At the risk of doxxing myself, I have an advanced degree in Applied Mathematics. I have authored and contributed to multiple published papers, and hold a US patent all related to the use of machine learning in robotics and digital signal processing. I am currently employed as a supervising engineer by at a prominent tech company. For pseudonymity's sake I am not going to say which, but it is a name that you would recognize. I say this not to brag, but to establish some context for the following.
Imagine that you are someone who is deeply interested in space flight. You spend hours of your day thinking seriously about Orbital Mechanics and the implications of Relativity. One day you hear about a community devoted to discussing space travel and are excited at the prospect of participating. But when you get there what you find is a Star Trek fan-forum that is far more interested in talking about the Heisenberg compensators on fictional warp-drives than they are Hohmann transfers, thrust to ISP curves, or the effects on low-gravity on human physiology. That has essentially been my experience trying to discuss "Artificial Intelligence" with the rationalist community.
However at the behest of users such as @ArjinFerman and @07mk, and because X/Grok is once again in the news, I am going to take another stab at this.
Are "AI assistants" like Grok, Claude, Gemini, and DeepSeek intelligent?
I would say no, and in this post I am going to try to explain why, but to do so requires a discussion of what I think "intelligence" is and how LLMs work.
What is Intelligence
People have been philosophizing on the nature of intelligence for millennia, but for the purposes of our exercise (and my work) "intelligence" is a combination of perceptivity and reactivity. That is to say, the ability to perceive or take in new and/or changing information combined with the ability to change state based on that information. Both are necessary, and neither is sufficient on it's own. This is why Mathematicians and Computer Scientists often emphasize the use of terms like "Machine Learning" over "Artificial Intelligence" as an algorithms' behavior is almost never both.
If this definition feels unintuitive, consider it in the context of the following example. What I am saying is that an orangutan who waits until the Zookeeper is absent to use a tool to force the lock on it's enclosure is more "intelligent" than the insect that repeatedly throws itself against your kitchen window in an attempt to get outside. While they share an identical goal (to get outside) but the orangutan has demonstrated the ability to both perceive obstacles (IE the lock and the Zookeeper), and react dynamically to them in a way that the insect has not. Now obviously these qualities exist on a spectrum (try to swat a fly and it will react) but the combination of these two parameters define an axis along which we can work to evaluate both animals and algorithms, and as any good PM will tell you, the first step to solving any practical engineering problem is to identify your parameters.
Now the most common arguments for AI assistants like Grok being intelligent tend to be some variation on "Grok answered my question, ergo Grok is intelligent." or "Look at this paragraph Claude wrote, do you think you could do better?" but when evaluated against the above parameters, the ability to form grammatically correct sentences and the ability to answer questions are both orthogonal to it. An orangutan and a moth may be equally incapable of writing a Substack, but I don't expect anyone here to seriously argue that they are equally intelligent. By the same token a pocket calculator can answer questions, "what is the square root of 529?" being one example of such, but we don't typically think of pocket calculators as being "intelligent" do we?
To me, these sorts of arguments betray a significant anthropomorphic bias. That bias being the assumption that anything that a human finds complex or difficult must be computationally complex and vice versa. The truth is often the inverse. This bias leads people who do not have a background in a math or computer science to have completely unrealistic impressions of what sort of things are easy or difficult for a machine to do. For example, vector and matrix operations are a reasonably simple thing for a computer that a lot of human students struggle with. Meanwhile bipedal locomotion is something most humans do without even thinking, despite it being more computationally complex and prone to error than computing a cross product.
Speaking of vector operations, let's talk about how LLMs work...
What are LLMs
LLM stands for "Large Language Model". These models are a subset of artificial neural network that uses "Deep Learning" (essentially a fancy marketing buzzword for the combination of looping regression analysis with back-propagation) to encode a semantic token such as the word "cat" as a n-dimensional vector representing that token's relationship to the rest of the tokens in the training data. Now in actual practice these tokens can be anything, an image, an audio-clip, or a snippet of computer code, but for the purposes of this discussion I am going to assume that we are working with words/text. This process is referred to as "embedding" and what it does in effect is turn the word "cat" into something that a computer (or grad-student) can perform mathematical operations on. Any operation you might perform on a vector (addition, subtraction, transformation, matrix multiplication, etc...) can now be done on "cat".
Now because these vectors represent the relationship of the tokens to each other, words (and combinations of words) that have similar meanings will have vectors that are directionally aligned with each other. This has all sorts of interesting implications. For instance you can compute the dot product of two embedded vectors to determine whether their words are are synonyms, antonyms, or unrelated. This also allows you to do fun things like approximate the vector "cat" using the sum of the vectors "carnivorous" "quadruped" "mammal" and "feline", or subtract the vector "legs" from the vector "reptile" to find an approximation for the vector "snake". Please keep this concept of "directionality" in mind as it is important to understanding how LLMs behave, and it will come up later.
It should come as no surprise that some of the pioneers of this methodology in were also the brains behind Google Translate. You can basically take the embedded vector for "cat" from your English language model and pass it to your Spanish language model to find the vector "gato". Furthermore because all you are really doing is summing and comparing vectors you can do things like sum the vector "gato" in the Spanish model with the vector for the diminutive "-ito" and then pass it back to the English model to find the vector "kitten".
Now if what I am describing does not sound like an LLM to you, that is likely because most publicly available "LLMs" are not just an LLM. They are an LLM plus an additional interface layer that sits between the user and the actual language model. An LLM on its own is little more than a tool that turns words into math, but you can combine it with a second algorithm to do things like take in a block of text and do some distribution analysis to compute the most probable next word. This is essentially what is happening under the hood when you type a prompt into GPT or your assistant of choice.
Our Villain Lorem Epsom, and the Hallucination Problem
I've linked the YouTube video Badness = 0 a few times in prior discussions of AI as I find it to be both a solid introduction to LLMs for the lay-person, and an entertaining illustration of how anthropomorphic bias can cripple the discussion of "alignment". In it the author (who is a professor of Computer Science at Carnegie Mellon) posits a semi-demonic figure (akin to Scott Alexander's Moloch) named Lorem Epsom. The name is a play on the term Lorem Ipsom and represents the prioritization of appearance over all else. When it comes to writing, Lorem Epsom doesn't care about anything except filling the page with text that looks correct. Lorem Epsom is the kind of guy who, if you tell him that he made a mistake in the math, is liable interpret that as a personal attack. The ideas of "accuracy" "logic" "rigor" and "objective reality" are things that Lorem Epsom has heard of but that do not concern Lorem Epsom. It is very possible that you have had to deal with someone like Lorem Epsom in your life (I know I have), now think back and ask yourself how did that go?
I bring up Lorem Epsom because I think that understanding him provides some insight into why certain sorts of people are so easily fooled/taken in by AI Assistants like Claude and Grok. As discussed in the section above on "What is Intelligence", the assumption that the ability to fill a page with text is indicates the ability to perceive and react to a changing situation is an example of anthropomorphic bias. I think that a lot of people assume that because they are posing their question to a computer, they expect the answer they get to be something analogous to what they would get from a pocket calculator rather than from Lorem Epsom.
Sometime circa 2014 I kicked off a heated dispute in the comment section of a LessWrong post by asking EY why a paperclip maximizing AI that was capable of self-modification wouldn't just modify the number of paperclips in its memory. I was accused by him others and a number of others of missing the point, but I think they missed mine. The assumption that an Artificial Intelligence would not only have a notion of "truth", but assign value to it is another example of anthropomorphic bias. If you asked Lorem Epsom to maximize the number of paperclips, and he could theoretically "make" a billion-trillion paperclips simply by manipulating a few bits, why wouldn't he? It's so much more easier than cutting and bending wire.
In order to align an AI to care about truth and accuracy you first need a means of assessing and encoding truth and it turns out that this is a very difficult problem within the context of LLMs, bordering on mathematically impossible. Do you recall how LLMs encode meaning as a direction in n-dimensional space? I told you it was going to come up again.
Directionally speaking we may be able to determine that "true" is an antonym of "false" by computing their dot product. But this is not the same thing as being able to evaluate whether a statement is true or false. As an example "Mary has 2 children", "Mary has 4 children", and "Mary has 1024 children" may as well be identical statements from the perspective of an LLM. Mary has a number of children. That number is a power of 2. Now if the folks programming the interface layer were clever they might have it do something like estimate the most probable number of children based on the training data, but the number simply can not matter to the LLM the way it might matter to Mary, or to someone trying to figure out how many pizzas they ought to order for the family reunion because the "directionality" of one positive integer isn't all that different from any another. (This is why LLMs have such difficulty counting if you were wondering)
In addition to difficulty with numbers there is the more fundamental issue that directionality does not encode reality. The directionality of the statement "Donald Trump is the 47th President of the United States", would be identical regardless of whether Donald Trump won or lost the 2024 election. Directionally speaking there is no difference between a "real" court case and a "fictitious" court case with identical details.
The idea that there is a ineffable difference between true statements and false statements, or between hallucination and imagination is wholly human conceit. Simply put, a LLM that doesn't "hallucinate" doesn't generate text or images at all. It's literally just a search engine with extra steps.
What does this have to do with intelligence?
Recall that I characterized intelligence as a combination of perceptivity and and the ability to react/adapt. "AI assistants" as currently implemented struggle with both. This is partially because LLMs as currently implemented are largely static objects. They are neither able to take in new information, nor discard old. The information they have at time of embedding is the information they have. This imposes substantial loads on the context window of the interface layer, as any ability to "perceive" and subsequently "react" must happen within it's boundaries. Increasing the size of the window is non trivial as the relationship between the size of the window and the amount of memory and the number of FLOPS required is a hyperbolic curve. This is why we saw a sudden flurry of development following the release of Nvidia's multimodal framework and it's mostly been marginal improvements since. The last significant development being June of last year when the folks at Deepseek came up with some clever math to substantially reduce the size of the key value cache, but multiplicative reductions are no match for exponential growth.
This limited context window, coupled with the human tendency to anthropomorphize things is why AI Assistants sometimes appear "oblivious" or "naive" to the uninitiated. and why they seem to "double down" on mistakes. They can not perceive something that they have not been explicitly prompted to even if it is present in their training data. This limited context window is also why if you actually try to play a game of chess with Chat GPT it will forget the board-state and how pieces move after a few turns and promptly lose to a computer program written in 1976. Unlike a human player (or an Atari 2600 for that matter) your AI assistant can't just look at the board (or a representation of the board) and pick a move. This IMO places them solidly on the "insect" side of the perceptivity + reactivity spectrum.
Now there are some who have suggested that the context window problem can be solved by making the whole model less static by continuously updating and re-embedding tokens as the model runs, but I am skeptical that this would result in the sort of gains that AI boosters like Sam Altman claim. Not only would it be computationally prohibitive to do at scale, what experiments there have been (or at least that I am aware of) with self-updating language models, have quickly spun away into nonsense for reasons described in the section on Lorem Epsom., as barring some novel breakthrough in the embedding/tokenization process there is no real way to keep hallucinations and spurious inputs from rapidly overtaking the everything else.
It is already widely acknowledged amongst AI researchers and developers that the LLM-based architecture being pushed by OpenAI and DeepSeek is particularly ill-suited for any application where accuracy and/or autonomy are core concerns, and it seems to me that this unlikely to change without a complete ground-up redesign from first principles.
In conclusion, it is for the reasons above and many others that I do not believe that "AI Assistants" like Grok, Claude, and Gemini represent a viable path towards a "True AGI" along the lines of Skynet or Mr. Data, and if asked "which is smarter, Grok, Claude, Gemini, or an orangutan?" I am going to pick the orangutan every time.
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Notes -
Overall I agree, and think it's an excellent post, but with a few quibbles and thoughts... well, at least "a few" was my intention. I think my thoughts ballooned once I started sketching out some bullet points and an outline, so they are no longer bullet points. I will try to keep each paragraph roughly its own "thought" however.
As an aside, I haven't looked into it enough to tell if an LLM can change tacks and re-organize quite like this, or decide to take unusual approaches once in a while to get a point across. My intuition says that the answer is probably yes to the first, but no to the second, as manifested by the semi-bland outputs that LLMs tend to produce. How often to LLMs spontaneously produce analogies, for example, to get a point across, and carry said analogy throughout the writing? Not that often, but neither do humans I guess - still, less often IME. I think I should come out and say that judging LLM capabilities relative to what we'd expect out of an educated human is the most sensible point of comparison. I don't think it's excessively anthropomorphizing to do so, because we ARE the closest analogue. It also is easier to reason about, and so is useful. Of course it goes without saying that in the "back of your head" you should maintain an awareness that the thought patterns are potentially quite different.
While the current paradigm is next-token-prediction based models, there is such a thing as diffusion text models, which aren't used in the state of the art stuff, but nonetheless work all right. Some of the lessons we are describing here don't generalize to diffusion models, but we can talk about them when or if they become more mainstream. There are a few perhaps waiting in the stables, for example Google semi-recently demoed one. For those not aware, a diffusion model does something maybe, sort of, kind of like how I wrote this comment: sketched out a few bullet points overall, and then refined piece by piece, adding detail to each part. One summary of their strengths and weaknesses here. It's pretty important to emphasize this fact, because arguably our brains work on both levels: we come up with, and crystallize, concepts, in our minds during the "thinking" process (diffusion-like), even though our output is ultimately linear and ordered (and to some extent people think as they speak in a very real way).
So the major quibble pointed out below is that tokenization is a big part of why counting doesn't work as expected. I think it's super critical to state that LLMs ONLY witness the world through the lens of tokens. Yes, humans also do this, but differently (e.g. it's well known that in reading, we sometimes look at the letter that starts and ends the word but the letters in between can sometimes be scrambled without you noticing right away). It's like how a human can only mostly process colors visible to us. There are things that are effectively invisible to an LLM. Even if an LLM is smart enough to disentangle a word into its constituent letters, or a number into its constituent digits, the training data there is pretty weak.
Which leads me to another critical point, not pointed out: LLMs have trouble with things that don't exist in their training data, and actually we have some major gaps there. I'm speaking of things that are intuitive and obvious to people are not always written down, and in fact sometimes the opposite is the case! While an LLM has surely ingested many textbooks on kindergarten basics, it won't have actually experienced a kindergarten classroom. It will learn that kids run inside when it starts to rain, but more weakly learns that kids don't like to get wet. There's also a more limited spatial awareness. Perhaps it's like listening to someone describe the experience of listening to music if you are deaf? That's what a lot of text with implications for real life is like. The LLM has no direct sense at all and is only observing things through knock-on effects.
There are also issues with something that is partially taught but intuitively applied: how much to trust a given source, and what biases they might have. An LLM might read or ingest a document, but not think to consider the source (are they biased? are they an authority figure? are they guessing? all the things an English or history class attempts to teach more explicitly). Nope, it's still just doing next-token prediction on some level, and doesn't have the theory of mind to take a step back from time to time (unless prompted, or trained very explicitly). We can see this weakness manifest where the "grandma trick" is so consistently useful: you tell the LLM that you are some role, and it will believe you. Yes, that's kind of cheating because the trainers of the model don't want the LLM to constantly doubt the prompter, but it's also partly inherent. The LLM doesn't naturally have an instinct to take a step back. Better post-training might help this, but I kind of doubt it, because it won't be as stable as if it's more properly baked into the normal training process.
I've danced around this until now, but want to state this more directly. We are of course critical of how an LLM "thinks" but we don't actually understand quite what happens on a human-cognition level anyways, so we can't actually judge this fairly. Maybe it's closer than we think, but maybe it's farther away. The only way we have of observing human cognition is through inferences from snap judgements, an assortment of experiments, and hints from brain scans as to which regions activate in which scenarios/how strongly/what order. We have some analogous capabilities for LLMs (e.g. observing feature activation such as with Golden Gate Claude besides the usual experiments and even examining output token probability weights). Actually, on that note, I consider at the very least the post summary if not the paper just linked to be mandatory reading for anyone seeking to understand how LLMs function. It's just such a useful experiment and explainer. I will revisit this point, along with how some newer models also employ a "Mixture of Experts" approach, a little later, but for now let's remember that we don't know how humans think on a lower level, so we shouldn't expect too much out of figuring out the machine learning stuff either.
LLM's don't actually learn physics, which has important implications for if we can consider LLMs to have "world models" as they sometimes say. There's a nice 3 minute video accompanying that post. They try and have some vision models learn rules of physics with some very simple circles bouncing around. Obviously something pretty simple. If you give this to a young human, they will make some analogies with the real world, perhaps run an experiment or two, and figure it out pretty quickly as a generalization. We should however state that humans too have some processing quirks and shortcuts used in vision not unlike some of the issues we encounter with tokenization or basic perception, but these are on a different level. They are basic failures to generalize. For example, when referencing training data, it seems to pay attention to things in this order: color > size > velocity > shape. Obviously, that's incorrect. Sometimes shapes will even morph into something else when moving alone! I should disclaim that I don't know a whole lot about the multimodal outputs, though.
There are some evangelists that believe the embedded "concepts", mentioned in the Golden Gate Claude study, are true reasoning. How else, Ilya Sutskever asks, can a model arrive at the correct answer? Honestly as I mentioned referencing how we don't understand how human brains reason completely, I think the jury is out on this one. My guess would be no, however, these concepts aren't full reasoning. They are more like traditional ML feature clusters.
Re: Truth and falsehood. I think there's mild evidence that LLMs do in fact distinguish the two; it's just that these concepts are very fragile especially as compared to humans. I reference to some extent the physics point above: the model doesn't seem to "get" that a shape changing in the middle of an output is a "big deal", but a human would intuitively, without any actual instruction to that effect (instruction also so obvious it might not explicitly be taught in training data). One good piece of evidence for distinguishing true and false is here and related "emergent misalignment" research: how if you fine-tune an LLM to produce insecure (hack-prone) code, it also starts behaving badly in other areas! It will start lying, giving malicious advice, and other "bad" behavior. To me, that suggests that there are a few moral-aligned features or concepts embedded in an LLM's understanding that seem to broadly align with a vague sense of morality and truth. I recognize there's a little conflation there, but why else would an LLM trained on "bad" code start behaving badly in areas that have nothing to do with coding? As evidence for the fragility, however, of true and false, one need only get into a small handful of "debates" with an LLM about what is true and what isn't to see that sometimes it digs in its heels, but other times rolls over belly-up, often seemingly irrationally (as in, it's hard to figure out how hard it will resist).
Circling back to the physics example, causality is something that an LLM doesn't understand, as is its cousin: experimentation. I will grant that humans don't always fully experiment to their full potential, but they do on some level, where LLMs aren't quite there. I posit that a very important part of how humans learn is trying something, and seeing what happens, in all areas! The current LLM pipeline does not allow for this. Agentic behavior is all utilization, and doesn't affect the model weights. Tuning an LLM to work as a chatbot allows the LLM to try and do completion, but doesn't have a component where the LLM will try things out. The closest thing is RLHF and related areas, where the LLM will pick the best of a few options, but this isn't quite organic; the modality of this conversation is fundamentally in a chat paradigm, not the original training paradigm. It's not a true free-form area to learn cause and effect.
Either way, and this is where posts like yours are very, very valuable (along with videos like this, a good use of 3.5 hours if you don't know how they work at all) the point about how LLMs work in layers is absolutely critical; IMO, you cannot have a reasonable discussion about the limits of AI with anyone unless they have at least a general understanding of how the pre-training, training, post-training processes work, plus maybe a general idea of the math. So many "weird" behaviors suddenly start to make sense if you understand a little bit about how an LLM comes to be.
That's not to say that understanding the process is all you need. I mentioned above that some new models use Mixture of Experts, which have a variety of interesting implementations that can differ significantly, and dilute a few of the model-structure implications I just made, though they are still quite useful. I personally need to brush up on the latest a little. But in general, these models seem to "route" a given text into a different subset of features within the neural network model. To some extent these are determined as an architecture choice before training, but often make their influence heard later on (or can even be fine-tuned near the end).
Intelligence. First of all, I think it feels a little silly to have a debate about labels. Labels change according to the needs. Let's not try and pidgeonhole LLMs as they currently are. We can't treat cars like horseless carriages, we can't treat LLMs like humans. Any new tech will usually have at least one major unexpected advantage and one major unexpected shortcoming, and these are really hard to predict.
At the end of the day, I like how one researcher (Andrej Karpathy) puts it: LLMs exhibit jagged intelligence. The contours of what they can and can't do simply don't follow established/traditional paradigms, some capabilities are way better than others, and the consistency varies greatly. I realize that's not a yes/no answer, but it seems to make the most sense, and convey the right intuition and connotation to the median reader.
Overall I think that we do need some major additional "invention" to get something that reflects more "true" intelligence, in the sense we often mean it. One addition, for example, would be to have LLMs have some more agentic behavior earlier in their lifespan, the experimentation and experience aspect. Another innovation that might make a big difference is memory. Context is NOT memory. It's frozen, and it influences outputs only. Memory is a very important part of personality as well as why humans "work"! And LLMs basically do not have any similar capability.
Current "memories" that ChatGPT uses are more like stealth insertion of stuff into the system prompt (which is itself just a "privileged" piece of context) than what we actually mean. Lack of memory causes more obvious and immediate problems, too: when we had Claude Plays Pokemon, a major issue was that Claude (like many LLMs) struggles to figure out which part of its context matters more at any given time. It also is a pretty slapdash solution that gets filled up quickly. Instead of actual memory, Claude is instructed to offload part of what it needs to keep track of to a notepad, but needs to update and condense said notepad regularly because it doesn't have the proper theory of mind to put the right things there, in the right level of detail. And on top of it all, LLMs don't understand spatial reasoning completely, so it has trouble with basic navigation. (There are also some amusing quicks, too: Claude expects people to be helpful, so constantly tries to ask for help from people standing around. It never figures out that the people offer canned phrases that are often irrelevant but occasionally offer a linear perspective on what to do next, and it struggles to contextualize those "hints" when they do come up! He just has too much faith in humanity, haha)
Finally, a difficult question: can't we just ask the LLM itself? No. Human text used for training is so inherently self-reflecting that it's very difficult if not impossible to figure out if the LLM is conscious because we've already explored that question in too much detail and the models are able to fake it too well! We thus have no way to distinguish what's an original LLM thought vs something that its statistical algorithm output. Yes, we have loosely the same problem with humans, too, but humans have limits for what we can hold in our brain at once! (We also see that humans have, arguably, a kind of jagged intelligence too. Why are humans so good at remembering faces, but so bad at remembering names? I could probably come up with a better example but whatever, I'm tired boss). This has implications, I've always thought, for copyright. We don't penalize a human for reading a book, and then using its ideas in a distilled form later. But an LLM can read all the books ever written, and use their ideas in a distilled form later. Does scale matter? Yes, but also no.
Also, how incredibly good the LLM is at going convincingly through the motions without understanding the core reality is coming up all the time these days. When, as linked below, an LLM deletes your whole database, it apologizes and mimics what you'd expect it to say. Fine, okay, arguably you want the LLM to apologize like that, but what if the LLM is put in charge of something real? Anthropic recently put Claude in charge of a vending machine at their work, writeup here, and the failure modes are interesting - and, if you understand the model structure, completely understandable. It convinces itself at one point that it's having a real conversation with someone in the building over restocking plans, and is uniquely incapable of realizing this error and rescuing itself early enough, instead continuing the hallucination for a while before suddenly "snapping" out of a role-play. Perhaps some additional post-training on how its, um, not a real person could reduce the behavior, but the fact it occurs at all demonstrates how out of sample, the LLM has no internal mental representation.
I hate that I feel compelled to nitpick this. But while it's a good layman explanation for how Diffusion models work, the devil is in the details. Diffusion models do not literally, or figuratively diffuse thoughts or progressively clarify ideas. They diffuse noise applied to the input data. They take input data noised according to a fixed schedule and model it as a gaussian distribution which they learn to remove said noise. Since they are an encoder/decoder networks, during inference they take only the decoder (Edit. technically this is incorrect, it's the forward process vs reverse process they aren't explicitly encoder/decoders, its unfortunately how I always remember them), input noise and have it generate output words, text, etc. It is 100% not "thinking" about what it has diffused so far and further diffusing it. It is doing it according to the properties of the noise and the relationship to the schedule it learned during training. It is entirely following a Markovian property; it has no memory of any steps past the immediately previous one, no long-term refinement of ideas. During training it is literally comparing random steps of denoised data with the predicted level of denoising. You can do some interesting things where you add information to the noise via FFT during training and inference to influence the generated output, but as far as I know that's still ongoing research. I guess you could call that noise "Brain thoughts" or something but it is imprecise and very speculative.
Source: 3 years spend doing research on DDIM/DDPMs at work for image generation. I admittedly haven't read the new battery of nlp-aligned diffusion papers (They are sitting in my tabs) but I did read the robotic control paper via diffusion, and it was similar, just abstractions on how the noise is applied to different domains. I'm guessing the NLP ones are similar though probably uses some sort of discrete noise.
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