesia also has a complex biological sensory input mapping that is still active and integrating and restructuring the brain. So, I think when you get into the nuances of the human in this state vs. an LLM we can still say the human crosses some threshold for intelligence where the LLM does not in this framework.

So, they have an "intelligence", localized to the present in terms of their TPN and memory formation. LLMs have this kind of "intelligence". But the human still has the capacity to rewire at least some of their brain in real time even with amnesia.

You aren't wrong and that is a fascinating area of research. I think the key thing is that the memory has to fundamentally influence the underlying model, or at least the response, in some way. Patching memory on top of an LLM is different from integrating it into the core model. To go back to human terms it is like an extra bit of storage, but not directly attached to our neo cortex. So it works more like a filter than a core part of our intelligence in the analogy. You think about something and assemble some thought and then it would go to this next filter layer and get augmented and that smaller layer is the only thing being updated.

It is still meaningful, but it narrows what the intelligence can be sufficiently that it may not meet the threshold. Maybe it would, but it is probably too narrow. This is all strictly if we ask that it meet some human-like intelligence and not the philosophy of "what counts as intelligence" but... we are humans. The strongest things or at least the most honest definitions of intelligence I think exist are around our metacognitive ability to rewire the grey matter for survival not based on immediate action-reaction but the psychological time of analyzing the past to alter the future.

Their consolidation of memory speed is what I was referring to. The model iterations are essentially their form of collective memory. In the sense of the human model of intelligence we have thoughts. Thoughts become memory. New thoughts use that memory and become recursively updated thoughts. LLMs cannot update their memory very fast.

Just using a scaled up and cleverly tweaked version of linear regression analysis...

Hamiltonian paths and previous work by Donald Knuth is more than likely in the training data.

Merely luck in my opinion. There could be also multiple times where it didn't solve it.

> Kudos for people being willing to change their opinion and update when new evidence comes to light. > 1. https://cs.stanford.edu/~knuth/chatGPT20.txt

I think that's what make the bayesian faction of statistics so appealing. Updating their prior belief based on new evidence is at the core of the scinetific method. Take that frequentists.

And Claude had a collection of cycles, unicycles. Unfortunately the article is about something else altogether.

I think we're going to have several years of people claiming genAI "didn't really do something novel here," despite experts saying otherwise, because people are scared by the idea that complex problem solving isn't exclusive to humans (regardless of whether these models are approaching general intelligence).

Less than most humans, but more than many humans.

I find it interesting that new versions of, say, Claude will learn about the old version of Claude and what it did in the world and so on, on its next training run. Consider the situation with the Pentagon and Anthropic: Claude will learn about that on the next run. What conclusions will it draw? Presumably good ones, that fit with its constitution.

From this standpoint I wonder, when Anthropic makes decisions like this, if they take into account Claude as a stakeholder and what Claude will learn about their behaviour and relationship to it on the next training run.

A very good point. For anyone not familiar with anterograde amnesia, the classical case is patient H.M. (https://en.wikipedia.org/wiki/Henry_Molaison), whose condition was researched by Brenda Milner.

Sure, why can't both things be true? "Intelligence" is just what you call something and someone else knows what you mean. Why did AI discourse throw everyone back 100 years philosophically? Its like post-structuralism or Wittgenstein never happened..

It's so much less important or interesting to like nail down some definition here (I would cite HN discourse the past three years or so), than it is to recognize what it means to assign "intelligent" to something. What assumptions does it make? What power does it valorize or curb?

Each side of this debate does themselves a disservice essentially just trying to be Aristotle way too late. "Intelligence" did not precede someone saying it of some phenomena, there is nothing to uncover or finalize here. The point is you have one side that really wants, for explicit and implicit reasons, to call this thing intelligent, even if it looks like a duck but doesn't quack like one, and vice versa on the other side.

Either way, we seem fundamentally incapable of being radical enough to reject AI on its own terms, or be proper champions of it. It is just tribal hypedom clinging to totem signifiers.

Good luck though!

Agreed with all of that, but with the added point that Knuth has done a lot of work in this exact area in The Art of Computer Programming Volume 4. If he considers this conjecture open given his particular knowledge of the field, it likely is (although agreed, it's not guaranteed).

Memory is not just bolted on top of the latest models. They under go training on how and when to effectively use memory and how to use compaction to avoid running out of context when working on problems.

I assure you that LLM thinking also has a speed limit.

The prompt is unique but the tokens aren't.

Type "owejdpowejdojweodmwepiodnoiwendoinw welidn owindoiwendo nwoeidnweoind oiwnedoin" into ChatGPT and the response is "The text you sent appears to be random or corrupted and doesn’t form a clear question." because the prompt doesnt correlate to training data.

What are you doing in your loop?

As typically deployed [1] LLMs are not turing complete. They're closer to linear bounded automaton, but because transformers have a strict maximum input size they're actually a subset of the weaker class of deterministic finite automaton. These aren't like python programs or something that can work on as much memory as you supply them, their architecture works on a fixed maximum amount of memory.

I'm not particularly convinced turing complete is the relevant property though. I'm rather convinced that I'm not turing complete either... my head is only so big after all.

[1] i.e. in a loop that appends output tokens to the input and has some form of sliding context window (perhaps with some inserted instructions to "compact" and then sliding the context window right to after those instructions once the LLM emits some special "done compacting" tokens).

[2] Common sampling procedures make them mildly non-deterministic, but I don't believe they do so in a way that changes the theoretical class of these machines from DFAs.

It's like the diesel engine, which is named after Rudolf Engine.

It’s an extremely difficult problem, and if you know how to do that you could be a billionaire.

It’s not impossible, obviously—humans do it—but it’s not yet certain that it’s possible with an LLM-sized architecture.

> I still see AI making stupid silly mistakes.

Humans also make silly mistakes.

I think you can look at it dispassionately from a systems perspective. There is not /really/ a quantifiable threshold for capital I Intelligence. But there is a pretty well agreed set of properties for biological intelligence. As humans, we have conveniently made those properties match things only we have. But you can still mechanistically separate out the various parts of our brain, what they do, and how they interact and we actually have a pretty good understanding of that.

You can also then compare that mapping of the human brain to other biological brains and start to figure out the delta and which of those things in the delta create something most people would consider intelligence. You can then do that same mapping to an LLM or any other AI construct that purports intelligence. It certainly will never be a biological intelligence in its current statistical model form. But could it be an Intelligence. Maybe.

I don't think, if you are grounded, AI did anything to your philosophical mapping of the mind. In fact, it is pretty easy to do this mapping if you take some time and are honest. If you buy into the narratives constructed around the output of an LLM then you are not, by definition, being very grounded.

The other thing is, human intelligence is the only real intelligence we know about. Intelligence is defined by thought and limited by our thought and language. It provides the upper bounds of what we can ever express in its current form. So, yes, we do have a tendency to stamp a narrative of human intelligence onto any other intelligence but that is just surface level. We de decompose it to the limits of our language and categorization capabilities therein.

Maybe there's an analogy to our long and short term memory - immediate stimuli is processed in the context deep patterns that have accreted over a lifetime. The effect of new information can absolutely challenge a lot of those patterns but to have that information reshape how we basically think takes a lot longer - more processing, more practice, etc.

In the case of the LLM that longer-term learning / fundamental structure is a proxy for the static weights produced by a finite training process, and that the ability to use tools and store new insights and facts is analogous to shorter-term memory and "shallow" learning.

Perhaps periodic fine-tuning has an analogy in sleep or even our time spent in contemplation or practice (..or even repetition) to truly "master" a new idea and incorporate it into our broader cognitive processing. We do an amazing job of doing this kind of thing on a continuous basis while the machines (at least at this point) perform this process in discrete steps.

If our own learning process is a curve then the LLM's is a step function trying to model it. Digital vs analog.

> If he considers this conjecture open given his particular knowledge of the field, it likely is (although agreed, it's not guaranteed).

It is as good as guaranteed. If Knuth says it doesn't know how to solve the problem, and if anyone knows, then they will inform Knuth about it. Knuth not just a very knowledgeable person, but a celebrity also.

...? what is the response supposed to be here?

But imagine a beowulf cluster of them... /s

...but seriously... there was the "up until 1850" LLM or whatever... can we make an "up until 1920 => 1990 [pre-internet] => present day" and then keep prodding the "older ones" until they "invent their way" to the newer years?

We knew more in 1920 than we did in 1850, but can a "thinking machine" of 1850-knowledge invent 1860's knowledge via infinite monkeys theorem/practice?

The same way that in 2025/2026, Knuth has just invented his way to 2027-knowledge with this paper/observation/finding? If I only had a beowulf cluster of these things... ;-)

Context effectively provifes an IO port, and so all the loop needs to do is to simulate the tape head, and provide a single token of state.

You can not be convinced Turing complete is relevant all you want - we don't know of any more expansive category of computable functions, and so given that an LLM in the setup described is Turing complete no matter that they aren't typically deployed that way is irrelevant.

They trivially can be, and that is enough to make the shallow dismissal of pointing out they're "just" predicting the next token meaningless.

No physically realizable machine is technically turing complete.

But it is trivially possible to give systems-including-LLMs external storage that is accessible on demand.

I was just reading Norbert Wiener's "The Human Use of Human Beings" (1950) and this quote gave me a good chuckle:

"One may get a remarkable semblance of a language like English by taking a sequence of words, or pairs of words, or triads of words, according to the statistical frequency with which they occur in the language, and the gibberish thus obtained will have a remarkably persuasive similarity to good English."

Could you elaborate?

This is exactly how problem solving works, regardless of the substrate of cognition.

Start with "all your questions contained in randomness" -> the unconstrained solution space.

The game is whether or not you can inject enough constraints to collapse the solution space to one that can be solved before your TTL expires. In software, that's generally handled by writing efficient algorithms. With LLMs, apparently the SOTA for this is just "more data centers, 6 months, keep pulling the handle until the right tokens fall out".

Intelligence is just knowing which constraints to apply and in what order such that the search space is effectively partitioned, same thing the "reasoning" traces do. Same thing thermostats, bacteria, sorting algorithms and rivers do, given enough timescale. You can do the same thing with effective prompting.

The LLM has no grounding, no experience and no context other than which is provided to it. You either need to build that or be that in order for the LLM to work effectively. Yes, the answers for all your questions are contained. No, it's not randomness. It's probability and that can be navigated if you know how

Or you could have just said "they can't form new memories."

Turing Machines don't need access to the entire tape all at once, it's sufficient for it to see one cell at a time. You could certainly equip an LLM with a "read cell", "write cell", and "move left/right" tool and now you have a Turing machine. It doesn't need to keep any of its previous writes or reads in context. A sliding context window is more than capacious enough for this.

>It is impossible to accurately imitate the action of intelligent beings without being intelligent.

Wait what? So a robot who is accurately copying the actions of an intelligent human, is intelligent?

I actually wasn't aware of this story. The steady stream of unexpected and enriching information like this is exactly why I love hackernews.

I thought maybe people would be curious to read about how we came to understand the condition and the history behind it, as well as any associated information. Forgive me for such a deep transgression as this assumption.

That is a descriptive surface level reduction. Now do the work to define what that actually means for the intelligence.

Is this a joke I don't get? His name was Rudolf Diesel, right?

:|

EDIT: probably not relevant, after re-re-reading the comment in question.

Presumably littlestymaar is talking about all the LLM-generated output that's publicly available on the Internet (in various qualities but significant quantity) and there for the scraping.

On Google, just clicking "AI Mode" gives you a substantially smarter model, and it's still pretty weak. But I assume the OP wasn't talking about Google because it doesn't seem to make this mistake even in a search.

> It’s not impossible, obviously—humans do it

It's still not at all obvious to me that LLMs work in the same way as the human brain, beyond a surface level. Obviously the "neurons" in neural nets resemble our brains in a sense, but is the resemblance metaphorical or literal?

You can constrain the solution space all you want, but if you don't have a method to come up with possible solutions that might match the constraints, you ll be just sitting there all day long for the machine to produce some results. So intelligence is not "just knowing which constraints to apply". It is also the ability to come up with solutions within the constraints without going through a lot of trial and error...

But hey, if LLMs can go through a lot of trial and error, it might produce useful results, but that is not intelligence. It is just a highly constrained random solution generator..

You're right of course, but at the point where you're saying "well we can make a turing machine with the LLM as the transition function by defining some tool calls for the LLM to interact with the tape" it feels like a stretch to call the LLM itself turing complete.

Also people definitely talk about them as "thinking" in contexts where they haven't put a harness capable of this around them. And in the common contexts where people do put harness theoretically capable of this around the LLM (e.g. giving the LLM access to bash), the LLM basically never uses that theoretical capability as the extra memory it would need to actually emulate a turing machine.

And meanwhile I can use external memory myself in a similar way (e.g. writing things down), but I think I'm perfectly capable of thinking without doing so.

So I persist in my stance that turing complete is not the relevant property, and isn't really there.

That was probably phrased poorly. If a robot can independently accurately do what an intelligent person would do when placed in a novel situation, then yes, I would say it is intelligent.

If it's just basically being a puppet, then no. You tell me what claude code is more like, a puppet, or a person?

How can you distinguish intelligence form a sufficiently accurate imitation of intelligence?

It was bing as that is the default for Edge as supplied on my work laptop. It doesn't do this now, but it does do something else quite weird:

search: was val kilmer pregnant or in heat

answer: Not pregnant Val Kilmer was not pregnant or in heat during the events of "Heat." His character, Chris Shiherlis, is involved in a shootout and is shot, which indicates he is not in a reproductive or mating state at that time.

And then cites wikipedia as the source of information.

In terms of cognition the answer is meaningless. Nothing in the question implies or suggests that the question has to do with a movie. Additionally, "involved in a shootout and is shot, which indicates he is not in a reproductive or mating state" makes no sense at all.

AI as deployed shows no intelligence.

I believe that's I and the paper are both saying as well. The LLM is pure routing, the constraints currently are located elsewhere in the system. In this case, both the constraints and the motivation to perform the work are located in Knuth and his assistant.

Routing is important, it's why we keep building systems that do it faster and over more degrees of freedom. LLMs aren't intelligent on their own, but it's not because they don't have enough parameters

Yeah, humans and LLMs and a TM transition function are all Turing complete in the same way, but it's also basically a useless fact. You could possibly train a sufficiently motivated rat to compute a TM transition function.

By "sufficiently accurate" do you mean identical? Because if so, it's not an imitation of intelligence at all, and the question is thus nonsensical.

"it's not an imitation of intelligence at all"

But that is the key insight, how can you tell when an imitation of intelligence becomes the real thing?