<div dir="auto"><div><br><br><div class="gmail_quote gmail_quote_container"><div dir="ltr" class="gmail_attr">On Thu, Feb 12, 2026, 6:59 AM John Clark <<a href="mailto:johnkclark@gmail.com">johnkclark@gmail.com</a>> wrote:<br></div><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div dir="ltr"><div dir="ltr"><div dir="ltr"><div class="gmail_default" style="font-family:arial,helvetica,sans-serif"><span style="font-family:Arial,Helvetica,sans-serif">On Wed, Feb 11, 2026 at 10:29 AM Jason Resch via extropy-chat <</span><a href="mailto:extropy-chat@lists.extropy.org" style="font-family:Arial,Helvetica,sans-serif" target="_blank" rel="noreferrer">extropy-chat@lists.extropy.org</a><span style="font-family:Arial,Helvetica,sans-serif">> wrote:</span></div></div><div class="gmail_quote"><div><br></div><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex"><div dir="auto"><div dir="auto"><i><font size="4" face="georgia, serif"> <span class="gmail_default" style="font-family:arial,helvetica,sans-serif">> </span>consider that the brain only gets neural spikes, from different nerves at different times.These are just symbols, bits.</font></i></div></div></blockquote><div><br></div><div><font size="4" face="tahoma, sans-serif"><b>Yes<span class="gmail_default">, and a neuron cell does not understand how the external physical world works; the entire individual does but he can directly interact with that world. </span> </b></font></div><div> </div><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex"><div dir="auto"><div dir="auto"><font size="4" face="georgia, serif"><i><span class="gmail_default" style="font-family:arial,helvetica,sans-serif">> </span>And yet, from these mere patterns of firings, the brain is able to construct your entire world.This test is no less magical than what LLMs can do from "just text".</i></font></div></div></blockquote><div><br></div><font size="4" face="tahoma, sans-serif"><b>I don't think either one is doing anything "magical", it's just that I don't have a deep understanding of how they are able to do what they do, and most of the top researchers at the AI companies admit that they don't either.</b></font></div></div></div></blockquote></div></div><div dir="auto"><br></div><div dir="auto">There are different levels of explanation.</div><div dir="auto"><br></div><div dir="auto">We can explain what happens at high-levels: (UAT, Function approximation, why good token prediction functions require real world understanding/modeling), etc.</div><div dir="auto"><br></div><div dir="auto">And we can explain what happens at low-levels: neuron activation, weights and biases, feeding forward of signals from one level to the next, etc.</div><div dir="auto"><br></div><div dir="auto">Where we get lost is in the myriad details of the middle. The collective weights of billions or trillions of parameters, the millions of neural circuits tuned to recognize any of tens of thousands of distinct patterns, the wiring diagram linking and relating this vast number of internal mid-level functions and algorithms.</div><div dir="auto"><br></div><div dir="auto">And the same is true for any complex system, whether it be computer software or the human brain. We can understand Microsoft Word at a high-level, through it's top level interface. And we can understand what happens at the bottom-level with transistors and wires, and logic gates. But no human on earth can fully comprehend it's 50,000,000 lines of code.</div><div dir="auto"><br></div><div dir="auto">The brain is like that too. We understand high-level psychological behaviors, motivations, desires. And we understand how neurons work. But no human has a chance of fully understanding the 700 trillion connections and what they all mean nor the algorithms behind how every function and subroutine the brain computes.</div><div dir="auto"><br></div><div dir="auto">This is what I call, "the complexity of the middle." What we know from our quantification of its complexity is that a detailed non-summarized full understanding of it will forever be beyond our grasp with our human-level intelligence. We lack the capacity to grasp billion- or trillion-part machines. And that is what our brains, and current LLMs, are.</div><div dir="auto"><br></div><div dir="auto"><div class="gmail_quote gmail_quote_container"><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div dir="ltr"><div dir="ltr"><div class="gmail_quote"><font size="4" face="tahoma, sans-serif"><b><br></b></font></div><div class="gmail_quote"><br></div><div class="gmail_quote"><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex"><div dir="auto"><div dir="auto"><div class="gmail_quote"><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex"><div dir="ltr"><div class="gmail_quote"><div><font size="4" face="tahoma, sans-serif"><b><span class="gmail_default">>> OK, I can see how you might be able to determine that a book written by ET was about arithmetic, and that some of the symbols represented integers and not letters, and that a base 10 system was used. But I don't see how you could use the same procedure to determine that another book was about chemistry unless you already had a deep understanding of how real world chemistry worked, and I don't see how you could obtain such chemical knowledge without experimentation and by just looking at sequences of squiggles. But apparently there is a way. </span></b></font></div></div></div></blockquote></div></div><div dir="auto"><br></div><div dir="auto"><font size="4" face="georgia, serif"><i><span class="gmail_default" style="font-family:arial,helvetica,sans-serif">> </span>A student can learn a great deal from reading a chemistry text books, without ever entering a lab and taking out a beaker.</i></font></div></div></blockquote><div><br></div><div><font size="4" face="tahoma, sans-serif"><b>Yes but before the average student has even open <span class="gmail_default">a</span> chemistry book he has already been exposed to nearly two decades of real world experience and has an intuitive feel for things like mass, velocity<span class="gmail_default">, heat</span><span class="gmail_default"> and</span> position<span class="gmail_default">,</span> so he c<span class="gmail_default">an</span> understand what the book is saying, </b></font></div></div></div></div></blockquote></div></div><div dir="auto"><br></div><div dir="auto">But for the human brain to read that point , it has to construct it's full understanding of the real world using nothing more than the *raw statistics* as it finds in the patterns and correlations of nerves firing at different times.</div><div dir="auto"><br></div><div dir="auto">This is all the brain ever sees of the outside world. The student didn't receive images and sounds and smells, the brain had to invent those out of raw "dots and dashes" from the nerves.</div><div dir="auto"><br></div><div dir="auto">If the brain can do such a thing, then is it any more surprising that another neural network could bootstrap itself to a degree of understanding from nothing more than data that contains the statistical correlations?</div><div dir="auto"><br></div><div dir="auto"><br></div><div dir="auto"><div class="gmail_quote gmail_quote_container"><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div dir="ltr"><div dir="ltr"><div class="gmail_quote"><div><font size="4" face="tahoma, sans-serif"><b>but that would not be the case for an AI that has never observed anything except a sequence of squiggles and understood almost nothing except arithmetic and the laws of logic. It might have an intuitive understanding of time but not of space, <span class="gmail_default">and </span>from such a humble beginning I don't understand how anything could deduce Newtonian Physics, much less Quantum Physics which would be required to understand modern chemistry. </b></font></div></div></div></div></blockquote></div></div><div dir="auto"><br></div><div dir="auto"><br></div><div dir="auto">Think about the problem from the perspective of a human brain, alone inside in a dark, quiet, hollow bone. With only a fiberoptic cable connecting it to the outside world. This cable sends only bits. The brain must figure out how to make sense of it all, to understand the "real world" from this pattern of information alone.</div><div dir="auto"><br></div><div dir="auto"><br></div><div dir="auto"><div class="gmail_quote gmail_quote_container"><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div dir="ltr"><div dir="ltr"><div class="gmail_quote"><div><b><font face="tahoma, sans-serif" size="4"><span class="gmail_default"> </span> </font></b></div><div><br></div><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex"><div dir="auto"><div dir="auto"><div class="gmail_quote"><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex"><div dir="ltr"><div class="gmail_quote"><div><div><font face="tahoma, sans-serif" size="4"><b><span class="gmail_default" style="font-family:arial,helvetica,sans-serif">>> </span>The existence of isotopes<span class="gmail_default"> would greatly complicate things, for example we know that the element Tin has 10 stable isotopes and 32 radioactive ones, they all have identical chemical properties but, because of neutrons, they all have different masses. </span></b></font></div></div></div></div></blockquote></div></div><div dir="auto"><br></div><div dir="auto"><font size="4" face="georgia, serif"><i><span class="gmail_default" style="font-family:arial,helvetica,sans-serif">> </span>The periodic table, and Wikipedia's article on each element, lists atomic number (number of protons) in addition to atomic weights.</i></font></div></div></blockquote><div><br></div><div><font size="4" face="tahoma, sans-serif"><b>But how could the poor AI<span class="gmail_default"> make sense out of that Wikipedia article if it had no understanding of what the sequence of squiggles "w-e-i-g-h-t-s" even means? I don't deny that it can understand what it means, I just don't know how. </span> </b></font></div></div></div></div></blockquote></div></div><div dir="auto"><br></div><div dir="auto">I gave you an example, regarding Pi, and surrounding words. Think of all alien civilization trying to decide our Wikipedia. Think about how what words always follow "the." Think about how a hierarchy of words can be constructed by seeing which words are found in the pattern "X is a Y" at the top of this hierarchy you'll find the most general words like "object" and "thing", and a while tree structure revealing a hierarchical taxonomy of real world things. </div><div dir="auto">For example:</div><div dir="auto"><br></div><div dir="auto"></div><div dir="auto"> Being</div><div dir="auto"> |</div><div dir="auto"></div><div dir="auto"> human</div><div dir="auto"> / \</div><div dir="auto">Man Woman</div><div dir="auto"><br></div><div dir="auto">The entire structure of all real world objects can thus be extracted even if the meaning of these words just yet known, just by looking at the patterns around the word "is".</div><div dir="auto"><br></div><div dir="auto">Consider the linguistic pattern "X is made of Y"</div><div dir="auto"><br></div><div dir="auto">Following this linguistic pattern, reveals a hierarchy where the top most level is things like "atom, fundamental particles, and quantum fields" it reveals the fundamental nature of our reality. Things whose dictionary entries and encyclopedia articles are explained in mathematical equations, rather than in words referring to other things.</div><div dir="auto"><br></div><div dir="auto">Now consider: what more might one learn about our world from examining any of the millions of other words, and the linguistic pattern at surround each one?</div><div dir="auto"><br></div><div dir="auto"><div class="gmail_quote gmail_quote_container"><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div dir="ltr"><div dir="ltr"><div class="gmail_quote"><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex"><div dir="auto"><div dir="auto"></div></div></blockquote><br><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex"><div dir="auto"><div dir="auto"><div class="gmail_quote"><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex"><div dir="ltr"><div class="gmail_quote"><div><div><font size="4" face="tahoma, sans-serif"><b><span class="gmail_default">>> Perhaps Mr. Jupiter Brain </span>could deduce the existence of the physical world<span class="gmail_default"> starting from nothing but arithmetic (but I doubt it) however it is certainly far far beyond the capability of any existing AI, so they must be using some other method. I just wish I knew what it was. </span></b></font></div></div></div></div></blockquote></div></div><div dir="auto"><br></div><div dir="auto"><br></div><div dir="auto"><font size="4" face="georgia, serif"><i><span class="gmail_default" style="font-family:arial,helvetica,sans-serif">> </span>Are you familiar with the universal approximation theorem?</i></font></div></div></blockquote><div><br></div><div><font size="4" face="tahoma, sans-serif"><b>Yes,<span class="gmail_default"> a neural network can model any continuous function with arbitrary precision, but the vast majority of continuous functions do not model anything fundamental in either Newtonian or Quantum Physics, so</span></b></font><b style="font-family:tahoma,sans-serif;font-size:large"><span class="gmail_default"> how does an AI differentiate between those that do and those that don't? </span> </b></div></div></div></div></blockquote></div></div><div dir="auto"><br></div><div dir="auto">Are you asking how neural networks learn functions from samples of input and output? If so then I would refer you to back propagation and gradient descent.</div><div dir="auto"><br></div><div dir="auto">If you asking how an neural network can approximate any computable logic circuit? If so then I would refer you to Hillis's "Pattern on the Stone":</div><div dir="auto">"The first thing to notice about artificial neurons is that they can be used to carry out the And, Or, and Invert operations. [...] Since any logical function can be constructed by combining the And, Or, and Invert functions, a network of neurons can implement any Boolean function. Artificial neurons are universal building blocks."</div><div dir="auto"><br></div><div dir="auto">If you are asking how it's possible in principle to bootstrap meaning from the raw patterns and relationships of words found in text, I would refer you to what I wrote above.</div><div dir="auto"><br></div><div dir="auto">If you are asking something else not covered here, you will need to be more specific.</div><div dir="auto"><br></div><div dir="auto">It may be that what you are asking falls within "the complexity of the middle", in which case I hope you can appreciate why no comprehensible answer can be provided.</div><div dir="auto"><br></div><div dir="auto"><br></div><div dir="auto"><div class="gmail_quote gmail_quote_container"><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div dir="ltr"><div dir="ltr"><div class="gmail_quote"><br><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex"><div dir="auto"><div dir="auto"><font size="4" face="georgia, serif"><span class="gmail_default" style="font-family:arial,helvetica,sans-serif">> </span>"What does it mean to predict the next token well enough? [...] It's a deeper question than it seems. Predicting the next token well means that you understand the underlying reality that led to the creation of that token."</font></div><div dir="auto"><font size="4" face="georgia, serif">-- Ilya_Sutskever (of OpenAI)</font></div><div dir="auto"><a href="https://www.youtube.com/watch?v=YEUclZdj_Sc" target="_blank" rel="noreferrer"><font size="4" face="georgia, serif">https://www.youtube.com/watch?v=YEUclZdj_Sc</font></a></div></div></blockquote><div><br></div><font size="4" face="tahoma, sans-serif"><b>Ilya Sutskever certainly knows a hell of a lot more about this than I do so maybe he's right.</b></font></div></div></div></blockquote></div></div><div dir="auto"><br></div><div dir="auto">I don't see how it could be wrong. Short of this explanation, how can we explain that LLMs can play chess? To play chess well requires a model/function that understands chess, how the pieces move, relate, attack, and what the goal of the game is. This is far beyond a mere "stochastic parrot" as some have attempted to describe LLMs as being.</div><div dir="auto"><br></div><div dir="auto">Jason</div><div dir="auto"><br></div><div dir="auto"><br></div><div dir="auto"><div class="gmail_quote gmail_quote_container"><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div dir="ltr"><div dir="ltr"><div class="gmail_quote"><br></div><div class="gmail_quote"><font size="4" face="tahoma, sans-serif"><b><span class="gmail_default"> John K Clark</span><br><br> </b></font><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex"><div dir="auto"><div dir="auto"><br></div><div dir="auto"><br></div><div dir="auto"><div class="gmail_quote"><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex"><div dir="ltr"><div class="gmail_quote"><br></div></div></blockquote></div></div><div dir="auto"><br></div><div dir="auto"><br></div><div dir="auto"><div class="gmail_quote"><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex"><div dir="ltr"><div class="gmail_quote"><div><div><font size="4" face="tahoma, sans-serif"><b><span class="gmail_default"> By chance you might be using the best possible compression algorithm on your data, but there's no way to prove to yourself or to anybody else that you are.</span></b></font></div><div><font size="4" face="tahoma, sans-serif"><b><span class="gmail_default"><br></span></b></font></div><div><font size="4" face="tahoma, sans-serif"><b><span class="gmail_default"> John K Clark</span></b></font></div><div><br></div><div><br></div></div></div></div></blockquote></div></div></div>
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