On finding the way forward

 

We all face the dilemma of decision-making in an uncertain 

and rapidly changing world, the difficulty of making  a master plan, 

and how to go "uphill" in a world that has gravity and thermodynamic 

rules that insist you canonly go downhill.

It's a visual representation of the very simple organic core truth

 of Gerald Loeb's investment strategy and "take one step at a time,

act even in uncertainty, but be willing to reverse course if that doesn't seem to be working"

Loeb claimed he was "right" only  about 20% of the time, but those times he let his money

keep running, and the rest of the time he would instantly recognize that things were not

as he thought and get out of that stock or investment,  admitting the step was wrong,

with perfect humility and not "just waiting till it comes back up again," 

aka

* consultation ( as Baha'is see it )

* group decision making ( management theory )

* collective intelligence ( AI framework)

* swarm-based decision making ( ala bird flocks )

* decentralized collective decision-making  ( no obvious single leader )

* Participatory solving problems through collective action ( Mutual aid )

 
https://youtube.com/shorts/8T-uQ89GJOg?si=ZeNJS_drLMx6yKST

Perhaps another solidly reliable demonstration of this pattern

is the operation of a sailplane ( glider ).    

  This captures also one model of how to

accomplish global resuits (get from point A to point B)

from a rapid iterative cycle of local responsive sampling

and action,  effectively resolving a weak signal in a strong noise environment in

an analog fashion.  

It also captures operation in a world in which the vehicle

can only go "downhill",  bound by gravity and thermodynamics, and yet by

detection and exploitation of external "thermals" the net result is going uphill,

Sailplanes have three levels of input to the pilot,  corresponding to the three

typical inputs to any control system of a "PID controller",  short for 

Proportional-Integral-Derivative signals, ie  the signal itself,  the time integral

of the signal, and the derivative of the signal.  

The cockpit display shows your altitude ( very smoothed of noise ),  your rate-of-climb

( somewhat smoothed of noise), and your instantaneous vertical acceleration  which is fedback to you

via an audio signal whose pitch is instant feedback of whether you are "receiving" vertical lift or not.

The result is that you can immediately sort out what is helping versus what is not helping,

AT THIS EXACT MOMENT,  and guides a turn back into the thermal to circle there until that

train has run its course and you need to exit and go find your next thermal.

By circling in the thermal, you continue to go downhill ( with respect to the surrounding air mass), but you select a locally rising air-mass,  so the net effect of circling downhill is that you gain altitude.

In a noisy but highly variable ( risky, uncertain ) environment,  without a master plan if such could even exist, it is one way to succeed.

Another analog situation of course is the classic "sailboat" operation in which any direction wind can take a good pilot "uphill" to where they want to go.  

So what are the requirements to not simply be blown "downhill" and downwind?

it is critical to have a deep keel, probably even with a large weight at the bottom of it,

and a knowledge and understanding of "tacking" back and forth making "some" progress

uphill with each pass which, by thermodynamics, can only be physically "downhill". 

Again,  both sailplanes and sailboats and I would affirm humanity can only go "downhill",

but with understanding of the process and external energy input ( of any sort, at any 

direction, even variable and random ) but a constant deep keel,  the result we can 

accomplish is net progress "uphill".

I'd say that "Faith" is both the equivalent of a sail and a constant deep keel, that looks 

initially like a burden ( a weight? on the keel?  Something that forces you to go one way when

you want to be "free" to be yourself? ) but turns out to be a help.

Very much trading total freedom of a jellyfish at a local sense level, with no constraints, 

to the higher-freedom of a marathon runner,  who has constraints ( bones !) that "limit"

your motion, yet at the same time enable it.

The fact that we started 14 billion years ago as hot hydrogen and now we have Mozart

and daffodils tells the physics part of my scientific self that there is clearly an external

source of "energy" that conquers the downhill constraints of thermodynamics. 

Things we initially think are in our way are actually helpful.  Take "gravity" for

example.   Yes, it pulls us down, but without it there would be no planet to live on,

no star to heat it, etc.    Without gravity pulling cold air down,  hot air and smoke would

not rise.  Without gravity holding air down,  our airplanes and birds can fly.

 

It is a subtle process and not obvious, which things are our friends and which are our

enemies.

 

Another example of that is the shape and process of ''problems" or "difficulties" or "obstacles".

 Are these "bad things/"

 

Consider what the "obstacles" force us to do!  The Coast Guard develops cutters which right themselves if turned upside down by the waves, as shown in this photo from Morro Bay, CA.  We CAN and WILL overcome obstacles, motivated by the sincere desire to help other people who are in trouble.

 

 

 

The superb book on applied game design by Jane McGonigal argues that people, especially children just LOVE complexity and difficulties, with a catch -- they love intentionally and voluntarily chosen "unnecessary difficulties".

 

Example -- how long would you play golf if the target hole was a meter across and a meter away from the tee?  Maybe once.

Instead we have golf courses with trees in the middle of the fairway, with sharp bends and sand traps and lakes in the way, and a green on a convoluted slope,  Why?? Because we actually enjoy a challenge!  

 

Similarly children love games like Dungeons and Dragons with a thousand complex rules and factors.

 

McGonigal argues that the problem with our jobs at "work" is that they are not designed like great games, like World of Warcraft,  which challenge us and force us to rise to the challenge, collectively.   The joy of rising to an almost impossible challenge, voluntarily accepted, and conquering it together she calls "Fiero" is is what keeps people coming back for more.

 

This is the joy poeple feel in a natural disaster or actual battle which forces us to wake up, get out of our shell, and work together and work up a sweat beating the odds.  People don't want "easy" -- they want FIERO!

 

So when life, or Life, or the gods, or God, or nature gives us true "developmental opportunities" this is actually a gift, without which we would slowly decay and become depressed.

 

The kind of collective love and caring energy we need to stir up and bring to a challenge is not a "zero sum" thing -- the more we use it and try to give it away, the more of it there is to give away next time.

 

It is like the paradox of body building -- the way to get strong is to use up your muscles and become exhausted and weak, and then, overnight ( on good days ),  some kind of magic happens, and you wake up with more muscles than had 3 days ago.  

 

 

 

So don't leap to conclusions about which way is down,  what is a bad thing to happen, etc. 

 

Life conspires to force us to do those things that cause us to come together and collectively advance and evolve socially.

 

 

 The difference is that typical managers or bosses,  using mentality from the 1850's,  give us tasks to do in isolation that suck the life out of us, and exhaust us.    God / Gaia / Life gives us tasks which, net net, result in us becoming individually and collectively stronger and more joyous.  

 

If only all management would learn to follow the pattern that works and produces collective mutual thriving! But for sure, lesson number one, is that our tasks should bring us together and allow us, or even force us, to work together, which means rising to the challenge and learning how to work together, and how to ask for help, and how to give help when asked.

 

The most destructive way we could tackle tasks is to work separately and even compete for credit. 

 

Consider last the concept of an airplane.   Every single part of an airplane is heavier than air, and left to its own devices, put in the air, will fall out and crash.  Yet,  if they work together,  the plane can actually fly.      So can people!   Parts of the plane should not try to steal "flying-ness" from other parts.   The flyingness is not in the parts. it is basically external, tapped into by cooperation and consultation and compassion. 

 

 In fact,  and here other people may have different models and frames and stories, it seems to me that the "flyingness" and "thriving" and "abundance' is all around us, in this space,  trying every trick it has to make us wake up and become open to it flowing into and through us, making us thrive on multiple levels simultaneously.

 

Working together, singing as we do it, in joy and amazement at what we can accomplish - that's a blast! 

 

 

 As people in very cold countries know, the cold can push people together,  not apart,  I think in Sweden it is illegal if passing a motorist stopped with car troubles in winter, NOT to stop and help them out.  

 

 

 

 

 

 

Why the singularity won't be singular

as I posted on techdirt.com

https://www.techdirt.com/2025/06/16/why-centralized-ai-is-not-our-inevitable-future/#comments 

Successful systems in the world around us are massively parallel, not a "Rambo" style super-entity.  supercomputers are actually societies of smaller computers whose net capacity depends more on the operating system and architecture than individual chip speed. 

I posted some other reasons and examples in my blog post of the inevitable result of ever grander AI entities, namely the ever grander Dunning-Kruger effect and ever greater vulnerability to a single point of failure ecologically. 

What GPT needs to do now is not learn everything -- it needs to learn how to ask for help and figure out how to assemble teams of whatever expertise is out there for whatever problem it is working on.  Then growth of individual AI agents is basically done, and, like human society,  the collectivity will now take over evolving. 

 Details follow

================== 

 The Library of Medicine observed back in 1990 or so that we don't need a $billion system - what we need is a billion $1 systems. If only every user could fix just one tiny part of the systems they use that is most clearly broken.

This matches the advice from The Toyota Way,  that many small steps will accomplish the growth you desire, even if it surprises you in the way it gets there.   

It's like the rooted organic growth that is sustainable occurs on the surface of a hypersphere, in the largest 1% shell,  in the domain where tensors are linear and what is "obvious" is most likely also true.

Larger steps are equally "obvious", but are in the non-linear domain and most likely wrong for reasons you cannot see from where you are.

The rise of  "supercomputers" masks the reality that, to accomplish great calculations,  use ten thousand or more smaller computers doing smaller calculations, working together.    The concept of a single huge CPU was abandoned long ago.

 Similarly in AI algorithms,  from 1970 to 1995 or so people tried to create ever larger ever more complex algorithms to do everything,  but finally realized that to accomplish AI what you needed instead was thousands of much smaller algorithms that worked cooperatively.

 I think the Zuckerberg model of a Renaissance AI - able to do everything,  is totally misguided. Now that AI can carry on a conversation,  it doesn't need to also be able to do long division -- there are other systems that do that.   No CEO would do long division in their head or on paper -- you turn to an expert ( eg a calculator ) or delegate to an assistant.    Similarly,   AI only needs one more skill - to be able to figure out which expertise would help it get its job done,  where to find it, and how to tap into it.

 In other words, the growth at this point should be outside the box, not inside the box.  There is no point in trying to reinvent every wheel!

 There is however a much more important problem with the "Rambo" solution to AI.  I cannot do the math but I am fairly certain that any single system is guaranteed to have blind spots that it is incapable of detecting internally.     The result is a sort of Dunning-Kruger syndrome of computing - an idiot-savant model that is very sure it is correct even when it is not.

If we look at the human visual system, there is no single super-cell or super-neuron which collects all the signals of texture and color and edges and shape and creates a percept -- it is a group effort.   "Rambo" class solutions are not used. 

The more of the world the model sucks up and makes subservient to and consistent with itself, the deeper it falls into the pit of blindness.   Like the ecological danger of mono-culture,  you don't want to plant all Chestnut trees, or Elm trees, because when the wrong thing comes along, they all die at once.

Another lesson can be learned from looking at radio telescopes, such as very Very Large Array in Arizona.

There is an absolute limit of resolution of a lens or dish antenna given by the wavelength divided by the diameter of the device.   Cleverly, however, the  device can only sparsely cover the middle ground and still get high resolution along certain axes.   By moving the dishes around, you can change which axes are seen the best.  So, in fact,  today Radio Astronomy uses virtual aperture synthetic dishes that are the size of the planet, with one part located, say, in Sweden and another in Peru. 

 But what is crucial is to maximize the distance between the edges.  In different words, in some sense, to maximize resolution and receiving capacity,  you want the maximum diversity possible.    In the very same sense, to maximize the capacity to generate novel insights, you want a team that has broadly diverse backgrounds and perspectives,  not 20 people who have the exact same background.

Put another way,  any single AI engine is essentially a monopole,  like an electron or a proton, capable only of receiving signals of a certain type.    A monopole won't react to dipolar radiation - for that you need a dipole antenna.   In the hardware tool category, a monopole is a hammer which can pound nicely, but is unable to rotate the simplest nut or screw.  For those you need torque.     The same limits apply if you want to receive gravity waves, which require a quadrapole antenna.   

Conceptually,  as Carl Sagan and Frank Drake pointed out in the SETI project, every time you open a new window in the electromagnetic spectrum,  you discover totally unexpected new phenomena, not just new sides of things you already knew.  Thus we now have infrared, ultraviolet, gamma-ray telescopes, etc.

But every one of those is still looking for dipole radiation.  There are an infinite number of higher rank antenna designs possible that no one has ever tried to create.

The point is that any superintelligent AI system is just a larger monopole, with much more collecting area but still totally blind to most of the universe.  You cannot get around that with scale.

Furthermore,  I suspect that Large Language Models suffer from a fatal defect or limitation -- they are based on language -- which is to say,  on processing serialized strings of symbols.  Very clever processing but still based on linear strings or linked lists, whatever you call them.

Linear strings look good when viewed as Turing machines, where infinite tapes of ones and zeros happily computer anything even if it takes almost forever and takes up almost the the entire universe of space and energy.

Reality, however is not a Turing machine. 

All tapes take energy to maintain and are prone to decay.  The error correcting problem grows too fast.

As anyone who struggled with calculus knows,  if you make a single mistake on an 8 page proof, everything after the error is completely wrong.

 A more powerful approach would be to use as the basis not linear strings, but images, especially now that we have graphics processing chips.  Images are much more powerful and you can take a picture of a dollar bill, change half the pixels to random black or white, and still recognize it.  

Parallel processing is vastly more powerful than serial processing. And "images" don't need to be restricted to two-dimensions,  as multispectral remote-sensing images are multidimensional.

The more components you put in, the more relationships there are to work with.  

Ultimately it is the relationships that hold the critical information that you are trying to capture and process, not the pixels individually.   

So we will almost certainly soon realize that "Large Image Models" are several orders of magnitude more powerful and robust and capable of rich concepts than "Large Language Models" -- regardless how large you make the box, the world outside the box will always trump the world that fits inside the box.

Some would say you could make a sequence of the pixels in a 2-D image, and therefore an image is identical to a string.  This in my mind is only true in the Turing fantasy world.  In a world in which everything takes time and energy,   one GPU cycle to process an image is much more helpful than 20 million pixel operations.

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There is no "best" way to look at it

 

I've started reading Doppleganger by Naomi Klein, and in a true irony the key point seems to be illustrated by the "Klein Bottle"

This is a variant of the Moebius strip 

and reminiscent of M.C. Escher's waterfalls.

And what is described in Escher Godel and Bach as 'strange loops'.

Another favorite example of which is intransitive dice, for which the

term "best" does not exist: whichever die you pick, and you go first,

I can always pick one of the remaining three that will beat your choice

2/3 of the time.

A beats B, B beats C, C beats D, and D beats A.  see wikipedia

 =========================

None of the standard Western education even exposes us to such things that cannot be laid flat, even

PhD level training in most fields does not help.

We seek "better and better" ways to grab hold of ( "grasp") such things, and keep failing, and assume that with just a little more work we will surely achieve our (impossible) goal.

 

In fact,  the whole familiar process of breaking something into parts and looking at each separately is exactly the wrong thing to do.  The problem is not with the parts, the parts are fine, the problem is the relationship between the parts.

The mountain will not come to Mohammed, we must go to it.

There is no "right way to look at it." that surely additional conversation and discourse will lead us to.

I suspect that all serial-string logic has this weakness, and "step-by-step" progress along the string remains open to such twisting.

 

I think we need to go to at least 2-dimensional, "image-processing" techniques, where there is a huge amount of cross-structure that prevent skew and twisting, like the diagonals that strengthen the very weak rectangular shape on bridges which otherwise could collapse sideways.

 

While I recognize that this concept makes me a heretic, I suspect as well that the "Word of God"

cannot be represented in any language using serial symbol strings, ie what we call "words". 

 

Not the least of which have the problem that the "meaning" of a "word" is highly context

dependent, so the meaning of any sentence will change over time. 
 

This is why for example

the US Supreme Court is always stuck with the problem of trying to figure out how to 

interpret what prior decisions,  especially those over 100 years old,  actually mean.

 

What was the intent, and do those "words" continue to express that intent, and should 

they be true to the intent, or true to the words ( and what those words mean today?).

 

So, sadly, even if say the words of the Koran are kept sacred and unchanging,

the meaning people will take from them keeps changing over time.

It is really inconvenient that simple approaches to seeking truth and meaning don't work.

Partially for this reason this blog is titled "tree-circles", a whole different way to find

meaning in signals that is neither deductive nor inductive.

Another problem with using 1-dimensional symbol-strings  ( "sentences of words")

is that this type of reasoning is incredibly noise-sensitive.   In theory perhaps for

a perfect theoretical "Turing Machine" various things are considered "computable"

but in the world our bodies live in ,  nothing is without noise, and nothing lasts

forever, and most things decay rapidly - so the infinite immutable "tape" of 

1's and zero's cannot ever be implemented. 

So, even in the golden haired child of Science,  algebra and calculus, 

a single error becomes a single-point-of-failure.  A 20 page computation

with an error on page 3 is meaningless and "wrong". 

Theologians may argue forever over "the meaning" of a single word,

but their whole process is inescapably flawed, not to mention

context sensitive over time.

 

On the other hand, images are very noise-tolerant, so that even with multiple "errors" the 

end result may still be correct. You can "see through" the "salt and pepper" noise and 

realize what this image represents.

I got in a great deal of trouble in my computer science classes for arguing that, if the founders had image processing chips and computers,  the whole field would have been based on image processing not symbol processing.    And we would now have

"context processing engines" not just "content processing engines".      Turns out I made my arguments the week our department chair had just won the Turing Award.   Bad timing on my part.  And meaning or ability to engage and be heard depends so much on timing and fickle social context.  People never finish reading even your sentences or argument, but stop part way in, "auto-complete" your meaning into something they grew up with, and discard your work as being wrong since that meaning is wrong.

At least computer science has finally come up with Kubernetes and similar technology to save the entire context along

with application "code"  ( symbol strings. )

 

Nevertheless,  the current golden-haired child of AI is "Large Language Models",  by which they mean a huge amount of language.

 

It seems safe to predict that this will migrate and evolve towards Large Image Models, or other 2-dimensional and larger bases.

The symbols in any language essentially, like even DNA,  have epigenetic auras,  more than just metadata, which alters the meaning of the symbols enough to matter, enough that if you ignore it,  your answers will not be stable or reliable.