What is intelligence? (2024)
whatisintelligence.antikythera.org
whatisintelligence.antikythera.org
The effect is that it's unclear at first glance what the argument even might be, or which sections might be interesting to a reader who is not planning to read it front-to-back. And since it's apparently six hundred pages in printed form, I don't know that many will read it front-to-back either.
* Verifiable Fact
* Obvious Truth
* Widely Held Opinion
* Your Nonsense Here
* Tautological Platitude
This gets your audience nodding along in "Yes" mode and makes you seem credible so they tend to give you the benefit of the doubt when they hit something they aren't so sure about. Then, before they have time to really process their objection, you move onto and finish with something they can't help but agree with.
The stuff on the history of computation and cybernetics is well researched with a flashy presentation, but it's not original nor, as you pointed out, does it form a single coherent thesis. Mixing in all the biology and movie stuff just dilutes it further. It's just a grab bag of interesting things added to build credibility. Which is a shame, because it's exactly the kind of stuff that's relevant to my interests[3][4].
> "Your manuscript is both good and original; but the part that is good is not original, and the part that is original is not good." - Samuel Johnson
The author clearly has an Opinion™ about AI, but instead of supporting they're trying to smuggle it through in a sandwich, which I think is why you have that intuitive allergic reaction to it.
[1]: https://changingminds.org/disciplines/sales/closing/yes-set_...
[2]: https://en.wikipedia.org/wiki/Compliment_sandwich
Personally, I think the book does not add anything novel. Reading Karl Friston and Andy Clark would be a better investment of time if the notion of predictive processing seems interesting to you.
Dive into the Mindscape podcast, investigate complex systems. Go into information theory. Look at evolution from an information theory perspective. Look at how intelligence enables (collective) modeling of likely local future states of the universe, and how that helps us thrive.
Don’t get caught in what at least I consider to be a trap: “to use your consciousness to explain your consciousness”. I think the jump is, for now, too large.
Just my 2 ct. FWIW I consider myself a cocktail philosopher. I do have a PhD in Biophsyics, it means something to some. Although I myself consider it of limited value.
The linked multimedia article gives a narrative of intelligent systems, but Hutter and AIXI give a (noncomputable) definition of an ideal intelligent agent. The book situates the definitions in a reinforcement learning setting, but the core idea is succinctly expressed in a supervised learning setting.
The idea is this: given a dataset with yes/no labels (and no repeats in the features), and a commonsense encoding of turing machines as a binary string, the ideal map from input to probability distribution model is defined by
1. taking all turing machines that decide the input space and agree with the labels of the training set, and
2. the inference algorithm is that on new input, the output is exactly the distribution by counting all such machines that assent vs. reject the input, with their mass being weighted by the reciprocal of 2 to the power of the length, then the weighted counts normalized. This is of course a noncomputable algorithm.
The intuition is that if a simply-patterned function from input to output exists in the training set, then there is a simply/shortly described turing machine that captures that function, and so that machine's opinion on the new input is given a lot of weight. But there exist plausible more complex patterns, and we also consider them.
What I like about this abstract definition is that it is not in reference to "human intelligence" or "animal intelligence" or some other anthropic or biological notion. Rather, you can use these ideas anytime you isolate a notion of agent from an environment/data, and want to evaluate how the agent interacts/predicts intelligently against novel input from the environment/data, under the limited input that it has. It is a precise formalization of inductive thinking / Occam's razor.
Another thing I like about this is that it gives theoretical justification for the double-descent phenomenon. It is a (noncomputable) algorithm to give the best predictor, but it is defined in reference to the largest hypothesis space (all turing machines that decide on the input space). It suggests that whereas prior ML methods got better results with architectures that are carefully designed to make bad predictors unrepresentable, it is also not idle, if you have a lot of computational resources, to have an architecture that defines an expressive hypothesis space, and instead softly prioritizing simpler hypotheses through the learning algorithms (e.g. an approximation of which is regularization). This allows your model to learn complex patterns defined by the data that you did not anticipate, if that evidence in the data justifies it, whereas a small, biased hypothesis space would not be able to represent such a pattern if not anticipated but significant.
Note that under this definition, you might want to talk about a situation where the observations are noisy but you want to learn the trend of it without the noise. You can adapt the definition to be over noisy input by for example accompanying each input with distinct sequence numbers or random salts, then consider the marginal distribution for numbers/salts not in the training set (there are some technical issues of convergence, but the general approach is feasible), and this models the noise distribution as well.
Why not answer the question?
And looking at your paragraphs I'm still not sure I see a definition of intelligence. Unless you just mean that intelligence is something that can approximate this algorithm?
I don’t know how much I buy the idea that intelligence maximizes parsimony. Certainly true for inductive reasoning but I feel like there’s some tradeoff here. There are probably cases where a small TM explains a very large but finite set of observations, but if a few new ones are added the parsimonious explanation becomes much longer and looks much different from the previous one. I know this wouldn’t be under the same assumptions as the book though :p
> There are probably cases where a small TM explains a very large but finite set of observations, but if a few new ones are added the parsimonious explanation becomes much longer and looks much different from the previous one.
Indeed, to use an analogy, if you have 99 points that can be described perfectly by a linear function except for one outlier, then clearly your input isn't as clear-cut as might have been originally assumed.
On the other hand, you may be in a different setting where you have noisy sensor inputs and you expect some noise, and are looking for a regression that tolerates some noise. In such a situation, only when the stars align perfectly would your input data be perfectly described by a linear function, and we just have to accept that a broken watch is perfectly right twice a day whereas a working one is almost always only approximately right, but all the time.
All this to say I hope there are some gaps in our theoretical understanding of true AI, otherwise I wouldn’t be able to make a living filling them in
> is that true intelligence might not have these big gaps between explanation-lengths that parsimonious TMs do.
I don't know the field and literature well enough to know if this is the case, is there a published result you can point me to?
> And there’s also the question of deduction; having a few redundant “theorems” on hand might make deductive inferences more efficient whereas parsimony would elide them.
Especially with the words "redundant", "deductive", and "efficient", it sounds to me that you have in mind something like CDCL SAT solvers learning redundant conflict clauses that help prune the search space. In respect to this recall that the AIXI definition/Solomonoff induction definition is noncomputable and so doesn't have a notion of efficiency.
Indeed, some optimally parsimonious TMs for some inputs are not going to meet fixed resource bounds on part of the input. Intuitively if you are concerned about a finite part of the input space, you can just tack them on to the definition of the TM to obtain a TM that has good efficiency on that finite space, at the cost of definitional parsimony. Possibly something in-between for particular infinite spaces exist (dovetailing with a more complex TM with better runtime characteristics that agrees on that space?) and I wonder if there might very well be an efficient frontier of parsimony against say time complexity.
> efficient frontier of parsimony against say time complexity.
As you mentioned before regularization approximates parsimony, could it be that what’s gained from this loss of precision wrt parsimony are runtime guarantees (since now we’re mostly talking about constant depth circuit-esque DL architectures)? Or is the jump to continuous spaces more relevant? Are these the same?
I’ll have to read up more to see
There was a concept buried in it that before a system evolves replication it first will learn how to copy badly, then better, then replicate. This might feel a minor and obvious statement, but I have never seen it called out before and it is a concept I have discussed many times with people since. Some obvious things such as if I want a system to obtain replication my initial selection filter should be for copying. I am able to induce replication in a system for less input energy via this process. But this can also be flipped around and being hyper aware of systems that are copying badly knowing they are much more likely to phase shift to full replication shortly. I see this everywhere from ideas, startups, and even in finance.
And to nerd snipe everyone here, I spotted a bug in the brainfuck which is still on the online copy, can you find it?
It seems to me as a mereological nihilist that meaning primitives are state changes.
The existence of syntatic meaning depends on state changes.
The existence of semantic meaning depends on syntax.
The existence of complex meaning generators like humans is dependent on less complex meaning in DNA and RNA.
We are meaning generators, we are the empirical generators of the most complex observed meaning and you can follow the causal chain all the way to state change.
I don't know if the universe has an ultimate meaning, but I suspect that if we don't wipe ourselves out we'd be part of the causual chain that creates and/or discovers it.
All the particles and information is all tied to the systems/universes entropy so there is a timer on how long we have, but in the meantime.
We emit meaning like stars emit photons. We are the complex meaning generators of the universe. Maybe there are others out there but we haven't met them yet, and until we do we should take precaution.
What he would like people to believe is that AI is real intelligence, for some value of real.
Even without AI, computers can be programmed for a purpose, and appear to exhibit intelligence. And mechanical systems, such as the governor of a lawnmower engine, seem able to seek a goal they are set for.
What AI models have in common with human and animal learning is having a history which forms the basis for a response. For humans, our sensory motor history, with its emotional associations, is an embodied context out of which creative responses derive.
There is no attempt to recreate such learning in AI. And by missing out on embodied existence, AI can hardly be claimed as being on the same order as human or animal intelligence.
To understand the origin of human intelligence, a good starting point would be, Ester Thelen's book[0], "A Dynamic Systems Approach to the Development of Cognition and Action" (also MIT Press, btw.)
According to Thelen, there is no privileged component with prior knowledge of the end state of an infant's development, no genetic program that their life is executing. Instead, there is a process of trial and error that develops the associations between senses and muscular manipulation that organize complex actions like reaching.
If anything, it is caregivers in the family system that knowledge of an end result resides: if something isn't going right with the baby, if she not able to breastfeed within a few days of birth (a learned behavior) or not able to roll over by themselves at 9 months, they will be ones to seek help.
In my opinion, it is in the caring arts, investing time in our children's development and education, that advances us as a civilization, although there is now a separate track, the advances in computers and technology, that often serves as a proxy for improving our culture and humanity, easier to measure, easier to allocate funds, than for the squishy human culture of attentive parenting, teaching and caregiving.
[0] https://www.amazon.com/Approach-Development-Cognition-Cognit...
Note that you'd also have to be somewhat more precise as to what the "state" and "next state" are. It is likely that the state is everything that enters the brain (i.e. by means of sensing, such as what we see, hear, feel, introspect, etc.). However, parts of this state enter the brain at various places and at various frequencies. Abstracting that all away might be problematic.
One example of this I often ponder is the boxing style of Muhammad Ali, specifically punching while moving backwards. Before Ali, no one punched while moving away from their opponent. All boxing data said this was a weak position, time for defense, not for punching (offense). Ali flipped it. He used to do miles of roadwork, throwing punches while running backwards to train himself on this style. People thought he was crazy, but it worked, and, imho, it was extremely creative (in the context of boxing), and therefore intelligent.
Did data exist that could've been analyzed (by an AI system) to come up with this boxing style? Perhaps. Kung Fu fighting styles have long known about using your opponents momentum against them. However, I think that data (Kung Fu fighting styles) would've been diluted and ignored in face of the mountains of traditional boxing style data, that all said not to punch while moving backwards.
I would argue that the only truly new things generative AI has introduced are mostly just byproducts of how the systems are built. The "AI style" of visual models, the ChatGPT authorial voice, etc., are all "new", but they are still just the result of regurgitating human created data and the novelty is an artifact of the model's competence or lack thereof.
There has not been, at least to my knowledge, a truly novel style of art, music, poetry, etc. created by an AI. All human advancements in those areas build mostly off of previous peoples' work, but there's enough of a spark of human intellect that they can still make unique advancements. All of these advancements are contingent rather than inevitable, so I'm not asking that an LLM, trained on nothing but visual art from the Medieval times and before, could recreate Impressionism. But I don't think it would make anything the progresses past or diverges from Medieval and pre-Medieval art styles. I don't think an LLM with no examples of or references to anything written before 1700 would ever produce poetry that looked like Ezra Pound's writing, though it just might make its own Finnegan's Wake if the temperature parameter were turned out high enough.
And how could it? It works because there's enough written data that questions and context around the questions are generally close enough to previously seen data that the minor change in the question will be matched by a commensurate change in the correct response from the ones in the data. That's all a posteriori!
I would have agreed with you at the dawn of LLM emergence, but not anymore. Not because the models have improved, but because I have a better understanding and more experience now. Token prediction is what everyone cites, and it still holds true. This mechanism is usually illustrated with an observable pattern, like the question, "Are antibiotics bad for your gut?" which is the predictability you mentioned. But LLM creativity begins to emerge when we apply what I’d call "constraining creativity." You still use token prediction, but the preceding tokens introduce an unusual or unexpected context - such as subjects that don't usually appear together or a new paradoxical observation (It's interesting that for fact-based queries, rare constraints lead to hallucinations, but here they're welcome)
I often use the latter for fun by asking an LLM to create a stand-up sketch based on an interesting observation I noticed. The results aren’t perfect, but they combine the unpredictability of token generation under constraints (funny details, in the case of the sketch) with the cultural constraints learned during training. For example, a sketch imagining doves and balconies as if they were people and real estate. The quote below from that sketch show that there are intersecting patterns between the world of human real estate and the world of birds, but mixed in a humorous way.
"You want to buy this balcony? That’ll be 500 sunflower seeds down, and 5 seeds a day interest. Late payments? We send the hawk after you."But there are real splits on substrate dependence and what actually drives the system. Can you get intelligence from pure prediction, or does it need the pressure of real consequences? And deeper: can it emerge from computational principles alone, or does it require specific environmental embeddedness?
My sense is that execution cost drives everything. You have to pay back what you spend, which forces learning and competent action. In biological or social systems you're also supporting the next generation of agents, so intelligence becomes efficient search because there's economic pressure all the way down. The social bootstrapping isn't decorative, it's structural.
I also posted yesterday a related post on HN
> What the Dumpster Teaches: https://news.ycombinator.com/item?id=45698854
It really is a stupid system. No one rational wants to hear that, just like no one religious wants to hear contradictions in their stories, or no one who plays chess wants to hear its a stupid game. The only thing that can be said about the chimp intelligence is it has developed a hatred of contradictions/unpredictability and lack of control unseen in trees, frogs, ants and microbes.
Stories becomes central to survive such underlying machinery. Part of the story we tell is no no we don't all have to be Kant or Einstein because we just absorb what they uncovered. So apparently the group or social structures matters. Which is another layer of pure hallucination. All social structures if they increase the prediction horizon also generate/expose themselves to more prediction errors and contradictions not less.
So again Coherence at group level is produced through story - religion will save us, the law will save us, trump will save, the jedi will save us, AI will save us etc. We then build walls and armies to protect ourselves from each others stories. Microbes don't do this. They do the opposite and have produced the krebs cycle, photosynthesis, crispr etc. No intelligence. No organization.
Our intelligence are just bubbling cauldrons at the individual and social level through which info passes and mutates. Info that survives is info that can survive that machinery. And as info explodes the coherence stabilization process is over run. Stories have to be written faster than stories can be written.
So Donald Trump is president. A product of "intelligence" and social "intelligence". Meanwhile more microbes exist than stars in the universe. No Trump or ICE or Church or data center is required to keep them alive.
If we are going to tell a story about Intelligence look to Pixar or WWE. Don't ask anyone in MIT what they think about it.
Also evolution is the original information-processing engine, and humans still run on it just like microbes. The difference is just the clock speed. Our intelligence, though chaotic and unstable, operates on radically faster time and complexity scales. It's an accelerator that runs in days and months instead of generations. The instability isn’t a flaw: it’s the turbulence of the way faster adaptation.
I think we need a meta layer - ability to reason over one's own goals (this does not contradict the environment creating hard constraints). The man has it. The machine may have it (notably a paperclip maximizer will not count under this criteria). The crow does not.
Similarly, a machine could emulate meta-cognition, but it would in effect only be an reflection and embodiment of certain meta-cognitive processes originally instantiated in the mind which created that machine.
IIRC the researcher was Hugo Mercier, probably on Sean Carroll’s fantastic Mindscape podcast, but it might have been Lex Fridman before he strayed from science/tech.
"reasoning evolved not to complement individual cognition but as an argumentative device" -- and it has more positive effects at social level than at individual level
https://www.dan.sperber.fr/wp-content/uploads/2009/10/Mercie...
Why do humans reason? Arguments for an argumentative theory
Now it raises the question should we be reasoning in our head then? Is there a better way to solve intractable math problems for example? Is math itself a red herring created for argumentative purposes?
We can never know, but I personally favour the rise of "handedness" and the tool-making (technological) hypothesis. To make and use tools, and to transfer the recipes and terminology, we must educate one another.
"In the physical adaptation view, one function (producing speech sounds) must have been superimposed on existing anatomical features (teeth, lips) previously used for other purposes (chewing, sucking). A similar development is believed to have taken place with human hands and some believe that manual gestures may have been a precursor of language. By about two million years ago, there is evidence that humans had developed preferential right-handedness and had become capable of making stone tools. Tool making, or the outcome of manipulating objects and changing them using both hands, is evidence of a brain at work." [1]
[1] _ https://assets.cambridge.org/97811084/99453/excerpt/97811084...
If by “truth” you mean more like Kants “the thing in itself”, then the problem there is we need abstraction. If I show you how to make an arrowhead, somehow I need to convey that you can follow the same process with your own piece of flint, and that they are both arrowheads. Without any language abstraction my arrowhead and your arrowhead are just two different rocks with no relation to each other.
Think about it, if all reasoning is post-hoc rationalization, reasons are useless. Imagine a mentally ill person on the street yelling at you as you pass by: you're going to ignore those noises, not try to interpret their meaning and let them influence your beliefs.
This theory is too cynical. The real answer has got to have some element of "reasoning is useful because it somehow improves our predictions about the world"
And it's also important that language and words are just another invention of humanity. We achieved plenty before we had any meaningful language whatsoever. At the bare minimum we reproduced - and think about all that's involved in forming a relationship, having a child, and then successfully raising that child for them to go on and do the same, all without any sort of language. It emphasizes that ideas and language are two very different concepts, at least for humans.
The other thing is their inability to intelligently forget and their inability to correctly manage their own context by building their own tools (some of which is labs intentionally crippling how they build AI to avoid an AI escape).
I don’t think there’s anything novel in human intelligence as a good chunk of it does appear in more primitive forms in other animals (primates, elephants, dolphins, cepholapods). But generally our intelligence is on hyperdrive because we also have the added physical ability of written language and the capability for tool building.
https://chatgpt.com/share/68fd15af-ee48-8010-8269-e10f518a3a...
Then, what is what we are incapable of? Magic? ;-)
> Maybe the only thing we can do is advanced pattern matching
Pattern matching as a way to support the excellent heuristic "correlation is likely causation", yes. This is what allows us to analyze systems, what brings us from "something thrown away will eventually fall to the ground" to the theory to relativity.
Intelligence is understanding, and understanding comes from hacking systems in order to use them to our advantage - or just observe systems being broken or being built.
By doing that, we acquire more knowledge about the relationships and entities within the system, which in turn allows more advanced hacking. We probably started with fire, wolves, wheat, flint; and now we are considering going to Mars.
The main thesis seems to be "the brain evolved precisely to predict the future—the “predictive brain” hypothesis."
Which I guess is ok although we can do other stuff - write stories, play the piano and so on. Also:
>What Is Intelligence? argues—quite against the grain—that certain modern AI systems do indeed have a claim to intelligence, consciousness, and free will.
Even if we accept that premise, taking an evolutionary perspective means acknowledging that the brain could, in the future, evolve toward other dominant traits besides prediction. In that sense, the definition becomes elusive and time-dependent: what we call the brain's "purpose" today might only describe a temporary evolutionary state rather than a fixed function.
Putting that to the side - i don’t think I’ll read this fully soon, but the core thesis of “imitation is intelligence” can be easily disproven by a process that exists in society. An actor acting to be a genius is in fact, if they are a good actor, indistinguishable in their appearance to a genius. Yet they are not, in fact, a genius, they’re just good at memorisation. This is a clear showcase that imitation of a level of intelligence does not mean that this level of intelligence is present.
We have fallen into a trap of thinking that answering in plausible sentences is what makes humans intelligent. While in reality we are observing an actor responding from an infinitely large script. What makes humans intelligent (reasoning from first principles and pattern recognition across all the sensory inputs of the world) is still very much out of grasp.
An actor will be distinguishable from a genius in their ability to answer questions and generate new insights. If the imitation was actually perfect, the actor would be able to do these things, and would in fact be a genius.
When I write prompts, I've stopped thinking of LLMs as just predicting a next word, and instead to think that they are a logical model built up by combining the logic of all the text they've seen. I think of the LLM as knowing that cats don't lay eggs, and when I ask it to finish the sentence "cats lay ..." It won't generate the word eggs even though eggs probably comes after lay frequently
What you are seeing is a semi-randomized prediction engine. It does not "know" things, it only shows you an approximation of what a completion of its system prompt and your prompt combined would look like, when extrapolated from its training corpus.
What you've mistaken for a "logical model" is simply a large amount of repeated information. To show the difference between this and logic, you need only look at something like the "seahorse emoji" case.
https://www.analyticsvidhya.com/blog/2021/07/word2vec-for-wo...
A markov chain presents the same representation in a lower vector space.
Surely trained neural networks could never develop circuits that implement actual logic via computational graphs...
https://transformer-circuits.pub/2025/attribution-graphs/met...
So what is it repeating?
It's not enough to just point to an instance of LLMs producing weird or dumb output. You need to show how it fits with your theory that they "just repeating information". This is like pointing out one of the millions of times a person has said something weird, dumb, or nonsensical and claiming it proves humans can't think and can only repeat information.
But it's not doing that. It's just replacing a relation in vector space with one that we would think is distant.
Of course you would view an LLM's behavior as mystifying and indicative of something deeper when you do not know what it is doing. You should seek to understand something before assigning mysterious capabilities to it.
You just pointed out an example of LLMs screwing up and then skipped right to "therefore they're just repeating information" without showing this is what your explanation predicts.
> What you've mistaken for a "logical model" is simply a large amount of repeated information.
If you'd like to explain why "What you've mistaken for a 'logical model' is simply a large amount of repeated information." actually means something else, or why you think I've misinterpreted it, be my guest.
> It won't generate the word eggs even though eggs probably comes after lay frequently
Next token prediction is still context based. It does not depend on only the previous token, but on the previous (N-1) tokens. You have "cat" so you should get words like "down" instead of "eggs" with even a 3-gram (trigram) model.
I'll also add that a lot of people really binarize things. Although there is not a precise and formal definition, that does not mean there aren't useful ones and ones that are being refined. Progress has been made in not only the last millennia, but the last hundred years, and even the last decade. I'm not sure why so many are quick to be dismissive. The definition of life has issues and people are not so passionate about saying it is just a stab in the dark. Let your passion to criticize something be proportional to your passion to learn about that subject. Complaints are easy, but complaints aren't critiques.
That said, there's a lot of work in animal intelligence and neuroscience that sheds a lot of light on the subject. Especially in primate intelligence. There's so many mysteries here and subtle things that have surprising amounts of depth. It really is worth exploring. Frans de Waal has some fascinating books on Chimps. And hey, part of what is so interesting is that you have to take a deep look at yourself and how others view you. Take for example you reading this text. Bread it down, to atomic units. You'll probably be surprised at how complicated it is. Do you have a parallel process vocalizing my words? Do you have a parallel process spawning responses or quips? What is generating those? What are the biases? Such a simple every thing requires some pretty sophisticated software. If you really think you could write that program I think you're probably fooling yourself. But hey, maybe you're just more intelligent than me (or maybe less, since that too is another way to achieve the same outcome lol).
I also think some statements are plainly incorrect. For example "humanity is already collectively superintelligent" in Chapter 10. The term superintelligence isn't one we have a shared definition for, but it's usually understood as an intelligence that surpasses all prior forms of intelligence(s), not one that merely aggregates them. In that sense, superintelligence could represent a qualitatively new level of cognition limited only by the physical computational capacity of the universe. Once you have a superintelligent entity you can imagine a future one surpassing it.
In this book, I see Hume cited in a misunderstanding of his thought, and Kant is only briefly mentioned for his metaphysical idealism rather than his epistemology, which is a legitimately puzzling to me. Furthermore, to refer to Kant's transcendental idealism as "solipsism" is so mistaken that it's actually shocking. Transcendental idealism has nothing whatsoever to do with "solipsism" and is really just saying that we (like LLMs!) don't truly understand objects as "things in themselves" but rather form understanding of them via perceptions of them within time and space that we schematize and categorize into rational understandings of those objects.
Regarding Hume, the author brings up his famous is/ought dichotomy and misrepresents it as Hume neatly separating statements and "preferring" descriptive ones. We're now talking more about fact-value distinction because this is not talking about moral judgments but rather descriptive vs prescriptive statements, but I'll ignore that because the two are so often combined. The author then comes to Hume's exact conclusion, but thinks he is refuting Hume when he says:
>While intuitive, the is/ought dichotomy falls apart when we realize that models are not just inert matrices of numbers or Platonic ideas floating around in a sterile universe. Models are functions computed by living beings; they arguably define living beings. As such, they are always purposive, inherent to an active observer. Observers are not disinterested parties. Every “is” has an ineradicable “oughtness” about it.
The author has also just restated a form of transcendental idealism right before dismissing Kant's (and the very rigorously articulated "more recent postmodern philosophers and critical theorists") transcendental idealism! He is able to deftly, if unconvincingly, hand wave it with:
>We can mostly agree on a shared or “objective” reality because we all live in the same universe. Within-species, our umwelten, and thus our models—especially of the more physical aspects of the world around us—are all virtually identical, statistically speaking. Merely by being alive and interacting with one another, we (mostly) agree to agree.
I think this bit of structuralism is where the actual solipsism is happening. Humanity's rational comprehension of the world is actually very contingent. An example of this is the study that were done by Alexander Luria on remote peasant cultures and their capacity for hypothetical reasoning and logic in general. They turned out to be very different from "our models" [1]. But, even closer to home, I share the same town as people who believe in reiki healing to the extent that they are willing to pay for it.
But, more to the point, he has also simply rediscovered Hume's idea, which I will quote:
>In every system of morality, which I have hitherto met with, I have always remarked, that the author proceeds for some time in the ordinary way of reasoning, and establishes the being of a God, or makes observations concerning human affairs; when of a sudden I am surprised to find, that instead of the usual copulations of propositions, is, and is not, I meet with no proposition that is not connected with an ought, or an ought not.
Emphasis mine. Hume's point was that he thought descriptive statements always carry a prescriptive one hidden in their premise, and so that, in practice, "is" statements are always just "ought" statements.
Had the author engaged more actively with Hume's writing, he would have come across Hume's fork, related to this is-ought problem, and eventually settled on (what I believe to be) a much more important epistemological problem with regards to generative AI: the possibility of synthetic a priori knowledge. Kant provided a compelling argument in favor of the possibility of synthetic a priori knowledge, but I would argue that it does not apply to machines, as machines can "know" things only by reproducing the data they are trained with and lack the various methods of apperception needed to schematize knowledge due to a variety of reasons, but "time" being the foremost. LLMs don't have a concept of "time"; every inference they make is independent, and transformers are just a great way to link them together into sequences.
I should point out that I'm not a complete AI skeptic. I think that it could be possible to have some hypothetical model that would simply use gen AI as its sensory layer and combine that with a reasoning component that makes logical inferences that more resemble the categories that Kant described being used to generate synthetic a priori knowledge. Such a machine would be capable of producing true new information rather than simply sampling an admittedly massive approximation of the joint probability of semiotics (be it tokens or images) and hoping that the approximation is well constructed enough to interpolate the right answer out. I would personally argue that the latter "knowledge", when correct, is nothing more than persuasive Gettier cases.
Overall, I'm not very impressed with the author's treatment of these thinkers. Some of the other stuff looks interesting, but I worry it's a Gell-Mann amnesia effect to be too credulous, given that I have done quite a bit of primary source study on 19th century epistemology as a basis for my other study in newer writing in that area. The author's background is in physics and engineering, so I have a slight suspicion that (since he used Hume's thought related to moral judgments rather than knowledge), these are hazily remembered subjects from a rigorous ethics course he took at Princeton, but that is purely speculative on my part. I think he has reached a bit too far here.
1: https://languagelog.ldc.upenn.edu/nll/?p=481 (I am referring mostly to the section in blue here)
Intelligence is the ability of the human body to organize its own contours in a way that corresponds to the objective contours of the world.
Evald illyenkov.
And yes, the mind is part of the body, thus thinking consists of an action of organization to the contours of the world
What about animals?
To me best definition of intelligence is:
It's the ability to:
- Solve problems
- Develop novel insightful ideas, patterns and conclusions. Have to add that since they might not immediately solve a problem, although they might help solve a problem down the line. Example could be a comedian coming up with a clever original story. It doesn't really "solve a problem" directly, but it's intelligent.
The more you are capable of either of the two above, the more intelligent you are. Anything that is able to do the above, is intelligent at least to some extent, but how intelligent depends on how much it's able to do.
It's not about excluding animals, the quote was in the context of talking about people. You can apply the very same thing to any entity