I won’t respond further.

Understood – a common response, to uncommon ideas.

Having truly “said what I meant”, is a rather different issue than: Is what has been said, a true depiction, of the nature of reality?

My critiques were directed against the positions you are maintaining, not your person – and your abrupt disinclination to defend them, against the likes of me.

And the reason being… reminiscent of the interlocutors frequently depicted in Plato’s Socratic dialogues.

But like the Moon, Reality is a harsh mistress.

This is my attempt at a short response to all the issues being raised here. Longer discussions about these issues exist in my book and on-line elsewhere.

My “common response, to uncommon ideas” was a somewhat cryptic allusion to, but a typical example of, what has become common enough, at least in modern physics, to have earned its own name – Planck’s Principle, named after the physicist Max Planck – the “establishment” is highly disinclined to accept any new idea, even when it happens to be true. And since it is true, they cannot refute it. So as soon as they realize that it is difficult to refute, they will simply switch to ignoring it, and refuse to consider it any further. The linked example deals with how misconceptions about the nature of “Information”, have generated a complete misunderstanding of Quantum Physics. The problem here concerns how the exact same misconceptions, generate the misunderstanding of Intelligence and indeed, all of reality. Hence the very deep connection. Same problem, just two different situations, at opposite ends of the complexity spectrum; an individual “quantum” of information, versus a brain embodying an astronomical amount of information, in the process of constructing its own, individual account, of the reality it has encountered.

in neuroscience, your conclusions are not too unconventional.

Thirty years ago, when I first proposed that it would require the equivalent of a one hundred million giga-flop processor, to perform anything like the functioning of a human brain, the conventional wisdom of the day was that, that was a ridiculously high value.

At that time, the newest, most powerful supercomputer, could perform the conventionally accepted estimate for a brain, so people at a conference discussing this matter, were wondering why that supercomputer could not be made to behave as if it had anything even remotely approaching a human-level of intelligence. I then mentioned to one of the conference participants, that the reason is obvious – the conventional estimate is off, by a factor of a million, and it will be another 30 years before even a supercomputer comes close to the actual requirement.

Many people are still surprised by how complex a neuron is; https://www.quantamagazine.org/neural-dendrites-reveal-their-computational-power-20200114/

Today, my old estimate has become the convention, which is why that no longer appears to be “too unconventional.”

You say that (quoting) “all the misconceptions about the nature of reality” (all?) are because we don’t realize our “senses evolved to keep us alive”.

But that is not what I said. This is what I said, in reply to someone else, that I was quoting:
“There is no evidence at all that ‘Our senses evolved over billions of years to ever more accurately experience our Existing.’ Believing that false assumption is responsible for all the misconceptions about the nature of reality. Our senses evolved to keep us alive, not to accurately experience anything. Our survival only requires “precise” experiences, not “accurate” experiences; the failure to appreciate that distinction, is the ultimate source of a great deal of misunderstanding about the nature of reality.”

You have completely failed to take notice of the very distinction, that I was emphatically pointing-out because it is central to the above noted misconceptions about the nature of “Information.”

The conventional view of our senses, is that they have evolved to accurately inform us about “what is out there.” But in reality (an unconventional view) they have evolved only to tell us if “what is out there” happens to perfectly match, the only things (certain types of signal modulations) that they ever actually look for; everything else is being completely ignored. Because in Information Theory, “Information” can only be acquired by detecting the presence of the only things that you actually know how to perfectly detect. (A most unconventional conclusion, proven to be TRUE 75 years ago, but entirely ignored by virtually everyone outside of the Information Theory field, because it fell victim to Planck’s Principle.) Hence, Neural Networks must ultimately be all about the implementation of the “matched filtering” processes required to detect the only things (Information) being sought for (both internally and externally). Such processes play a central role in enabling modern, high-performance, communications systems to function – by correctly detecting Information. And the same appears to be true of the brain. Because, as Shannon discovered 75 years ago, “Information” (his very unconventional conception of information) can only be reliably detected via such processes.

So any system that needs to deal with moving around enormous amounts of “Information”, is going to have to employ the kinds of processes that Shannon identified, as the only ones that can actually accomplish such a task, without hopelessly garbling and degrading that “Information”, as the result of frequent “copying” errors. That is where the Matched Filtering comes into play. (and that, by the way, is what double strands of DNA are, in effect, doing, in order to prevent copying errors; one strand “matches” the other; DNA that is subsequently used to construct brains, that employ the same underlying “Information” processing principles.)

In essence, matched filtering is somewhat analogous to the pattern matching that Searle imagined in regards to his Chinese Room. But one that is guaranteed to almost never make an error, such as misreading a message (a rather likely misfortune, in a system processing astronomical numbers of messages every second, without any such guarantee). And as Shannon demonstrated, such processing (getting close to the “never” error) does not come particularly cheap. Which is why it took decades, to reduce the cost of computing enough, to begin using these kinds of techniques to enable systems like cell-phone networks, and now, neural networks – to arise.

So, a brain requires enormous processing power, precisely because, it is in effect, a brute-force implementation of a very sophisticated, guaranteed-to-work, enormous version of a (somewhat limited) Chinese Room (guaranteed in the sense that it will successfully accomplish whatever it has been successfully trained to perform, i.e. it will not fail due to frequent “copying” errors). A Chinese Room that processes sensory inputs, language inputs and various other inputs as well, to generate appropriate (as determined via previously remembered experiences) responses (effects) to whatever inputs trigger a “match”, which in turn, then triggers (causes) the previously associated response (effect).

Tiny versions of such things (based on simplified models of very small sets of neurons), have played a central role in modern communications systems, for decades. Building vastly larger (trillion fold) versions of such things, capable of processing all kinds of signals, not just specialized communication signals, is what AI Neural Network development is, in effect, attempting to achieve, even though few people (including the developers) seem to realize that fact. An interesting observation in this connection is that this becomes possible, because it is not necessary for the developers to understand any process that happens to be sufficient at developing itself. But the sufficiency condition is limited by the processing power, because that determines the Match filtering performance. When you only have enough power to build one matched filter, to detect one bit of Information, Heisenberg’s Uncertainty principle arises, and quantum behaviors emerge as a result. But at the other end of the complexity spectrum, the upper limit is only limited by the available processing power.

An aside:
“Self awareness” seems to emerge as a minor (but highly consequential) side-effect, by having a small part of the system begin to perform (via happenstance) these same types of processes, on its own internally generated signals, rather than just its external input signals. In other words, it begins to “work-out” the “causes and effects” within itself, and not just those pertaining to the external world. For example, determining (via brute-force trial-and-error) what will cause its own visual sensors, to point in some particular direction. “Self” eventually became associated with “that which can be controlled” via “will power alone”. Thus, a “self” appendage, like an arm, can be caused to move, by learning (trial and error) how to just “will” it to move, via some causative, internal signal routing. So human babies learn to walk etc. But no such “willing” ever seems to cause an external tree’s appendages to move, like a baby’s own legs can be made move, so they, and most other things, become categorized as “non-self.”

In regards to the “interlocutor’s criticism of your sensory deprivation case”, you have again failed to notice the distinction that I was endeavoring to point-out. The interlocutor was not criticizing my case, he was criticizing his own strawman case. In essence he merely said: a system that has only been trained to process language, cannot process sensory data.

So what? The underlying neural network can be trained to process both. So why on earth restrict it to just one, and then bother to state the resulting truism; that there will then be no role for the other – “there is no role for the sensory inputs in a large language model”?

It is important to keep in mind here, that the only reason why things like chess and language performance, ever became a standard, for gauging AI, was because that is what had previously been used to informally gauge the performance of “smart” humans. So why not demonstrate that an AI can beat them at their own games:

“Dumb jocks” played football. “Smart geeks” played chess. So if someone wanted to claim that a machine is “acting smart”, it needed to beat the chess team, not the football team.

But humans don’t like to lose. So they kept moving the AI’s goalposts. As soon as the AI did beat the chess team, the humans said, Oh Wait! It’s not chess that matters when it comes to being “smart”, it’s Go, or Backgammon, or Jeopardy. And most recently, language. And now that the language team looks like it is about to be defeated, the humans are scrambling to find a new challenge, to enable someone, to once again proclaim “but we are still number one, because the AI has still not…”

But it’s just a matter of time, because reducing the size and cost of the required Matched Filtering Computing Power, takes time. About another decade – then game over – every game. Not an AI brain in a clean-room vat, untaught by Reality’s Harsh Mistress, but smart, autonomous robots – lots of them – more tightly organized than any ant colony. And a whole lot “smarter” than any society of humans.

Technologically, not just possible, but highly probable, at an affordable cost – if we humans endeavor to “make it so.”

I discussed the likely consequences of doing just that, at some length, in the book I wrote, thirty years ago.

Finally, for all the philosophers out there (this is after all a philosophy forum), Shannon’s unconventional conception of information demands (it is necessary) that all information, by definition, be completely meaningless. That coupled to its perfect copying attribute, is what makes it perfectly objective; every entity that knows, a priori how to detect information (knows the necessary Matched Filters etc.), is guaranteed to detect exactly the same thing. Hence, any and all meaning of all information, must be “slapped onto” it, like a post-it note, thus making all meaning perfectly subjective. That is how the distinction between objective and subjective arises, built upon a foundation of “Information.” So all of observable Reality is perfectly meaningless, until something comes along (be it a quantum or a human) and slaps on the “post-it note” that gives it the only meaning that it can have. So, while Brains are not Blank-Slates, all the “Information” being processed by them must be. So the bad news is that there is no meaning at all, to the cosmos, life, or anything else, until something, like a quantum, and then us, comes along and “makes it so.” But the good news is, we, everything, are free to create our own meaning in this world, all of it, for better or worse. Reality is a harsh mistress indeed. We are each like the Phoenix, arising from whatever ashes we are rooted in.

And that brings us back to the fundamental quantum. A quantum is just a matched filter for a single bit of information – it can correctly detect another “Identical” quantum, but nothing else, because it is an identical quantum’s matched filter (technically, they auto-correlate). It becomes the Matched Filter, by simply being the Matched filter (being a type of twin) and thus cannot possibly behave otherwise. That is what all the fuss about “entanglement”, the EPR Paradox, and Bell’s theorem is ultimately all about – the Matched Filter correlation behaviors of “Identical Twins” vs. “Fraternal Twins.”

And strangest of all, for all this to work, Shannon deduced that “the transmitted signals must approximate, in statistical properties, a white noise.”
(https://www.quantamagazine.org/pioneering-quantum-physicists-win-nobel-prize-in-physics-20221004/#comment-6127386192)

In other words, the very phenomenon known as deterministic cause and effect, itself is an emergent property, arising entirely from the statistical, Matched Filter properties of pure noise! Existence itself as a detectable “thing” arising from chaos – a giant Phoenix. In Shannon’s limiting case, an entity cannot reliably detect the existence of another entity, unless the entities behave, statistically, like meaningless noise. Which is why Information must be meaningless – white noise is the only thing that can be reliably (perfectly) detected, under the worst of conditions – buried in noise, chaos. It is the only thing that can be “pulled out” (via its auto-correlation property) and thus “emerge”, not quite out of “nothing”, but out of chaos – disorder – noise – ashes – The Phoenix.

We are Strangers in a Strange Land indeed.

But this next Phoenix will be different.

Vaster than Empires and More Fast.

Now consider the old question about why neurons do not communicate with nice, clean-looking bit-streams, or some similar coding system. Why employ a noise-like encoding of neural spikes?

Because, as Shannon realized, clean-looking encodings, cannot survive the transmission and retransmission “down-the-line” (think of the systematic degradation encountered in a xerox copy, of a xerox copy, of a xerox copy…) due to all the build-up of noise and interfering “cross-talk”, between all the crowded neural circuitry in a brain:

Survival of the fittest signal-encoding schemes – which, as Shannon discovered, happen to be those, in which “the transmitted signals must approximate, in statistical properties, a white noise.”

It’s me again. I wonder about your explanation that AI is good at chess, instead of football, because computer scientists tend to be people who were better at chess (is that fair? There seems to be this fair request of you asking to be quoted exactly, but even when quoted exactly you repeat the same point while saying the other person “completely failed” to understand you). Maybe that is a small part of it. I think there are other or coincident explanations that are about differences between chess and football (for example one can be played–and well–while blindfolded in another room, the other can’t…and many others). Also I’m in a major science…the vast majority of PIs, students, businesspeople, lay people…are not working on their project because it is related to something they are personally good at or they personally enjoy. They are working on what has the best chance to succeed, as cogs in the giant business/enterprise of science (I’m not criticizing that, I’m part of it, too…)…I suspect that is also true of computer science, but don’t know (i.e., it is a hard historical/technical claim with no simple answer).

Regarding your “moving the goalposts” claim…for one example (that you referenced), you may be aware the “Go” programs were beaten recently by humans because of their failure to generalize a “group of pieces”. You probably know this problem was also discussed by Minsky and Papert in the 1960s; it’s been a problem for 50 years or more (generalizing simple concepts as “perceptions”). That is (just one of many) one small reason some say that the computer software is amazing but not anything-like intelligent, it’s not because we don’t want to admit progress on computer software (we “don’t like to lose”). I think the progress has been amazing and will continue to be amazing…it will dominate more of our lives, maybe be dangerous…probably just as you say. But you have staked out this rhetorical high ground where everytime admittedly amazing progress happens in one area, it becomes more evidence your much broader theory is true (otherwise the rest of us are “moving the goalposts”). That isn’t a falsifiable approach.

So I would suggest you take the opposite approach…what would falsify your theory? What conditions or experiments would prove you are wrong? What are the biggest worries for your theory? You say how entrenched is everyone by dogmatism, but you may be surprised to hear I am thinking the opposite is true. On these few online conversations of which I am aware, I see it is actually you who is expressing 100% certainty, comparing yourself to Socrates, belittling the approach of others, while it is actually your opponents who are urging caution, suggesting things may be harder than very simplistic answers, suggesting we need to be more humble in the face of conflicting evidence, etc. Just my 2cc.

Typo:

they are still coming forward today

should read “they are still not coming forward today”

Such is life – an example of the very issue that Shannon’s discovery addresses – drastically reducing the error rate, in communications. Humans are not very good at this. Societies of machines will soon be much better – hence, much smarter.

What do you think (as I mentioned) about the failure of “Go” programs to generalize “group of pieces”–and consequently to be beaten by humans even with large starting deficits–and that same problem as it was formulated over 50 years ago, still apparently unresolved in many cases (failure to generalize to perform simple math, perceiving a different animal when small number of irrelevant pixels are changed, and many others of this kind)? You skipped over that, but it was my only point by which I was not merely speculating.

Regarding your reformulated story about why AI is good at chess and not football (I won’t paraphrase it because I seem to keep paraphrasing you wrong). I still think I am right: there are other and coincident stories about the differences between football and chess, that I mentioned above, that are just as plausible. It is an historical claim you raise that seems to me to be hard to answer.

Regarding your two falsifications. On the first, you will forgive me for never having heard of your paper, before this thread. At least for me, I will go into the beginning stages of carefully thinking about it. I don’t want to be unfair to you and say you can’t be right in that paper, if I haven’t read it. I also don’t want to say “I’ll read it right away!” and sound patronizing, or dismiss as impossible that you have resolved this longstanding problem. I will give this paper of yours a fair shot, on a timescale beyond this conversation.

On your second point, many people say that (again, popular scientists like Sam Harris say that constantly…this is why I keep saying your view is not unusual). What I don’t like about this one is there is too much wiggle room. If we reach some specified level of computing power, and the machines don’t meet some definition of intelligence, at that point you could just say “oh that is only because the computing power was not even greater…” (maybe you wouldn’t, but you could)…alternately, if machines are better than us at every task just as they are now at (say) being calculators, but still are bad at common sense generalizations…in that case we will be right back where we are now of you saying there is even more evidence you are right and me saying there is not. So this one aspect of your view seems to amount to “if machines have enough computing power to be intelligent, then they will be intelligent” but at the same time you are rejecting arguments, made in the present, that intelligence is not “computing” something. So at least on this one point, I don’t see how we make progress (in this conversation). I am open to how I am wrong.

To your last post, I assume you are the “person of inquisitive disposition”, not me, and that you are the one who “reality” instructs, not me. For my part, I am not going to get frustrated like your other interlocutors, so I would suggest dropping this sort of rhetoric–not because it is frustrating to others, or because in my opinion you are actually in the opposite role compared to what you think, like I said above…but simply because it doesn’t help any of your substantive points.

Regarding… your paper… I will go into the beginning stages of carefully thinking about it

Given what I have already told you, you probably will not require more than a few minutes, to understand my simple point, and also why physicists “won’t touch it, with a 10 foot pole”:

If you take a nice, clean, polarized “coin” (as imagined/assumed by physicists), and paste a “QR-like code” onto it, then Shannon’s astonishing discovery, that “the transmitted signals must approximate, in statistical properties, a white noise”, will ensure that any pair of “entangled coins” that have the same “QR-like code” applied to them, will behave very differently (like Identical twins), in “Bell Tests”, compared to any pair of ” entangled coins” that have had a different, but similar “QR-like code” applied to them (Fraternal twins).

And that ability to distinguish Identical-twins from Fraternal-twins, will remain, even after each pair has been severely “band-limited” (been blurred beyond normal recognition) to ensure that they only manifest a single-bit-of-information, as defined by Shannon.

Thanks for the discussion.

As for the future of real AI, being of a certain age, I will not have to deal with it. At least not for long.

My advice to those that will: put the games away and start looking at Reality, through the new “lens” that Shannon discovered, thereby altering our view of Reality, just as it was altered 400 years ago, when first viewed through Galileo’s new lens. They both opened up a whole new way of looking at the world. And in that regard…

I had not thought about the Cosmic Microwave Background Noise, as a possible form of Low Probability of Intercept signaling, forming a Cosmic-Civilization-equivalent, of the “noise-like” signaling between neurons, until our conversation, triggered the thought. Thanks for that.