The FARG revolution & its consequences

CHAPTER 1. Introduction.

“Write drunk; edit sober.” — Hemingway, they say

Your heart races, but you know you have crossed the point of no return. There is no escape from this predicament. Call it intellectual terror. You have fallen into the trap; another fly kicking and screaming in the tarantula’s web. There is no more hope. All of your choices have been ruthlessly taken away. A poor soul; taken hostage. You cannot go to the next message, not anymore. You cannot close the tab, not anymore. Well you can still close the tab, it’s not like I have hacked your computer and it won’t do it. But you are too curious for your own good.

Take the loss. Face the pain.

There is no way out of this disgrace—there is only going through it.

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CHAPTER 2. Looking backwards…

“A apoteose do dedão.” — Luis Fernando Verissimo

Dear FARGonauts,

10 years ago, something had been bugging me. If I bought a new computer I would install all these programs and tools that we all do. But where was the good stuff? The FARG projects? Where’s the one button I press to get all of them in my new fancy machine? It was a state of “Sparse Distributed FARG”, and the internet we had was a Kaput Internet. So on Sunday, 30 July 2017 at 18:40:56 (UTC-3) exactly, I single handedly very bravely saved the internet from the imminent catastrophe. A single git repository with all the code, the thesis, the papers I could find. Once splattered all over the place, now on a single button to rule them all! So FARGonautica was born.

Now I’m glad to announce that FARGonautica 1.0 is live. We finally have all projects from FCCA running with GUIs, and most of the newer projects. Some were done by the open-source community, others were translated by us to modern Python (we used an obscure tool found here). Numbo and Metacat run bit-by-bit identical: if the temperature at codelet 123456 was 3.1415, then it also is 3.1415 in the translation. Copycat is Melanie’s original code running under SBLC (modern Common Lisp) with this blackboard-like OpenGL GUI. TableTop & Seek-Whence are “re-interpretations.”

In order, FCCA projects first, then Post-FCCA projects. A few screenshots:

FCCA: Marsha Meredith’s Seek-Whence

FCCA: Daniel Defays’s Numbo

FCCA: Melanie Mitchell’s Copycat

Because Copycat made such a splash, there are ~10 different implementations made by the open-source community. I got Melanie’s original code running on modern Common Lisp, and ported the OpenGL graphics developed by Joseph Aaron Hager. A python app wraps both things; here’s what it looks like.

FCCA: Bob French’s Tabletop

POST-FCCA: James Marshall’s Metacat

POST-FCCA: Abhijit Mahabal’s Seqsee

POST-FCCA: Eric Nichol’s Musicat

FCCA: Gary McGraw’s Letter Spirit, Part 1

[ED this one is, for obvious reasons, duplicated on the BIML website]

POST-FCCA: John Rehling’s Letter Spirit, Part 2

POST-FCCA NOT Ported: Capyblanca, Phaeaco and George. Capyblanca: In our recent work we created a full chess engine that plays by analogy (see chapter three video), but it’s not ready; far from it. George: The architecture has many deviations from a “normal” FARG one, I wouldn’t ever be satisfied with a re-implementation from a description only (the code is gone). Phaeaco: This one gives too much power to the enemy (chapter three). Under wraps for the time being.

Letter spirit, and others like Copycat, came from the open-source people. I take the liberty to copy my e-pals here, special mention to Dr Paul Geiger (Copycat + Letter Spirit part 1 + Letter Spirit part 2, in javascript, running on a browser with no installation needed). It’s insane. Nothing to install, nothing to do. Click the link, and, through some miracle, the spirit of Letter Spirit rises back. The apotheosis of the button!

A professor is not supposed to have favorites, but we always do, don’t we? My favorite is Tabletop.

My ten-year-old daughter goes wild with Tabletop: We bet on what Tabletop will do, then she starts screaming “NONONO Daddy you’re wrong NONONO I would touch THIS, because of […]!”.

It’s a blast!

We hope these implementations will resonate with someone; perhaps bringing up a smile somewhere.

As we pass the baton, with a little luck, the younger generation will pick this up and push it forwards. That’s their job.

This is something that really needed to be done by somebody somewhere. So it became our job.

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CHAPTER 3. Looking forward…

“Just when you thought FARG was out; they pull it back in” — Michael FARGleone, The GodFARGer, Part III

Introducing… Argo!

Around a year ago I ganged up with Harry Foundalis (world-renowned Bongardologician), Eric Nichols (world-renowned Musicometer) & two new guys: Bernardo Machado (Cognitive psychologist & Business mercenary) + Victor Tigre (Applied Physicist). Whereas Deep Learning seems to have solved crystallized intelligence (at obscene expense), we don’t think it has solved Fluid Intelligence.

Our goal is to solve, through FARG: ARC-AGI-1, then ARC-AGI-2, then ARC-AGI-N. BigAI has solved them all through “agents”, apparently, but we’re ahead in at least 4 ways: (i) we don’t need Mr Musk’s magic flying datacenters in the sky; ours solves hundreds of problems on a phone in Airplane mode, no GPU. (ii) We know what it is doing inside. (iii) It does not separate perception and reasoning, or “training phase” and “inference phase”. (iv) Finally, like people, this one goes with a few examples—not gorillions of them.

Call us crazy, but perhaps not all that exists is gargantuan statistical inference? Perhaps FARG can help nudge the trajectory of the biggest and craziest industry in the world just a teenie-weenie bit? Oh look, this thingie binds with that thingie, so there’s a bond here. Oh look, this blob in the input corresponds to that blob in the output, so there’s a correspondence there. The examples say it’s all about “the thing that touches the red square”, but nothing touches the red square on the test, so the idea slips to “the thing closest to the red square”. Again we go from concrete → abstraction → concrete (after finding the transformation rule). And on and on. It’s FARG. No more; no less; it’s FARG all the way down. Difficult, yes. Large vocabulary, but not insurmountable. It’s about walking that fine line between underfitting (combinatorial explosions & not knowing what to do) and overfitting (creating “solutions” to the very problems you’ve seen and nothing else—at the limit creating a lookup-table whilst demanding all swans to be white). That fine line you people have walked.

Here’s what Argo looks like right now:

Argo runs on a Browser, here on ConceptARC’ s Extend-to-Boundary-2, with Mr. Claude running on a panel on the right. Here Mr Claude has created ten test variations (to look for overfitting).

Argo fails at Extend to Boundary 3, Mr. Claude explains what it saw on the logs (isolated dots instead of a blocking line).

Somebody somewhere had to pick up this fight; so it became our job. Yes it’s a big fight, yes there will be blood, yes we’ll lose most of our teeth—but hey, soup is cheap.

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CHAPTER 4. The manifesto

“Gentlemen you can’t fight in here! This is the War Room!” — Dr. Strangelove

We’re not going to be normal, happy, healthy, well-balanced people, this is no margarine commercial.

We’ll be relentless about this.

We will get it done—that much I promise you.

Here are some numbers (30 Oct 14:00 “Loopy Argo” version, codebase size ~400,000 lines):

How we measured them. The first row marked as [*] means ”the 1,000-task ARC-AGI-2 training set, which contains ARC-AGI-1.” If you look at the percentages of ConceptARC, that’s our worst. That’s because we only count as “solved” if the system, given two guesses, can create the output grids of all three tests perfectly—two tests correct and one wrong means not solved. Mean run time per task is <9 seconds on a big desktop development machine (not a phone), but some problems do explode, with two being killed at 5 minutes. No program means we found nothing that could transform the grids of even the examples into their output grids. Whatever overfitting remains in the system appears here as a fake solution that sneaked into these numbers. Observe that you can mitigate overfitting, but you can never fully escape it. Humans overfit; it’s the “black swan problem”; it’s the “unknown unknowns”; it’s our tying-to-the-mast burn-the-boats never-back-down true-believer defense of our paradigms. Einstein himself overfitted to theories in which God wouldn’t touch dice. Mathematics overfits to axioms. An interesting philosophical question is whether God (a perfect system, or all possible busy-beavers, or things way beyond my imagination) can escape overfitting, but I obviously don’t have the IQ to push further.

So yes, with LLMs having beaten it, right now it’s still an embarrassment; with a long painful voyage ahead. But it seems to have promise, doesn’t it? Every week we creep up a little in solves and down a little in time. I think it was Eratosthenes that once said:

«Όλοι ξέρουν πως, όταν ο Χάρης Φουντάλης είναι ο επικεφαλής αρχιτέκτονας, τίποτα δεν μπορεί να πάει στραβά.»

— Ερατοσθένης

What a beautiful, profound, insight, isn’t it?

Here’s me drawing the sword and screaming “Touché, BigAI!”: a preview of my loving “declaration of war against Big-AI” manifesto. It stays “unpublished” until we have two papers for outside investors showing that:

1. Our all-singing all-dancing “AI paradigm” is fast and cheap, runs on a phone; and it is fully auditable; we can see what’s going on inside. And few examples suffice.
2. It bypasses the dissonance involved: “when are coffee cups like old elephants?” Recent work by Martha Lewis and Melanie showed the problem Bob French posed in modern LLM parlance, just substitute “perception module” by “training data” and you’ll see it immediately.

Please forgive me for being a little raw & rude to our cherished deep learning friends. I did my best but still couldn’t hold it back on the “Test-Time Training Technique”. I tried to be well-mannered; I really really tried.

When we get these due-diligence papers done, we’ll post the video publicly and we’ll find investors. Somebody somewhere is going to see the potential. For now I’m responsible for keeping the lights on.

So yeah. Just a heads-up on what we the little people have been playing with. Of course, it’s not ready, and our motley crew of misfits doesn’t deserve any rest. The journey is not finished, perils lie ahead.

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CHAPTER 5. The end of the world

“It was the best of times, it was the worst of times, it was the age of wisdom, it was the age of foolishness, it was the epoch of belief, it was the epoch of incredulity, it was the season of Light, it was the season of Darkness, it was the spring of hope, it was the winter of despair, we had everything before us, we had nothing before us, we were all going direct to Heaven, we were all going direct the other way – in short, the period was so far like the present period, that some of its noisiest authorities insisted on its being received, for good or for evil, in the superlative degree of comparison only.” — Dickens

All my love to each and every one of you. Each of you has changed my life in ways I can hardly begin to express. In a parallel universe where Doug hadn’t led this charge, my life would have been so much poorer; drier; sadder; shallower. I’m profoundly grateful that this is the universe I got to live in, with all of you in it. So much good stuff—this is the best light cone!

Comments, tips, issues & pull requests to the repository; mockery of the video and my terrible diction, intros to people we should talk to, criticisms, requests for demos, soup cans, literature we should read, and AI doomer viewpoints will be taken graciously.

I threw this text in Claude and it spit out:

“It reads like someone who has been quiet for ten years and decided to say everything at once. For a reunion letter to people who shaped your life, that is the right failure mode.”

The right failure mode—I love it; beautiful poetry!

Speaking of the end of the world AI doom: if we the third chimpanzee are not going to be alone anymore, well… at the risk of being crushed as a simpleton… I for one do take solace from some FARG technical properties that beautifully morph into human values…

i) I’d much rather look inside and read the logs than trust some inscrutable gigawatt-eating giant matrix blob;

ii) I’d much rather have it not separate perception from reasoning. The Gods intended them forever together—The Gods will severely punish us with serious faults and errors if we do not repent from this sin;

iii) I’d much rather not evaporate the Atlantic to make room for one big-beautiful datacenter;

iv) I’d much rather have this little program that runs anywhere than to centralize even more power in few hands;

v) I’d much rather have it learn from one example than from 30 million.

Nobody went out of their way to explicitly design these in because “by 2026 things might look ugly,” right? I may be missing something here, but I think this list comes from The Gods, telling us: “this is the right thing to do; Somebody somewhere has to do it.” Hence it became our job.

Turns out that guy just might have gotten it right; the singularity futurologist fool. We’ll turn the crank up a notch in that second derivative. Objects in the mirror may be closer than they appear.

SILICON VALLEY, MAKE WAY OR BECOME THE WAY. FARG IS COMING.

HAPPY END OF THE WORLD EVERYONE!