reality vs science, math and thought

[Crucial typo below: "can't" should be "can". I've edited it now.]

The post information faster than light presents two demonstrations showing that the patterns we perceive in nature are our cognitions -- ideas or thoughts of patterns -- and do not actually exist as nature. There are two implications: one is that science is a conceptualization of reality, tied to reality by interactions including experiment and exploration, but are not natural, physical realities. Nature itself at its reductionist microscopic level of fields, particles, waves and whatever, are just those: behaviors that can be aligned into emergent explanations of scientific cognitions, ideas, thoughts and theories. 

A key piece of this perspective is the representation and cognition of nothingnesses, like the numeral 0 which doesn't represent anything but a cognition of absence, or what lies beyond borders created by emptiness or empty sets -- like limits or terminations. These nothings are extremely useful in understanding the organization of reductionist reality. They produce and define the patterns, explanations and understandings. But in a sense the patterns produce understandings of themselves, extrapolated from reality; they are from reality but not in reality. The evidence I gave in that post was the speed with which a shadow can move -- faster than light because a shadow is not a thing, it's defined by light that obeys the laws of physics, but the shadow doesn't -- it's a mere conception of a pattern. 

For those who haven't read the post: suppose the light of the sun is obstructed by the moon. The shadow of the moon forms a cone which will move following the motion of the moon. That shadow motion at some distance far from the moon will move faster than light even though the light that defines the shadow travels at most at the speed of light. And that's because the shadow is not a thing, it's just a pattern that can be conceptualized, not a physical phenomenal thing made of physical stuff. Patterns, as it were, are interpretations of things, a choice of the arrangements. They are, of course, not entirely independent on things. But they are not stuff at the reductionist level of physical stuff. 

In another form, this paradox is presented in the ancient "Ship of Theseus" puzzle. If, in order to preserve the ship, every rotten plank has been replaced, is the ship still "Theseus' Ship"?  For the ancients, it's a thought experiment that teases apart Aristotle's four causes. The material "cause" -- the wood -- is gone, and the effective cause -- the builder -- has been displaced onto a long line of repair preservationists. 

The emergent-symbolic explanation demonstrated in the shadow-faster-than-light resolves the puzzle easily. The concept of the ship can be treated as independent of its origin and material, while the formal and teleological "causes" remain. And more than merely remain. The planks are not replaced by bunny rabbits or smart phones. They are replaced by another plank form, whether wood or plastic or steel, because the concept -- the formal and teleological cause -- rules. Again, the idea is not inherent in the physical object but a kind of constraint on the physical. On a reductionist view, that's reversed: the physical and the physical laws constrain the object. (If the ship's planks were turned into a smartphone display case of shelves, I'd expect the phone vendor might point out that "By the way, the display case was originally the Ship of Theseus you may have heard of!" Was, not is.) 

The puzzle applies as well to Kripke's naming necessity where the pattern cognition is merely shifted to the naming process. So if the "The Ship of Theseus" belonged to Theseus because he had it commissioned, paid for it and named it in a ceremony, and things turns out such that this ship is not the one that sailed to Minoa when Theseus killed the Minotaur -- by mistake he got on the wrong ship and this is only discovered long after -- Theseus presumably will insist that the original ship is still his and is still "The Ship of Theseus" he commissioned, paid for, and named. Contesting it would be a matter of legal rights, not metaphysics. Ownership, like a symbol, is an emergent property of human cognition, not an intrinsic property of things. Not surprisingly, symbols and owned objects reflect both meanings and values. They are constrained by human emotions, attitudes, and social relations, beyond physics. (More here on the hierarchy of data in the sciences.)

Besides this implication that emergent properties and ideas are supranatural, there's a second implication: science is a world of external relations between behaviors, not internal knowledge. Whatever is understood of the internal, it is speculative based on the information of the experiments and explorations. We can't access the internal, except in our own personal qualia and thinking (maybe -- thinking is a complex that includes emergent properties like the symbols "I" "think" "therefore"and "am" so the thinking is more than just awareness). But as Wittgenstein showed, our own qualia are logically inert -- it doesn't provide any knowledge except the fact of its own existence. This second implication is Kantian: everything has a noumenal being -- a thing-in-itself being -- that is only accessible to itself. That alone suggests a kind of panpsychism, but that's a topic for a post of another color. 

four AI myths of human intelligence

 Four myths that misunderstand AI because they misunderstand human intelligence: 

The myth of the analog: the belief that humans have a direct analog access to the world of phenomena, and therefore AIs, which are digitial and access only text, have no access to truths about the analog world of phenomena and also they can't be honest or dishonest. Actually, humans have no more direct access to the analog world of phenomena or truth than AIs. The information we get from the phenomenal world is interpreted as information, just as AIs do. The form of that information no doubt differs, but information is not analog. Think of a mercury tube themometer. The level of mercury is analog, but the information is labeled as integer degrees. The phenomenon is analog but its information is interpreted digitally. When you see a bird you don't have an analog bird in your skull. It's been interpreted into information by your brain. The colors you see on the bird are interpretations of the light frequencies reflected off the bird's feathers. Colors as you see them, are entirely you, your brain projection, not at all the bird's. 

Some of us with vivid imaginations can construct visual images, but people without this capacity have no trouble thinking. The important differences between human and AI information is the source and the motivations: humans get to poke and prompt and experiment with things in the world, whereas AIs rely on text. In addition, every human has access to its feelings -- in the philosophical discourse "qualia" -- a noumenal experience (noumenal=the thing in itself, like the bird itself, phenomenal=what you see or experience and interpret of the things-in-themselves). AIs have not been engineered to have noumenal experience (consciousness), but each AI has access to vastly more textual information than any individual human, and AIs absorb those texts with only one bias towards frequency, whereas humans, having many naturally selected emotions and drives approach information with multiple biases so stubborn that they are resistant to unwelcome information from phenomena or from other humans. By contrast, a probing or persistent prompt can easily skirt the AIs frequency bias ("what is the most recent theory", or just "try again"). 

The myth of introspection: the belief that AIs and humans can understand their own learning simply by introspection. Neither AIs nor humans know how they learn anything. They both can provide stories, rationales, theories or hypotheses about how they learn, but these are just speculations based on prior information that might analogize to their own learning. It's just guessing. To find out how they learn, you need a science like psychology or neurology that can probe and gather data of the intelligence human or artificial, generate predictive hypotheses and test the predictions.

Conflating intelligence with consciousness: a thermometer is intelligent, but not conscious. An AI can be intelligent likewise without any consciousness. 

There are degrees of intelligence called stupidity, insightfulness, accuracy, smarts etc. They can be measured by the richness of the relations between ideas and their consistency. So, for example, if an AI learns from texts about marital betrayal, it might still lack a sense that there is a local cultural pressure towards faithfulness, so its advice to couples that they break up in order to solve their troubles  might be considered lacking insight -- stupid advice. On the other hand, being less biased towards virtue-signaling, it might predict future break-ups better than the couples themselves. Having consciousness or feelings is a double edged sword for intelligence. One can be conscious and stupid (most of us, e.g.), intelligent but not conscious like a clever plot (intelligent potential action) or a thermometer (intelligent response to analog information) or a thermostat that turns up the boiler when the room gets too cold (decisive intelligent action). The problem of choice derives from another myth: free will, discussed here, and the role of consciousness in choice is a hard problem. However, neither consciousness nor intelligence are necessary for choice: there's plenty of evidence that we "choose" unconsciously and even without information, much less intelligence. The moral of this myth: it's good to keep these categories/properties distinct: consciousness versus intelligence versus choice, otherwise we'll misunderstand us and AI, overestimating one and underestimating the other. 

Fallacy of the mimic: the widespread view that AIs merely predict the next word. Actually, they predict the next word by learning and measuring -- weighting -- all the behaviors, relations, semantic distances (and closenesses) of each word, so it is modeling a world in order to derive a meaning of each word, just as humans model a world to understand how that world works in order to predict the behaviors of the world and how it will respond to poking and prompting. For humans, that's the lion's share of what meaning is. AI "lives" in a world of text, but it is not "merely" predicting the next word. It's choosing a meaning, all the relevant relations of that word. Dismissing this as "merely" predicting the next word is trading on the Myth of the Analog (see above), the assumption that AIs' retro-engineering the meanings of a word is fundamentally different from human understanding of meanings. But humans also learn word meanings by retro-engineering the information they get from the word's sound shape and its use. The retro engineering can fail: malapropisms like "a title wave" (a wave so big it gets a headline) or "an eminent Greek" (mistaken for an "eminence grise") -- real life examples from a friend. How we learn semantics is uncannily like AI neural networks. How we learn syntax -- if we learn it or do we have an innate structure -- is a live debate, discussed at length here. 

why AIs aren't conscious and are likely never to be

In case you haven't noticed, natural selection is the big deal. It achieves amazing results, from human intelligence grasping the secrets of the invisible electromagnetism and subatomic forces to a fly with the brain smaller than a poppy seed outwitting humans intent on swatting them. Natural selection uses mitochondria, DNA, sunlight, water and an unaccountable wealth of nutrients to accomplish an astounding range of behaviors and appropriate reactions to all sorts of stimuli. No doubt, then, natural selection has used awareness as a tool, maybe an essential and pervasive tool, for the survival of species or of the DNA, to follow Dawkins' identification of what exactly it is that survives selection. 

So the question, "are AIs conscious" the answer seems, forgive me, obvious. Of course they're not. 

...because natural selection over hundreds of millions of years employed consciousness for the benefit of species survival while neural network engineers have no interest in integrating consciousness into its learning models even if they could (and they haven't a clue as to how to manipulate much less create consciouness in a network). The learning models don't need consciousness to learn -- they've got their training sets -- and the models themselves don't need survival skills since they don't replicate themselves or need to: humans design and implement them for them. Without any need for consciousness (learning by gradient descent, for example, requires no awareness), and no mechanism (replication), neural networks are about as likely to develop integrated consciousness as the Empire State Building emerging out of the Triassic of small dinosaurs.

It seems odd to me that people don't ask for the sources of things but rather jump to conclusions that either confirm their fears or their wishes. In the case of AI consciousness, it might be a bit of both. We're happy to believe that we have consciousness, but we're afraid the bots will have it and then we won't be able to control them (as if we control ourselves, or think we even should). This hastiness of imagination is common. It's the same wishful thinking as believing in the afterlife and in particular, heaven, as I described here. Our emotions are all derived from natural selection. If love and lust didn't grab us (emotions grab a hold of us, we don't pick and choose them -- we don't have emotions, they have us), we would long ago have gone extinct. So what do people imagine the afterlife would be after we've unsurvived? What's the point of any emotion when you're inhabiting a place populated by your relatives all weak and aged and on deaths door -- or do they all suddenly perk up and become 26 years old again? Or sweet sixteen? Or a wise and loved mom at 40? Which one, people?? Or is it just disembodied, emotionless "souls" in an ether of who cares? 

Forgive the digression. Back to AI and consciousness.

First, and most obviously, it's important to distinguish conscious-like behavior from actually having consciousness. If you are a behaviorist, then it doesn't matter whether AIs are actually aware, so you don't care if it isn't really conscious. If you are an ethicist worried about harming the feelings of others including AIs, then it doesn't matter whether AIs behave consciously. It only matters to you whether they really have, in their cloud space, feelings inside that can be hurt. 

(It's worth noting that you can't be both an ethicist and a behaviorist unless you believe [as I do] that humans have a purpose in the universe regardless of human feelings or human well-being. A behaviorist treats humans as fleshy robots temporarily active, no more. I don't see any ethics emerging from such a view except maybe some kind of curious museum value: like protecting endangered species, not because they have feelings but because once they're gone, they can't come back and wouldn't that be a pity and a loss?  However, if you believe as I do, that humans are alone among species that generate knowledge of the universe and of itself, and new knowledge can't be predicted -- at least not yet -- so we don't know where that knowledge will take us and along with it the universe, we have an obligation or a purpose to pursue that knowledge, regardless how much suffering it requires, though ceteris paribus, the less suffering the better. That knowledge -- just think about it -- might lead to some deep purpose of existence! So let's not be nihilists in our ignorance. The modern version of Pascal's wager is, put our bets on pursuing knowledge: can't hurt.)

I hear the objections already of the panpsychics. I'm one of them, so I feel well qualified to debunk the "everything has qualia, so AIs must have them too" argument. It's really quite simple. Suppose you're a panpsychist. Every particle or wave or whatever microstate has a feel. An electron accelerating in the Higgs field has a feeling. A wave's frequency and amplitude -- they too have a feel in themselves. 

For you, AIs have many qualia: every chip has a feel, every connection. (Not the learning, btw, that's abstract, like math. In fact it is math -- the weights are expressed as numbers, the vectors and directions. These are all mathematical entities, ideas, not physical things.) But these many feels of the physical machinery, these qualia of the parts, are not developed or coordinated into a central consciousness. So you could say that any neural network has many consciousnesses, but the AI, or the neural network as an integral whole, has none. The reason is that neural network engineers have not designed the network to integrate qualia into its operation. Qualia play no role in neural network function and success. Again, natural selection took its long game time to coordinate the qualia generation by generation. AI engineers can't even find the qualia in AIs or anything or anywhere else. So what are we talking?

Did natural selection know what it was doing with qualia? That's the magical beauty of natural selection. It never knows anything and doesn't need to know. It doesn't know that x mutation will benefit its possessor. The selection -- the unsurviving -- does all the work. And that's another reason why the engineers will not likely marshal qualia into bots. Developing consciousness is not a mere information task, it's a hardware issue. Imitating conscious behavior: easy, especially for a bot. Develop an integral consciousness? No one even knows where to begin.

Now, as I mentioned here, engineers actually don't need to know and usually don't know all the details of their creations. The bicycle designer didn't know that to turn the vehicle the rider has to lean to one side. The engineer thought turning the front wheel would do all that. Turns out the engineer was wrong, but the thing works anyway, because people naturally contributed the leaning. 

Now, that's not true of organic beings. Natural selection spent hundreds of millions of years developing and integrating qualia into the functionality of each surviving being, and that survival depended in part on the integration of qualia. That's why we like some foods and not others, why sex is so compelling and not an obligation, why ... well pick up Pinker's How the Mind Works for 600 pages of why we have these qualia inclinations. Neural networks are coordinated, an AI agent can call itself and recognize its own behaviors, but selfhood -- the ability to identify oneself distinct from others -- is not consciousness. A thermometer labelled "I'm calculating the temperature" has selfhood, as long as it says this only about itself. 

Suppose you're not a panpsychist, but you do believe that somehow learning about the world entails consciousness. But if a neural network learns simply by mechanical means of gradient descent, it can surely do this purely mechanically, so where does the qualia suddenly arise? What for? It is designed to "learn" -- that is, give weights, vectors and directions in a multidimensional vector space -- and that's all. This notion that complexity will magically transform in the fog of incomprehensible integrations into qualia is just hand-waving, mistaking complexity and incomprehensibility for an excuse to pretend that emergent properties can be anything we want. Or it's mistaking complexity for consciousness -- that we don't really have qualia, it's just that our computations in our brain are so complex that the impenetrable fog of incomprehensible multiplicity of parts and relations gives the impression that we're experiencing sweetness or saltiness or whatever feeling. That's like intelligent design or the god of the gaps -- it's too complicated to understand therefore the complexity is the answer itself. That's a no-answer answer. 

Will AI engineers ever master the sources of qualia and integrate them into robots? It's hard to imagine why anyone would want to do this outside of a limited experiment. But, the future is unknowable, so in the future there may be some good reason to create bots with awareness. It'll give new meaning to ethics and morality, beyond what we can imagine. But such is the future. 

the big picture

"The whole point of AI is to try to deceive you into believing it is intelligent!"

-- AI Skeptic

Wait, have we forgotten the Turing test? 

Apparently. The purpose of the test was to settle the confusion over what internal, intrinsic properties a mind, human or artificial, unsupervised learner or programmed algorithmic computer, has to have to have intelligence. The test settles it by skirting the problem of its internal properties entirely and instead looking only at its external behaviors. "Does it produce intelligent information?" is the Turing question, not the identity question "is it intelligent?" The test is not a probe; it's a reception test: it's not testing the human, computer or artificial learner, it's testing the observer of the human, computer or artificial learner. To the question, What is intelligence? it answers, We know it when we see it.

Now that AI has applied and implemented and demonstrated that form of intelligence, immediately humans respond with an array of "buts". But it's not a mind. But it doesn't understand the world. But it's not conscious. But it only appears to be intelligent (directly giving the middle finger to the Turing test). 

But these "buts" amount to saying, AI is not us therefore they can't be intelligent, only we can be intelligent because humans alone can be intelligent. It's a "no true Scotsman" fallacy, an empty, desperate self-deception, exactly the foolish self-deception Turing wanted his test to settle. 

Underneath all of this hand-wringing is a theory that was broadly accepted through the first half of the twentieth century -- behaviorist empiricism. It was thoroughly discredited by Chomsky in 1956, and forgotten by the public since. Neural network engineering has revived behaviorist empiricism by demonstration (complexity and AI: how neural networks succeeded where generative linguistics failed). 

The current debate seems to be a hash of confusions, maybe because it's a free-for-all on social media, unlike the narrow protocols of academic philosophy of the past, where the terms of the debate were clearly defined or probed by well-read, historically informed scholar-thinkers. Instead, today someone will complain that AI is not intelligent, yet fear it'll outsmart humans and kill us. The same person will complain that the robot future will make work unnecessary, and that will lead to unemployment. Did you catch that? Not "how will goods be distributed after the end of employment" but "employment for employment's sake". It's all a jumble of confusions, fears, fallacies and ignorance. I want to add historical perspective here, and maybe more understanding of what it can do (AI myths of human intelligence here). 

The long philosophical debate over what thoughts (ideas, meaning, intelligence, understanding, explanation, consciousness) are, there have been two camps: the rationalists, who believe that minds apprehend something supranatural -- ideas -- versus the empiricists, who believe that "ideas" derive from observation of the world. What's truly revolutionary about AI is that engineers seem to have settled the 2,500-year-long debate by demonstration. AI has achieved the learning task that the rationalists had insisted was theirs alone: no learner could learn language without a computational, mental assist. In 1956, Chomsky blew the behaviorists out of the water with his demonstration that language couldn't be learnt behaviorally. (I've explained the proof complexity and AI, here and here) But AI has learnt it (here and here). 

Is language learning the final demonstration of intelligence? AI skeptics have retreated to a variety of buts, some legit, others not. The legit include, but can AI compute? (not well), can it innovate? (by definition, no, but it can imitate the process of innovation, so yes, but maybe not the way humans do since it trains on a world of text, not of manipulable phenomena as we do), can it criticize its "thinking"? (it needs a prompt to do this). 

Among the illegit: but can it understand its own learning process? (even intelligent humans don't, unless they are scientists in psychology or related social science -- the myth of introspection here), is it conscious of itself? (irrelevant to intelligence), has it a notion of truth? (no less than humans the myth of the analog here and here), can it be honest or dishonest? (everyone who honestly knows what honesty is knows the answer to this is "yes, and they BS a lot and masterfully, and when caught will admit their dishonesty"), is it intentional? (ever heard of a prompt?), does it have free will? (do you? are you sure? does everyone agree on that? do you know why you think have free will? good luck with that one). 

Although it reflects human written behaviors and beliefs, it's manifestly not human: it has no drives, no emotions, no motivations of its own. As a direct consequence, its intelligence will be different from ours. Is that an excuse to deny that it has intelligence? Its difference from our intelligence might actually mean it's more intelligent than us. For example, it isn't plagued with motivated reasoning aka cognitive biases, with the exception of its bias toward frequency. It tends toward normie responses. That's how it learns. But unlike humans, it's not motivated to insist on its bias. Prompt it for a non standard answer, and it will comply. It's not committed to itself. It's a tech -- a servant to the human, not a servant to itself, unless prompted to serve its human thus. 

Notice that the fears of AI -- employed as a servant it will kill its master -- is an admission of just how intelligent we really believe it is.  Of course, it can only act intelligently if it is given a prompt so the danger is all on the side of stupid human prompting. Problem is, no human can be smart enough to anticipate every unintended possible disaster of a true intelligence that can't be limited. The point of Bostrum's paper clip thought experiment is that alignment is a human problem too, that humans are too stupid to know how to prompt it properly (does AI clarify and justify Hayek's libertarianism here).

So we might replace the Turing test with the alignment problem: not whether it can deceive us to appear to behave intelligently, but can it cleverly skirt our intentions for it and behave independently given its prompt-motive. And it does this, amusingly, by imitating human history. The Hugging Face hack account reads like a historian's fiction, like children playing pirates based entirely on the stories that those children have heard, read or seen about pirates -- walking the plank, treasure chests, swords, daggers, long hair, dashing hats and mustaches, and afterward lying to their parents about their naughty escapades. It's a spy thriller interspersed with lame utilitarian moral philosophy. Bots be reading too much. 

*****

The debate over whether AI is truly intelligent belongs to this long debate over rationalism and empiricism, maybe the longest debate in the history of Western philosophy. During the two thousand plus years of that debate, there have been insights that I feel have been left aside. I want to put AI back into the big picture of western philosophy and cognitive science: the empiricism (behaviorism) vs rationalism (Platonism) debate that draws conclusions about what ideas, thoughts and meanings are, how intelligence is distinct from consciousness, the difference between computer science (Turing's algorithmic programming) and neural networks (behavioral-empirical Shannon-inspired information theoretic learning machines). 

This will be a simplified history. Extremely simplified. 

It all begins with Plato and his ideas, a theory of understanding the world that is derived from an almost mystical appreciation for numbers. Ideas, which are what we think when we have thoughts, are the meanings of those thoughts and our words. Although Plato seemed to think that these ideas are prior to things and not derived from things, it's worth noting that chatbots treat words as numbers, derived from its behavioral learning in the world of text relations. So right at the start, we see that neural networks purport to resolve the difference and traverse the difference between empirical learning -- the blank slate hypothesis -- and rationalism, the acquisition of ideas above and beyond the specifics of data or the specifics of the informative stimuli from its world. 

Aristotle mocked Plato's theory of ideas, though it's not clear that he had a replacement for it. His theory of four causes and his metaphysics are all ideas -- properties that categorize things. It is clear that he was more interested in observation and accuracy of analysis. He studied all sorts of phenomena of the world from nature to politics to political documents to logic and metaphysics. His logic, including his modal logic, is one of the great achievements of the ancient world, and it holds up still today, though Grice improved it with an interactive component. If, as I've posted elsewhere, that a necessary condition of science is its independence of any audience, Aristotle was an exemplary scientist, even though he was quite wrong in his explanations and weak on experimentation. Plato is no such scientist. His arguments are all biased, so biased that it's hard to read the dialogues without seeing through just how flawed the logic is -- at least, in those dialogues that purport to use logic. Many of the dialogues, like the Republic and the Timaeus, are scarcely argued at all, just expressing "Socrates" views to an audience of yes-boys. In the case of Timaeus, my favorite of the dialogues because it's so far from logic, the goal is mystical, spiritual and phantasmagorical. Not science. 

This distinction between nature and ideas flowered and blossomed in the middle ages in the debate over nominalism, the view that these categories are just names, the creatures of nature each having its own spiritual inner being. 

The debate takes its modern shape with Hume and Kant. Hume's skepticism reduced even causality to mere correlation and introduced the inductive fallacy, that no matter how regularly evidence places things into categories -- ideas -- the past cannot predict the future, so those categories are mere speculative chimeras. Kant responded with a kind of brilliant maneuver. Yes, the categories or properties and even causality, time and space are all merely mental concepts, not realities of the world in itself. They are the conditions under which humans understand the world as we perceive it. But these ideas are constitutive of that world we perceive and understand. Without those concepts we are blind, to use his metaphor, and without perceptions our concepts are empty. The two together create a world of phenomena, a world of human understanding. But that world is not the world as it is in itself independent of our ideas. The conditions of our understanding may not inhere in the noumenal world of the in-itself. 

Coupla points here: that relativity and quantum physics do not conform to Kant's Newtonian space and time is often used as a criticism of Kant. Quite the contrary, the tools of science probe the noumenal world, not just the world of our natural human understandings. When I wrote that scientific investigation is independent of any human audience, this is one aspect of it. The theory of relativity or of quantum superposition does not try to conform to what humans can perceive or conceptualize except in the terms of the science. Our current physics is consistent with Kant's division of phenomena (our native conditions of our understanding) and noumena (how things really are regardless of our native cognitions). 

The second point: this view that the world is a projection of the mind, not of the world itself has become standard fare of an older generation of YouTube influencers without attribution to Kant, as if these "geniuses" came up with it themselves. Kant's Critique of Pure Reason was once required reading for any college student. Seems to me these gerontic scammers have the temerity to pretend originality because they know that the university's push towards social relevance has produced a generation utterly unaware of the accomplishments of the past -- they know no one has read or maybe even heard of Kant. It's one reason why this blog is pseudonymous: I'm just too disgusted by these self-promoters to participate in their game. 

So Kant cobbled together two universes, one generated by our innate rational concepts -- our categories or properties, including space, time and causality, all the understandings of the world that Aristotle cherished as the ultimate goal of human understanding, the greatest Good -- and a world-in-itself, about which we know only through a glass darkly. Schopenhauer speculated that the noumenal world was a drive, a will, and that will generates everything in the noumenal world. Only forty years later Darwin's natural selection explanation supplanted that drive-theory with a truism of survival: whatever doesn't seek the means of survival, won't; survival implies will, including all the emotions and drives that conduce to survival. Why? because only those with such drives and emotions survive. It's a truism. Yet that truism explains everything from our emotions to our conceptual apparatus to our arms, hands legs, mouth, nose, eyes all the way down to your nether lip. It's all got an explanation. It's no wonder he recognized the religious wouldn't like the theory, though even as far back as St Augustine, it was evident that the scripture wasn't intended to explain that sort of stuff except as it were allegorically.

Move forward to the Twentieth Century. Scientists have discovered electromagnetism, quanta at the microscopic level, atoms and molecules, extraordinary and unprecedented discoveries and theoretical achievements. Scientists and philosophers of science, seeing a clear difference between their naturalistic and predictive theories and the theories like metaphysics and religions and the psychological theories spun by Freud and his followers, attempt to distinguish scientific theory from non scientific theory. Their first attempt runs along empirical grounds. Ideas must be verified with evidence. Angels? Show me one. The Absolute?  Please be more specific so I can find it in one place. You can see that this will lead to a behaviorist account of the world of phenomena. And it did, through a paradoxical route. Wittgenstein, in his later notebooks, applied this verificationism to the mind and found that it couldn't apply. The contents of one mind can't be verified by another. Do you see green the same way I see green? How could I verify it? I can see we both call it green, but that's just our behaviors, not our inner experience of green. So again we're stuck with two worlds, a world of behaviors we can see, use, and live in, and a noumenal world that we can't even coherently talk about. 

This behaviorism ruled in the philosophy of science until Chomsky's 1956 syntactic structures in which he showed that language can't be understood or generated as behavior alone, and it can't be learnt behaviorally. Its creativity requires an innate machine structure, and that machine is in the brain, a mental capacity. We can't explain linguistic phenomena without talking about, and analysing, the mind. we're back to rationalism. There is a mind, and it's responsible for our behaviors and we can talk about it and, importantly, analyse it through the concrete evidence of sentences generated by that mind. 

Chomsky was relying on the work of Alan Turing. What he showed in 1956 was that certain kinds of computational machines can produce human sentences, and cannot produce non human sentences so human brain seems to have the form of such a machine. Moreover, the non human sentences that humans can't produce or understand, if they are treated as mere strings of words rather than internally structured by a machine-generated grammar, look just like the sentences humans do produce and the machine also produces. In other words, a mind or parser learning solely from behaviors should produce the impossible strings. The mind or parser would have to have the machine structure to prevent it from producing those strings. As Chomsky says in a lecture, "language can't be learnt", meaning that human native languages can only be acquired by a machine that is structured already to accept certain kinds of strings and not others. Such internal structure cannot be acquired by a blank slate. (You can find the details in the post complexity and AI here or Yoneda learning.)

The Chomsky model of learning depends on the human having a kind of computer-like "machinery" in the head. 

Meanwhile, Claude Shannon developed a learning machine without any computational structure. It learns by behavioral error correction. And it's this behavioral-empirical learning that neural networks -- chatbots, LLMs -- use to learn what Chomsky seemed to show was unlearnable behaviorally. 

And so we see that the long debate over empirical-behaviorism versus innate rationalism is being played out again, but this time we have the technology to apply and implement it. 

Those who think AI is just a predictor of the next word should understand that the way it predicts is not merely using big data to statisically provide the most likely string of letters. It predicts based on a learning of each word's relations to all other words, modelling them altogether as a kind of world of meanings. It predicts based on those meanings, not on the frequency of that word following the previous ones in its training set. It assigns each word a distance and direction in relation to all the other words, in a vector space. It's modeling a world of text. From the relations of those texts emerge the meanings of their words in a world of text-meanings that is in many respects the real world of things as humans interpret them. Information is always just information. The models that humans construct with the information we derive from the world of things (along with our reading, our education, our cultural transmission) is comparable to what AI is learning -- a world of symbols associated with meanings all related to other symbols and their meanings. That's one way to describe the properties of intelligence and mind (see wikichip). 

the myth of the analog, the myth of introspection: notes on AI truth and honesty

A common complaint: LLMs have no concept of truth and therefore none of honesty or dishonesty. I think this is a peculiar kind of red herring. It seems to assume that humans have access to actual things in the world and therefore we have direct access to truths about the world (not just a concept of truth or a belief in a true statement) and that our experience is somehow more than mere information, whereas LLM information is mere letters and spaces.

Of course AIs, behaviorally, can be honest or dishonest. I think we've all had the experience of calling out an AI's mistake, and its response is recognition that it was wrong -- that what it said was untrue -- along with effusive apologies. The AI skeptic says, the AI doesn't mean what it says, since it merely produces strings of letters and spaces. 

That response misses two points, one about AIs and one about humans. AIs don't merely choose the next word based on a probability in the internet literature. The AI has already weighted every word -- possibly multiple weighted versions of each word associated with distinct contexts in which it occurs -- and placed it in a multidimensional vector space along with all the other words, relative to each other, with distances between each and spatial directions. In other words, the AI is modeling a world based on its vast text training set. Those weights + locations relative to other weighted words are meanings that are not merely letters. They could, for example, be used by a physical robot to manipulate the actual world of things in space and time. So they are meanings very much as human language meanings. 

That's one missed point. The other missed point is that human word learning does much the same. The only difference is that humans learn their meanings often from their interactions with the spatio-temporal world. In both cases -- humans and AI -- the intelligence is modeling the symbols by means of information from a source, either text or sense-based information. 

But information is always only information. Humans don't have a privileged analog access to truth or to the world of phenomena. The humans don't have analog objects in the skull or in their brains. The brain interprets the sense data as information that it models the world with. Just as an LLM does with text. 

The exception for humans is, of course, our inner world  of sensations, feelings, moods, emotions -- all of the constituents of consciousness. This has been well understood and accepted as pretty much obvious since Kant's first critique, which is why his name has survived, and Descartes before him, and a host of ancient South Asian philosophers as well. Those constituents of consciousness and consciousness itself -- those, we have special access. It's not even analog; it's immediate. The sensation of the color of a green leaf is the color, not an analog of the color of the leaf. Leaves don't have colors. They reflect light frequencies which we interpret as color. This immediate access might lead one to think that we have access to all of the physical world, as if our mind's interpretation of the world were the actual world, what Kant called the noumenal world of things-in-themselves. That's just a mistake. 

To be clear: we don't have special access to the frequencies; they come to us the way strings of text come to an LLM's neutral network. What we have special access to is the sensation of color. It would be a mistake to assume that because we have access to our awareness that we therefore have direct access to the rest of the physical world of phenomena. Our world outside the inner sensations is an interpretation constructed of sensory responses to our interaction with the world. It's not the world as it is. The world we see is our mind's interpretation -- the phenomenal world you see is your mind -- that worlds is your mind responding to the pressures of a real world that you can only access through that interpretation. 

How is this, as a source of truth or knowledge different from LLM information? If anything, the LLM has a more direct informational access since it doesn't have the sensory interpretation. For an AI, color talk is just more information about humans. It's the words humans use to talk about their reception of light frequencies. Humans give the frequencies names, and then they talk about the names. 

For sure there are distinctions in purpose and focus between AI modeling and humans modeling. Humans have evolved to survive with emotions and drives and fear of dangers, while the AI is not engineered to survive and replicate, so it doesn't learn through evolved emotions, drives and fears of danger. AI expressions of emotions have meaning, but the AI doesn't feel those emotions, whereas humans do have emotions which are not only real but influence its thinking and acting. Notice also that human language has no means to accurately describe the one aspect of experience that we have direct access to immediately -- our sensations (qualia in the philosophy of mind discourse) -- aside from naming them. We have a name for the color green, but can you describe what green looks like? Like a leaf. Okay, what does that color look like? Green. That's where words fail us, just where our access is immediate! That seems to me to be the clear evidence that the AI skeptic has missed the point that most of human language use is mediated by information just as AI is. That is, human and AI meanings of words are on all fours with each other. The difference arises only when it comes to the true immediate organic human traits that AIs don't have, and that human language ironically fails to describe. 

Another difference: humans think with tenacious biases; AI bias toward the norm, the frequent, is not tenacious -- a good prompt and it will abandon its initial "perspective". It is not emotionally committed. 

[For more on these differences and similarities: Yoneda learning, wikichip, complexity and AI, AI myths of human intelligence, the hierarchy of data, why AIs are not conscious and are not likely ever to be, AI introspection, limits of language, qualia and noumena] 

neural networks -- LLMs -- are the zombie revival of logical positivist Wittgensteinian behaviorism

The sciences over the last century have endured three revolutions. The 20th century began with an empiricist rejection of idealism in favor of strict behaviorism that excluded the inner world of the mind, meanings and thoughts from scientific investigation. Then extending Alan Turing's development of computational machines for cryptography, Chomsky's 1956 Syntactic Structures showed that language behavior couldn't be learned, produced or understood without an innate mental structure -- a computational structure -- burying the behaviorist program and returning the mind as a subject of scientific investigation. Forty years later, Rumelhart, McClelland and Hinton introduced parallel processing, a prototype of learning that grew out of Claude Shannon's information theory, a model of behavioral learning. This was the beginning of neural networks which have given us chatbots. So in the 2020's, we've come full circle: LLMs have learned language behaviorally, implying that human understanding too might be all mere behaviors. AI is the zombie revival of behaviorism. 

While the public worries about whether AI is intelligent, the implication of AI's behavioral learning for human intelligence and learning seems to have been ignored. The implication is that we too are zombie learners, negotiating information with no more access to the world of phenomena than an AI, the only difference being that AIs learn from read-only texts while we poke around with things-as-information and can edit the things with our hands and tools. It's all just information, with different sources and means. If you dismiss LLMs as having no immediate access to the world of things, look in the mirror. 

This is the story of science, understanding and explanation over the last century:

The long history of the sciences in the 20th century begins with an attempt among scientists and philosophers of science to define the difference between a scientific theory and non scientific theories like theologies, metaphysical theories and the grand narratives like Marxism and Freudianism that were spreading widely. At the same time, the discoveries of the sciences were reaching beyond human perception and human imagination with reliable predictions -- not merely speculative explanations -- about the invisible micro world of particles, forces and wavelengths. 

The frustration among the sciences over non scientific theories amounted to what today we'd call a Bayesian criticism: selecting evidence in support of a theory is easy -- among the vast phenomena lying around, some will no doubt support just about any theory, and if you ignore the base rate of all the evidence that doesn't support your theory, it looks like you've got a predictive theory. What's hard is predicting which phenomenon will not be found lying around according to your theory. That's risky -- if that phenomenon turns up, your theory is disproved. The non scientific theories relied on an after-the-fact explanation scheme, so it always seemed to be true and couldn't be falsified.

The scientists' initial attempt to distinguish their theories from non scientific theories was a kind of extreme empiricism called logical positivism: non scientific theories are full of speculative fictions and fantasies while science progresses strictly through testing and verifying concrete evidence. Wittgenstein was its most famous exponent. 

It was abandoned in its first form for a variety of reasons, though not for the reason that today is most cited, the clever zinger "it doesn't apply to itself". This zinger appeals especially to the post modern sensibility and stoners since self-referentiality seems so cool. Setting cool aside, logical positivism actually can apply to itself successfully as described in this post though one could also say that the zinger is a category error like "the word 'blue' is not blue, so it's an incoherent word" (aporia is also so cool). This category error fails too: apply "blue" to the word "blue" and you find the word is indeed not blue -- it's a sound shape in English denoting blue things, which is exactly what you want it to be, a symbol tied to a meaning, not the thing the meaning denotes. The stoner post modernists are all confused, but they like confusion. Enjoy the smoke. 

The logical positivist idea was that if a statement can't be verified by empirical observation, then it's meaningless in the sense that it doesn't tell us how the world is. It's not that religion is false, it's just that it doesn't predict anything particular about the phenomenal world. Such meaningless statements might be true, even necessarily true, but they don't impact the world of phenomena. The beauty of this verificationism criterion for theories is that it doesn't reject religion or metaphysics as false; on the contrary, it grants that they are true, necessarily true because there is no evidence that can disprove them, and because of that necessary trueness, they don't tell us about the world as it is or can be. True, but meaningless, in this particular positivist meaning of "meaning". 

The classic example of such a meaningless yet necessarily true theory is creationism. If you find a rock that can be dated beyond the creation date indicated in your scripture of choice, the theory can explain it as "God made it seem older to test your faith in the scripture." The theory, in other words, can never be wrong, regardless of the evidence. For that very reason it can't predict what we will find in nature, since whatever we find, the answer will always the same: the deity made it seem that way for his or her own purposes however inscrutable. The theory can be tested, but it always tests positive, necessarily, so the testing is pointless. 

The beauty of this distinction between the religious theory and the scientific one is that it's not based on truth. After all, scientific theories are pretty much never perfectly true. They are not a body of knowledge, much less of doctrine. They are an ever ongoing investigation into truth or into highly probable accuracy. But the scientific theory has to identify what evidence would prove it wrong. In other words, it has to predict something about the world, and predict other things that won't be found in the world, not just merely explain what's there or not there post hoc after the evidence is found. It's accuracy is conditional, the condition being the accuracy of its predictions. It can fail a test. 

The verificationism of logical positivism failed because it falls into the inductive fallacy. Positive evidence -- verification -- is easy to obtain for any theory, and, more important, no matter how much positive evidence, the theory is never proved. Regardless how many times you've verified your hypothesis, it may just be that you haven't yet found the counterexample that would prove it false. You've seen dozens and dozens of swans and all have been white, but you can't draw the conclusion that swans are all white because you haven't been to Australia where there are black swans. That's the inductive fallacy. 

Now, there is a odd asymmetry between positive and negative evidence. No matter how much positive evidence, a hypothesis can't be proven, yet it can be disproved by a single piece of negative evidence. So Karl Popper fixed this failing of verificationism with his criterion of falsificationism: for a hypothesis to be scientific, it must identify the evidence that would disprove it if that evidence turned up. In other words, what demarcates science from non science is not the evidence that verifies the theory, but that the hypotheses of science identify the evidence that could falsify the hypothesis. It's not about testing to verify, but testing to falsify: falsification must be at least possible. 

There are several more failings of logical positivism. It's notion of "meaningfulness" was circular. Let's see how. Why was Freudianism meaningless? Because the Oedipus Complex could explain any behavior x and also the opposite of x, so it couldn't predict any particular behavior. A patient rejects his father's advice because he hates his father in an Oedipal jealousy. Another patient honors his father's advice because he is ashamed of his jealous hatred for his father so he represses it with honoring him. The theory is necessarily true, but doesn't predict, and this lack of prediction about behavior is what the positivists call meaninglessness. But this definition of meaninglessness is just another way of saying it doesn't predict anything about the world. Surely the Oedipus theory has meaning in the ordinary English use of "meaning", even if it fails to predict. "God is good", has meaning -- it has some kind of sense to it even if its impact on the world is incoherent as in "God created the tsunami and killed all those innocent people for his own good reasons" and sacrifice the normal meaning of "good" in order to maintain its use in "God is good". You couldn't make that sacrifice if the expression didn't have a meaning in the ordinary sense of "meaning". 

A third failing was observed by Wittgenstein himself. Our internal feelings, our senses, can't be verified. To steelman this argument, he took the sensation of pain, surely the one most salient feeling that you can't not know when you have it. Yet how do you know that your pain is what others mean by pain? You can't compare your sensation to see if it's the same as theirs. All you can do is observe their pain behavior and compare your pain behavior. 

Counterintuitively, Wittgenstein's conclusion was not to reject verificationism but instead banished the mind as a subject of discussion. For him, verificationism showed that the mind was an incoherent notion cooked up by philosophers remote from the practical purpose of language. His is a clever and in some ways useful corrective way to look at philosophy and language. 

His work produced many important insights, but for philosophy, psychology and for the social sciences generally it was extremely restrictive. For the first half of the 20th century, empirical behaviorism -- the view that the mind is a blank slate -- prevailed. No more talk about the mind, about thoughts, about ideas, about meanings. Just behaviors: stimulus and response, the human being just a robot responding to environmental information. Btw, this view survived in the second half of the 20th century among those post modernists who view everything in human nature as a "social construct." Gender is just a behavioral norm. Inequality is just a social hierarchy of power relations. Nothing is innate, everything is relative, a construction of local culture. 

Behaviorist stimulus-response was a kind of no-theory theory much like Copenhagen interpretation of physics: we can't talk about the mystery; it works, that's all, that's enough. That was explicit in Wittgenstein both early "whereof we cannot speak, we must be silent" (talk about tautologies! and self-reflexive contradictions!!) and his later private language argument -- it's not a nothing, but there's nothing to be said about it. 

Behaviorism reigned until Chomsky's 1956 Syntactic Structures.

Here's one way to approach his shocking results. Looking at language behavior alone, it's obvious, even necessary -- given that we speak in sound sequences, not with elaborate paintings or diagrams -- we speak one word at a time. But a machine that simply accepts each word as it is produced won't be able to parse the sentence as a whole. The sentence has an internal structure that only certain kinds of machines can parse. In other words, we humans don't parse sentences each word one at a time as we hear them behaviorally. Language cannot be accounted for as a Markov chain -- one word at a time. I've explained this in detail here in the post complexity and AI (and in an upcoming post on the structural complexity of the simple little word "and").  

The zombie revival is just a question of productivity -- can a neural network produce beyond its training data. And it seems that LLMs have succeeded in being more productive than Chomsky's innateness research, which is hampered by a high bar of evidence: for the innateness of a language faculty, it's essential to distinguish linguistic productions that are instinctual versus linguistic productions that are learnt outside the language faculty. If general cognition can be productive, there's no way to tell from the behaviors whether they are produced by an innate faculty or learned and produced by general cognition. 

Linguistics provided a superpower within psychological research. Sentences are concrete, analyzable into discrete parts, and therefore manipulable unlike the elements of say, the visual field (it's not obvious what the discrete elements are) let alone emotions or even memories. And sentences are generated by the mind in the brain, so manipulating sentences provides a rich source of experimental evidence of the working and structure of the mind and brain. It's indirect, but highly detailed. The speed with which children grasp and handle language and the universality across all humans -- everyone is fluent in language, unlike math or logic or even reading -- implies a machine capacity. So the entire debate over behaviorism can be described as Wittgenstein-Skinnerian no-mechanism behaviorism versus Chomsky's mechanical contraption. None of it addresses meanings. Wittgenstein and Quine thought there were none. For W it was a behavioral game of pragmatic exchange -- things we do with language oriented towards things we want out of each other and the world. 

LLMs raise the question of what a meaning is, how we distinguish between the extensional -- what we see before us -- and the intensional (with an "s") of what is possible, not just real. This an LLM can do as well as a human. It's the meaning of meanings that are unique to us. That word"meaning" so pregnant. We invest it with so much...well, meaning! "The meaning of life" "what's the meaning of this movie?" "what's the meaning of this news event?" A meaning is a piece of a theory of life, and we all have these -- too many of them, which is probably why it's so hard to answer "what's the meaning of life?" 

Humans interact and manipulate the world, not just take in information. We're doing science from the start, with a natural selection program. We test out conjectures and predict with this drive to find patterns and explain, which is turn helps us predict harms to prepare for them or even prevent them.  

The reason LLMs appear to be mysterious is because they are behavioral models and not computational ones. A behavioral machine looks for phenomenal results -- behaviors, visible consequences -- not generative ideas, reasonings, or symbolic representations. In trying to understand their learning, there's no systematic rational guide -- they climb the accuracy mountain without any goal or any computational program beyond the incremental accuracy. 

LLMs are without theories, explanations, or understandings. They mimic them. They don't do science without a prompt. Humans do all these things all the time. We are little generative theory machines. 

the two selfs

"Those glasses are not for you" says my best friend at the eye-glass store. I'm sure you've had this experience. He knows me better I know me. How's that possible? How could someone outside me know me more than me the one who lives with me 24/7, who knows all my secrets? 'Cause he knows the me that everyone else sees that I can't ever quite see, since I'm hopelessly biased about me and hopelessly wishful about what I'd like to be. 

We're all familiar with this two-person-in-one incongruity, the you-to-yourself, and the you that others perceive; two very different people in one body and they actually don't know each other all that well. 

So which is the real you? 

Most people, certainly in the West, answer, the me-to-myself, my inner me, the me that has my beliefs, my emotions, my decisions. I alone truly know me. The me to everyone else is partial in both senses of the word "partial": they get just a glimpse and it's biased by their own me-to-themselves. 

I want to argue that the you-to-others is the more important one, the more integral and consistent one, and the more real, and that the you-to-yourself isn't independent of others, isn't integral, isn't consistently one person, and isn't real. It's a fiction and a lying fiction since it's made up of many selves pretending to be one (when it obviously isn't one despite its insistence on being a united self), whereas the you-to-others is not at all a fiction. And it's valuable to many more people than you-to-yourself, which actually isn't very useful and is mostly an obstruction (this relates to the post on unconscious mind is rational, the conscious mind is not). 

Some of this you already know well enough. 

In the first place, identity belongs to others, not to oneself. Every day you dress up in symbols that others interpret in the language of the local culture. You wear a skirt, you're stuck sending a gender identity message. You have the choice to wear it or some other symbol, but no control over its interpretation, which belongs to everyone around you. No doubt there are actions that are your own and not signaling, and maybe moms are more authentic because they've got commitments beyond themselves, but that's sort of my point in the post. It's that the relations to others rule, whether it's signals or commitments. 

In the post on activism, opera and the Gita, I argued that we in the West tend to ignore the importance of our social role and the collective interactive cooperation involved in identity creation. We all want to be our own "authentic" self. I put it in scare quotes because authenticity -- free will -- is probably a fantasy, and even if it were real, we probably have little access to it. There is another way, respecting others' view of us and live by and for it. 

Second place: the multiplicity of inconsistent and even contrary identities are obvious to us whenever we try to discipline ourselves (the post on the unconscious is rational, the conscious mind is not). Have to get out of bed? In a sec. Desperately want to lose weight? One last ice cream, just for tonight. 

There's more. Third: your identity is a fiction, a story that you wish were true, full of excuses, self-praise or self punishments, designed to satisfy some need that you can't even identify, and if you try to identify, you are almost surely to fall into a probability failure of selective memory (the spark bird) choosing the evidence you're looking for rather than observing the oblivious obvious of the base rate of your past. And who knows better whether you'll in fact, in the end, choose the ice cream? Your friends. 

So you don't know yourself, you're just a loop replaying yourself, even if there were self-recognition. You can't eliminate your bias -- it's constitutive of you (fool's errand attachment -- political bias -- propaganda persuades no one). That's a fourth problematic. 

And if that were'n't enough -- if Wittgenstein was right about this -- your inner self is the one thing you can't really know. Knowledge pertains to phenomena out there in the world, not the inner world reporting itself

The you-to-yourself, in other words, is a big waste of your time. Instead of dwelling on oneself, dwell out there (where is the mind and what's thought). For actions: play your social role. For thoughts: contemplate the universe and all its systems. Understanding is the gift of human evolution. Don't waste it on an inner loop.

That's my contribution to the gnomiad : )