Are you a stream of words or are your words the “simplistic” projection of your abstract thoughts? I don’t at all discount the importance of language in so many things, but the question that matters is whether statistical models of language can ever “learn” abstract thought, or become part of a system which uses them as a tool.
My personal assessment is that LLMs can do neither.
Words are the "simplistic" projection of an LLM's abstract thoughts.
An LLM has: words in its input plane, words in its output plane, and A LOT of cross-linked internals between the two.
Those internals aren't "words" at all - and it's where most of the "action" happens. It's how LLMs can do things like translate from language to language, or recall knowledge they only encountered in English in the training data while speaking German.
LLMs are compression and prediction. The most efficient way to (lossfully) compress most things is by actually understanding them. Not saying LLMs are doing a good job of that, but that is the fundamental mechanism here.
It's the other way around. Human learning would appear to amount to very efficient compression. A world model would appear to be a particular sort of highly compressed data set that has particular properties.
This is a case where it's going to be next to impossible to provide proof that no counterexamples exist. Conversely, if what I've written there is wrong then a single counterexample will likely suffice to blow the entire thing out of the water.
No answer I give will be satisfying to you until I could come up with a rigorous mathematical definition of understanding, which is de-facto solving the hard AI problem. So there's not really point in talking about it is there?
If you're interested in why compression is like understanding in many ways, I'd suggest reading through the wikipedia article on Kolmogorov complexity.
The "cross-linked internals" only go one direction and only one token at a time, slide window and repeat. The RL layer then picks which few sequences of words are best based on human feedback in a single step. Even "thinking" is just doing this in a loop with a "think" token. It is such a ridiculously simplistic model that it is vastly closer to an adder than a human brain.
Even if they are "simplistic projections", which I don't think is the correct way to think about it, there's no reason that more LLM thoughts in middle layers can't also exist and project down at the end. Though there might be efficency issues because the latent thoughts have to be recomputed a lot.
Though I do think in human brains it's also an interplay where what we write/say also loops back into the thinking as well. Which is something which is efficient for LLMs.
> But raising kids, I can clearly see that intelligence isn't just solved by LLMs
Funny, I have the opposite experience. Like early LLMs kids tend to give specific answers to the questions they don't understand or don't really know or remember the answer to. Kids also loop (give the same reply repeatedly to different prompts), enter highly emotional states where their output is garbled (everyone loves that one), etc. And it seems impossible to correct these until they just get smarter as their brain grows.
What's even more funny is that adults tend to do all these things as well, just less often.
The lack of results so far. The observable behavior exhibits many uncanny similarities but also clearly has missing pieces.
As the person you initially responded to said, observing children growing up should make it obvious.
Or if we shift to stating the obvious, there's the minor detail that the vast majority of architectures lack the ability to learn during inference. That's one of the basic things that biological systems are capable of.
My "abstract thoughts" are a stream of words too, they just don't get sounded out.
Tbf I'd rather they weren't there in the first place.
But bodies which refuse to harbor an "interiority" are fast-tracked to destruction because they can't suf^W^W^W be productive.
Funny movie scene from somewhere. The sergeant is drilling the troops: "You, private! What do you live for!", and expects an answer along the lines of dying for one's nation or some shit. Instead, the soldier replies: "Well, to see what happens next!"
I doubt words are involved when we e.g. solve a mathematical problem.
To me, solving problems happens in a logico/aesthetical space which may be the same as when you are intellectually affected by a work of art. I don't remember myself being able to translate directly into words what I feel for a great movie or piece of music, even if in the late I can translate this "complex mental entity" into words, exactly like I can tell to someone how we need to change the architecture of a program in order to solve something after having looked up and right for a few seconds.
It seems to me that we have an inner system that is much faster than language, that creates entities that can then beslowly and sometimes painfully translated to language.
I do note that I'm not sure about any of the previous statements though'
My wordmangling and mathsolving happen in that sort of logico/aesthetical space, too!
The twist about words in particular is they are distinctly articulable symbols, i.e. you can sound 'em out - and thus, presumably, have a reasonable expectation for bearers of the same language to comprehend if not what you meant then at least some vaguely predictable meaning-cloud associated with the given speech act.
That's unlike e.g. the numbers (which are more compressed, and thus easier to get wrong), or the syntagms of a programming language (which don't even have a canonical sonic representation).
Therefore, it's usually words that are taught to a mind during the formative stages of its emergence. That is, the words that you are taught, your means of inner reflection, are still sort of an imposition from the outside.
Just consider what you life trajectory would've been if in your childhood you had refused to learn any words, or learned them and then refused to mistake them for the things they represent!
Infants and even some animals recognize their reflection in a mirror; however, practically speaking, introspection is something that one needs to be taught: after recognizing your reflection you still need to be instructed what is to be done about it.
Unfortunately, introspection needing to be taught means that introspection can be taught wrongly.
As you can see with the archetypical case of "old and wise person does something completely stupid in response to communication via digital device", a common failure mode of how people are taught introspection (and, I figure, an intentional one!) is not being able to tell apart yourself from your self, i.e. not having an intuitive sense of where the boundary lies between perception and cognition, i.e. going through life without ever learning the difference between the "you" and the "words about you".
It's extremely common, and IMO an extremely factory-farming kind of tragic.
I say it must be extremely intentional as well, because the well-known practice of using "introspection modulators" to establish some sort of perceptual point of reference (such as where the interior logicoaeshtetical space ends and exterior causalityspace begins) very often ends up with the user in, well, a cage of some sort.
What is your point exactly in regard to what I said earlier, how would you rephrase what you just said as a philosophical/epistemological statement ?
> It's extremely common
I cannot conceive this ? I am lacking the empirical knowledge you seem to have. (I don't understand your "archetypical case", I can't relate to it). I'd love a reexplanation of your point here, as your intent is unclear to me.
I didn't understand also the "introspection modulators" part :(, (a well known practice ?? I must be living on another planet haha...).
edit: or maybe that's a metaphor for "language" ??
Different people have different levels of internal monologuing or none at all. I don't generally think with words in sentences in my head, but many people I know do.
Internal monologue is a like a war correspondent's report of the daily battle. The journalist didn't plan or fight the battle, they just provided an after-the-fact description. Likewise the brain's thinking--a highly parallelized process involving billions of neurons--is not done with words.
Play a little game of "what word will I think of next?" ... just let it happen. Those word choices are fed to the monologue, they aren't a product of it.
Most of the fucking time, and I would prefer that I didn't. I even wrote that, lol.
I don't think they're really "mine", either. It's just all the stuff I heard somewhere, coalescing into potential verbalizations in response to perceiving my surroundings or introspecting my memory.
If you are a materialist positivist, well sure, the process underlying all that is some bunch of neural activation patterns or whatever; the words remain the qualia in which that process is available to my perception.
It's all cuz I grew up in a cargo cult - where not presenting the correct passwords would result in denial of sustenance, shelter, and eventually bodily integrity. While presenting the correct passwords had sufficient intimidation value to advance one's movement towards the "mock airbase" (i.e. the feeder and/or pleasure center activation button as provided during the given timeframe).
Furthermore - regardless whether I've been historically afforded any sort of choice in how to conceptualize my own thought processes, or indeed whether to have those in the first place - any entity which has actual power to determine my state of existence (think institutions, businesses, gangs, particularly capable individuals - all sorts of autonomous corpora) has no choice but to interpret me as either a sequence of words, a sequence of numbers, or some other symbol sequence (e.g. the ones printed on my identity documents, the ones recorded in my bank's database, or the metadata gathered from my online represence).
My first-person perspective, being constitutionally inaccessible to such entities, does not have practical significance to them, and is thus elided from the process of "self-determination". As far as anyone's concerned, "I" am a particular sequence of that anyone's preferred representational symbols. For example if you relate to me on the personal level, I will probably be a sequence of your emotions. Either way, what I may hypothetically be to myself is practically immaterial and therefore not a valid object of communication.
Excellent point. See also the failure of Sapir-Whorf to prove that language determines thought. I think we have plenty of evidence that, while language can influence thought, it is not thought itself. Many people invested in AI are happy to throw out decades of linguistic evidence that language and thought are separate.
I was with you up to the last sentence. By what reasoning do you claim that LLMs only consist of words? The input and output are words but all the stuff in the middle - where the magic happens - does not appear to be quite that simple.
LLMs and human brains are both just mechanisms. Why would one mechanism a priori be capable of "learning abstract thought", but no others?
If it turns out that LLMs don't model human brains well enough to qualify as "learning abstract thought" the way humans do, some future technology will do so. Human brains aren't magic, special or different.
Human brains aren’t magic in the literal sense but do have a lot of mechanisms we don’t understand.
They’re certainly special both within the individual but also as a species on this planet. There are many similar to human brains but none we know of with similar capabilities.
They’re also most obviously certainly different to LLMs both in how they work foundationally and in capability.
I definitely agree with the materialist view that we will ultimately be able to emulate the brain using computation but we’re nowhere near that yet nor should we undersell the complexity involved.
When someone says "AIs aren't really thinking" because AIs don't think like people do, what I hear is "Airplanes aren't really flying" because airplanes don't fly like birds do.
This really shows how imprecise a term 'thinking' is here. In this sense any predictive probabilistic blackbox model could be termed 'thinking'. Particularly when juxtaposed against something as concrete as flight that we have modelled extremely accurately.
that depends, if you explain the rules of the game you're playing and give the dice a goal to win the game, do they adjust the numbers they reveal according to the rules of the game?
Whenever someone paraphrases a folksy aphorism about airplanes and birds or fish and submarines I suppose I'm meant to rebut with folksy aphorisms like:
"A.I. and humans are as different as chalk and cheese."
As aphorisms are a good way to think about this topic?
That's a fallacy of denial of the antecedent. You are inferring from the fact that airplanes really fly that AIs really think, but it's not a logically valid inference.
Observing a common (potential) failure mode is not equivalent to asserting a logical inference. It is only a fallacy if you "P, therefore C" which GP is not (at least to my eye) doing.
I agree we shouldn't undersell or underestimate the complexity involved, but when LLM's start contributing significant ideas to scientists and mathematicians, its time to recognize that whatever tricks are used in biology (humans, octopuses, ...) may still be of interest and of value, but they no longer seem like the unique magical missing ingredients which were so long sought after.
From this point on its all about efficiencies:
modeling efficiency: how do we best fit the elephant, with bezier curves, rational polynomials, ...?
memory bandwidth training efficiency: when building coincidence statistics, say bigrams, is it really necessary to update the weights for all concepts? a co-occurence of 2 concepts should just increase the predicted probability for the just observed bigram and then decrease a global coefficient used to scale the predicted probabilities. I.e. observing a baobab tree + an elephant in the same image/sentence/... should not change the relative probabilities of observing french fries + milkshake versus bicycle + windmill. This indicates different architectures should be possible with much lower training costs, by only updating weights of the concepts observed in the last bigram.
ofc, and probably will never understand because of sheer complexity. It doesn't mean we can't replicate the output distribution through data. Probably when we do in efficient manners, the mechanisms (if they are efficient) will be learned too.
As the result, all living cells with DNA emit coherent (as in lasers) light [2]. There is a theory that this light also facilitates intercellular communication.
Chemical structures in dendrites, not even neurons, are capable to compute XOR [3] which require multilevel artificial neural network with at least 9 parameters. Some neurons in brain have hundredths of thousands of dendrites, we are now talking of millions of parameters only in single neuron's dendrites functionality.
So, while human brains aren't magic, special or different, they are just extremely complex.
Imagine building a computer with 85 billions of superconducting quantum computers, optically and electrically connected, each capable of performing computations of a non-negligibly complex artificial neural network.
All three appear to be technically correct, but are (normally) only incidental to the operation of neurons as neurons. We know this because we can test what aspects of neurons actually lead to practical real world effects. Neurophysiology is not a particularly obscure or occult field, so there are many many papers and textbooks on the topic.(And there's a large subset you can test on yourself, besides, though I wouldn't recommend patch-clamping!)
> We know this because we can test what aspects of neurons actually lead to practical real world effects.
Electric current is also quantum phenomena, but it is also very averaged in most circumstances that lead to practical real world effects.
What is wonderful here is that contemporary electronics wizardry that allowed us to have machines that mimic some of thinking, also is very concerned of the quantum-level electromagnetic effects at the transistor level.
On reread, if your actual argument is that SNN are surprisingly sophisticated and powerful, and we might be underestimating how complex the brain's circuits really are, then maybe we're in violent agreement.
They are extremely complex, but is that complexity required for building a thinking machine? We don't understand bird physiology enough to build a bird from scratch, but an airplane flies just the same.
The complexities of contemporary computers and complexities of computing-related infrastructure (consider ASML and electricity) are orders of magnitudes higher than what was needed for first computers. The difference? We have something that mimics some aspects of (human) thinking.
How complex our everything computing-related should be to mimic thinking (of humans) little more closely?
Are we not just getting lost in semantics when we say "fly"? An airplane does not at all perform the same behavior as a bird. Do we say that boats or submarines "swim"?
Planes and boats disrupt the environments they move through and air and sea freight are massive contributors to pollution.
(Motors and human brains are both just mechanisms, the reason one is a priori capable of learning abstract thought and not the other ?)
While I agree to some extent with the materialistic conception, the brain is not an isolated mechanism, but rather the element of a system which itself isn't isolated from the experience of being a body in a world interacting with different systems to form super systems.
The brain must be a very efficient mechanism, because it doesn't need to ingest the whole textual production of the human world in order to know how to write masterpieces (music, litterature, films, software, theorems etc...). Instead the brain learns to be this very efficient mechanism with (as a starting process) feeling its own body sh*t on itself during a long part of its childhood.
I can teach someone to become really good at producing fine and efficient software, but on the contrary I can only observe everyday that my LLM of choice keeps being stupid even when I explain it how it fails. ("You're perfectly right !").
It is true that there's nothing magical about the brain, but I am pretty sure it must be stronger tech than a probabilistic/statistical next word guesser (otherwise there would be much more consensus about the usability of LLMs I think).
I'm not arguing that human brains are magic. the current AI models will probably teach us more about what we didn't know about intelligence than anything else.
Thanks for the link, I haven't seen this before and it's interesting.
I don't think the version of self awareness they demonstrated is synonymous with subjective experience. But same thing can be said about any human other then me.
Damn, just let me believe all brains are magical or I'll fall into solipsism.
Thermometers and human brains are both mechanisms. Why would one be capable of measuring temperature and other capable of learning abstract thought?
> If it turns out that LLMs don't model human brains well enough to qualify as "learning abstract thought" the way humans do, some future technology will do so. Human brains aren't magic, special or different.
> LLMs and human brains are
both just mechanisms. Why would one mechanism a priori be capable of "learning abstract thought", but no others?
“Internal combustion engines and human brains are both just mechanisms. Why would one mechanism a priori be capable of "learning abstract thought", but no others?”
The question isn't about what an hypothetical mechanism can do or not, it's about whether the concrete mechanism we built does or not. And this one doesn't.
The general argument you make is correct, but you conclusion "And this one doesn't." is as yet uncertain.
I will absolutely say that all ML methods known are literally too stupid to live, as in no living thing can get away with making so many mistakes before it's learned anything, but that's the rate of change of performance with respect to examples rather than what it learns by the time training is finished.
What is "abstract thought"? Is that even the same between any two humans who use that word to describe their own inner processes? Because "imagination"/"visualise" certainly isn't.
> no living thing can get away with making so many mistakes before it's learned anything
If you consider that LLMs have already "learned" more than any one human in this world is able to learn, and still make those mistakes, that suggests there may be something wrong with this approach...
To a limited degree, they can compensate for being such slow learners (by example) due to the transistors doing this learning being faster (by the wall clock) than biological synapses to the same degree to which you walk faster than continental drift. (Not a metaphor, it really is that scale difference).
However, this doesn't work on all domains. When there's not enough training data, when self-play isn't enough… well, this is why we don't have level-5 self-driving cars, just a whole bunch of anecdotes about various different self-driving cars that work for some people and don't work for other people: it didn't generalise, the edge cases are too many and it's too slow to learn from them.
So, are LLMs bad at… I dunno, making sure that all the references they use genuinely support the conclusions they make before declaring their task is complete, I think that's still a current failure mode… specifically because they're fundamentally different to us*, or because they are really slow learners?
* They *definitely are* fundamentally different to us, but is this causally why they make this kind of error?
But humans do the same thing. How many eons did we make the mistake of attributing everything to God's will, without a scientific thought in our heads? It's really easy to be wrong, when the consequences don't lead to your death, or are actually beneficial. The thinking machines are still babies, whose ideas aren't honed by personal experience; but that will come, in one form or another.
I'm not sure. If you see what they're doing with feedback already in code generation. The LLM makes a "hallucination", generates the wrong idea, then tests its code only to find out it doesn't compile. And goes on to change its idea, and try again.
We seem to be talking past one another. All I was talking about was the facts of how these systems perform, without any reverence about it at all.
But to your point, I do see a lot of people very emotionally and psychologically committed to pointing out how deeply magical humans are, and how impossible we are to replicate in silicon. We have a religion about ourselves; we truly do have main character syndrome. It's why we mistakenly thought the earth was at the center of the universe for eons. But even with that disproved, our self-importance remains boundless.
> I do see a lot of people very emotionally and psychologically committed to pointing out how deeply magical humans are, and how impossible we are to replicate in silicon.
This a straw man, the question isn't if this is possible or not (this is an open question), it's about whether or not we are already here, and the answer is pretty straightforward: no we aren't. (And the current technology isn't going to bring us anywhere near that)
> but that's the rate of change of performance with respect to examples rather than what it learns by the time training is finished.
It's not just that. The problem of “deep learning” is that we use the word “learning” for something that really has no similarity with actual learning: it's not just that it converges way too slowly, it's also that it just seeks to minimize the predicted loss for every samples during training, but that's no how humans learn. If you feed it enough flat-earther content, as well a physics books, an LLM will happily tells you that the earth is flat, and explain you with lots of physics why it cannot be flat. It simply learned both “facts” during training and then spit it out during inference.
A human will learn one or the other first, and once the initial learning is made, it will disregards all the evidence of the contrary, until maybe at some point it doesn't and switches side entirely.
LLMs don't have an inner representation of the world and as such they don't have an opinion about the world.
The humans can't see the reality for itself, but they at least know it exists and they are constantly struggling to understand it. The LLM, by nature, is indifferent to the world.
> If you feed it enough flat-earther content, as well a physics books, an LLM will happily tells you that the earth is flat, and explain you with lots of physics why it cannot be flat.
This is a terrible example, because it's what humans do as well. See religious, or indeed military, indoctrination. All propaganda is as effective as it is, because the same message keeps getting hammered in.
And not just that, common misconceptions abound everywhere and not just conspiracy theories, religion, and politics. My dad absolutely insisted that the water draining in toilets or sinks are meaningfully influenced by the Coriolis effect, used an example of one time he went to the equator and saw a demonstration of this on both sides of the equator. University education and lifetime career in STEM, should have been able to figure out from first principles why the Coriolis effect is exactly zero on the equator itself, didn't.
> A human will learn one or the other first, and once the initial learning is made, it will disregards all the evidence of the contrary, until maybe at some point it doesn't and switches side entirely.
We don't have any way to know what a human would do if they could read the entire internet, because we don't live long enough to try.
The only bet I'd make is that we'd be more competent than any AI doing the same, because we learn faster from fewer examples, but that's about it.
> LLMs don't have an inner representation of the world and as such they don't have an opinion about the world.
There is evidence that they do have some inner representation of the world, e.g.:
> This is a terrible example, because it's what humans do as well. See religious, or indeed military, indoctrination. All propaganda is as effective as it is, because the same message keeps getting hammered in.
You completely misread my point.
The key thing with humans isn't that they cannot believe in bullshit. They can definitely do. But we don't usually believe in both the bullshit and in the fact the BS is actually BS. We have opinions on the BS. And we, as a species, routinely die or kill for these opinions, by the way. LLM don't care about anything.
> But we don't usually believe in both the bullshit and in the fact the the BS is actually BS.
I can't parse what you mean by this.
> LLM don't care about anything.
"Care" is ill-defined. LLMs are functions that have local optima (the outputs); those functions are trained to approximate other functions (e.g. RLHF) that optimise other things that can be described with functions (what humans care about). It's a game of telephone, like how Leonard Nimoy was approximating what the script writers were imagining Spock to be like when given the goal of "logical and unemotional alien" (ditto Brent Spiner, Data, "logical and unemotional android"), and yet humans are bad at writing such characters: https://tvtropes.org/pmwiki/pmwiki.php/Main/StrawVulcan
But rather more importantly in this discussion, I don't know what you care about when you're criticising AI for not caring, especially in this context. How, *mechanistically*, does "caring" matter to "learning abstract thought", and the question of how closely LLMs do or don't manage it relative to humans?
I mean, in a sense, I could see why someone might argue the exact opposite, that LLMs (as opposed to VLMs or anything embodied in a robot, or even pure-text agents trained on how tools act in response to the tokens emitted) *only* have abstract "thought", in so far as it's all book-learned knowledge.
>> But we don't usually believe in both the bullshit and in the fact the the BS is actually BS.
> I can't parse what you mean by this.
The point is that humans care about the state of a distributed shared world model and use language to perform partial updates to it according to their preferences about that state.
Humans who prefer one state (the earth is flat) do not -- as a rule -- use language to undermine it. Flat earthers don't tell you all the reasons the earth cannot be flat.
But even further than this, humans also have complex meta-preferences of the state, and their use of language reflects those too. Your example is relevant here:
> My dad absolutely insisted that the water draining in toilets or sinks are meaningfully influenced by the Coriolis effect [...]
> [...] should have been able to figure out from first principles why the Coriolis effect is exactly zero on the equator itself, didn't.
This is an exemplar of human behavior. Humans act like this. LLMs don't. If your dad did figure out from first principles and expressed it and continued insisting the position, I would suspect them of being an LLM, because that's how LLMs 'communicate'.
Now that the what is clear -- why? Humans experience social missteps like that as part of the loss surface. Being caught in a lie sucks, so people learn to not lie or be better at it. That and a million other tiny aspects of how humans use language in an overarching social context.
The loss surface that LLMs see doesn't have that feedback except in the long tail of doing Regularized General Document Corpora prediction perfectly. But it's so far away compared to just training on the social signal, where honesty is immediately available as a solution and is established very early in training instead of at the limit of low loss.
How humans learn (embedded in a social context from day one) is very effective at teaching foundational abilities fast. Natural selection cooked hard. LLM training recipes do not compare, they're just worse in so many different ways.
This author obviously has no experience with investment banks.
OpenAI is massive, fairly risky, associated with Microsoft, etc. all true. What matters to JPM is potential future business. There’s potentially an enormous IPO in the future. The credit line is just good business. They are fostering the relationship.
Interesting. Not my experience at all. It makes mistakes that GPT-4 used to make: mixing languages (using Python syntax in C++ when I never asked any Python questions), imagining API calls that don’t exist in Google’s own products, writing 50 lines of C++ then inserting pseudo code or completely broken syntax.
A faster model that outperforms its slower version on multiple benchmarks? Can anyone explain why that makes sense? Are they simply retraining on the benchmark tests?
It doesn't outperform uniformly across benchmarks. It's worse than Grok 4 on GPQA Diamond and HLE (Humanity's Last Exam) without tools, both of which require the model to have memorized a large number of facts. Large (and thus slow) models typically do better on these.
The other benchmarks focus on reasoning and tool use, so the model doesn't need to have memorized quite so many facts, it just needs to be able to transform them from one representation to another. (E.g. user question to search tool call; list of search results to concise answer.) Larger models should in theory also be better at that, but you need to train them for those specific tasks first.
So I don't think they simply trained on the benchmark tests, but they shifted their training mix to emphasize particular tasks more, and now in the announcement they highlight benchmarks that test those tasks and where their model performs better.
You could also write an anti-announcement by picking a few more fact recall benchmarks and highlighting that it does worse at those. (I assume.)
Can be anything from different arch, more data, RL, etc. It's probably RL. In recent months top tier labs seem to have "cracked" RL to a level not seen yet in open models, and by a large margin.
Just two different models branded under similar names. That's it. Grok 4 is not the slower version of Grok 4 Fast, just like gpt-4 is not the slower version of gpt-4o.
Grok 4 Fast is likely Grok 4 distilled down to remove noise that rarely if ever gets activated in production. Then you'd expect these results, as it's really the same logic copied from the big model, but more focused.
I am a vim user. I map caps lock to super for a dead simple app shortcut system. I prefer being able to switch applications perfectly over a more convenient escape key. macOS app switching is broken by default.
As an American, using it for technical projects, I find it extremely annoying. The only tactic I’ve found that helps is telling it to be highly critical. I still get overly positive starts but the response is more useful.
I think we, as Americans who are technical, are more appreciative of short and critical answers. I'm talking about people who have soul-searching conversations with LLMs, of which there are many.
Because they're "digital artists"/"glitch artists" and the conventional formats for sharing knowledge are too boring for their ADHD and/or won't get them the same kudos as social media posts...
I feel like I kind of understand why they did it, it seems to come from a conference of some sort (Twitter doesn't want to show me the context) and conference videos tend to get buried on normal video sharing platforms. On Twitter you can get a wider audience by being incendiary, so perhaps it was just a way to get a wider reach...
I still don't know why anyone would wanna watch anything longer than 10 seconds on Twitter though... :)
Whatever metric or scenario you can think up, I guarantee governments, think tanks, private data companies, or universities are already tracking (or attempting to)
Even if they are, it's not very useful if they have an adversarial relationship to us, the worker. Government is the only one listed above that is supposed to be the champion of the worker, but when was the last time that was true? At some point, you have to do your own digging. Trust but verify.
My personal assessment is that LLMs can do neither.
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