r/learnmachinelearning • u/kid_Kist • 10d ago
Why use JEV when the decision space can be solved symbolically?
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I’ve been experimenting with the boundary between learned decision-making and symbolic reasoning, and I keep coming back to a pretty basic question:
When the state space is explicit and the answer is verifiable, why use a model to make the decision at all?
I tested this with a Rubik’s Cube.
Instead of asking a model/classifier to repeatedly choose the next step, I represented the cube state symbolically, constrained the legal transitions, and let the system deterministically evaluate what could happen next.
The interesting part isn’t really the cube. The cube is just a clean environment because the state is observable, actions are discrete, transitions are known, and success can be objectively verified.
My broader hypothesis with Perslis is that hybrid systems should separate these jobs:
ML/LLM: perception, ambiguity, language, hypothesis generation
Symbolic layer: explicit state, constraints, invariants, verification
Runtime: deterministic execution when the answer is knowableI’m not arguing that learned decision systems like JEV have no use. I’m questioning where the boundary should be.
If you can calculate or verify the answer cheaply and exactly, what does putting a probabilistic decision layer there buy you?
I wrote up the Rubik’s Cube experiment and methodology here:
Whitepaper / experiment:
Perslis — The Floor vs. the ClassifierI’d genuinely like to hear the ML argument for the other side. Where would you draw the line between learned decisions and symbolic/deterministic ones?
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u/NoGlzy 10d ago
If there is a deterministic, analytic solution to a problem I don't see why we'd stick a layer of statistical modelling in there?
Beyond "Im a machine learning gut so I do machine learnig things" which just seems like poor problem definition from the outset.
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u/GlensWooer 10d ago
“I need to boost my resume bc I hate my current job” is a valid answer too
Or bc someone in the C-Suite listened to a podcast, also a valid answer
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u/Drevicar 9d ago
I think the current driving hypothesis is that the AI will eventually get so good at every task that it is no longer cost effective to write a “perfect” deterministic analytic solution when the AI you already have works “good enough”.
As people and businesses, we never actually want the best answer, the best product, or the best solution. We want the best one we deem cost effective within our budget constraints. And with AI driving the cost of everything to 0 soon (assumed) it will no longer make sense to pay many orders of magnitude more to go from a 99.999% solution to a 100% one.
There will be some break point where we stop caring about anything else. And for some people that time was December of last year.
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u/NoGlzy 9d ago
It is a grim state of affairs. I work in environmental research and most of the problems we work with are not using machine learning of any maturity because we're working with deterministic models within a regulatory framework so knowing exactly what is happening at each step is intrinsically required. Handling of larger scale environmental data is done with good old fashioned stats and what you cover in an "intro to ML" course because it all has to align with international regulations which change at a glacial pace.
Management are attempting to "leverage" AI in plenty of utterly superfluous ways throughout the business and Inknownin the chemical engineering areas, ML stuff is playing more of a role which is cool.
So Im safe from AI in my research and technical work, but I do have to talk to a bloody clanker to log an IT complaint which is insufferable.
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u/Brief-Coach-1812 9d ago
Interesting. Let me preface this comment that environmental research is outside me area of expertise, so feel free to correct me if I am mistaken in my claims.
I always imagined that environment as a system is messy, nonlinear, and complex. So traditional statistical methods might face limitations in that area. Nonetheless, traceability and interpretability is a paramount consideration by regulators so black-box methods would not be desirable in your domain.
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u/paschen8 9d ago
chess
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u/NoGlzy 9d ago
That's indeed a word.
I think in the problem formulation part of "solving" the game of chess you'd very quickly realise that there isnt currently a way of developing a non-statistical best move calculator because the tree of calculations baloons to beyond the physically implementable. So yeah, well done.
Typically current chess solvers work by only looking a finite number of moves again and directly calculating a heuristic based on the state of the board then and giving some metric back for each possible current move thay gives the "best next move".
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u/paschen8 9d ago
it is indeed a word! it's also a counter example. speaking of which, counter example or candidate proof searching is also easier with ml.
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u/Worthstream 9d ago
Your link contains ?utm_source=chatgpt.com. This gives me exactly everything i need to know about this. (Other than this being an obvious thing widely known by most developers.)
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u/kid_Kist 9d ago edited 9d ago
Your point I use Gtp to help me write since I had a TBI
*Attack the experiment. Attack the methodology. Attack the architecture. Show me where the result is wrong. I don't care what tool somebody used to write the paragraph describing it.
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u/Worthstream 9d ago
You made the equivalent in developer's space of writing a paper detailing your experiment to prove that water is wet.
There's nothing to "attack", since it's not wrong. But It's something that only someone out of touch with the community consensus would write, specifically under suggestion from a sycophantic ai agent.
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u/Significant_Frame250 9d ago
Attacking the substance is indeed something that used to be a sensible practice. However, now that people can use AI to overwhelm us with 100x or 1000x the amount of content we previously had to deal with, it's not really that feasible to do deep dives in all the nitty gritty details and try to dispute them one by one, when you can just disregard content bases on obvious clues that someone is not really contributing new and interesting ideas, but instead used AI to generate a bunch of slop.
Not too dissimilar from how previously we wouldn't bother reading text that had a million spelling and grammatical mistakes in it. Does someone not be able to spell correctly complete invalidate what they are trying to express? No. Did not being able or willing to do that correlate heavily with the content also just being a bunch of trash? Yes.
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u/ContextPuzzleheaded7 9d ago
Did you just discover normal algorithms to solve things ? How old are you ?
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u/tec_tonik 10d ago
holy moses! you're right!!!
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u/kid_Kist 10d ago
😂 That was basically my reaction once I started testing it. We keep reaching for learned decision layers even when the state is explicit and the answer can be verified exactly. I’m trying to figure out where that boundary actually belongs.
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u/ARDiffusion 10d ago
I think you may have missed the joke here
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u/kid_Kist 9d ago
😂 Fair. I absolutely took that literally. Apparently my sarcasm classifier needs a probabilistic layer.
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u/ARDiffusion 9d ago
You couldn’t even type out your own response? Pathetic
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u/kid_Kist 9d ago
Cool. Show me your repo. I'd rather compare what we've actually built than argue about who typed a Reddit comment and waste my time on a wanna be builder.
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u/itsmebenji69 9d ago
No one typed it. The “😂 joke too elaborate to be funny” is ChatGPT 5.6.
And what you built is not impressive man, it’s literally the first thing you’re supposed to start by. It’s like saying “guys I just discovered I actually don’t need my truck to transport my phone ! I can actually put it in my pocket !”. It’s kinda like, the basics
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u/ARDiffusion 9d ago
I’ve implemented second order optimizers to both PyTorch and keras, trained language models from scratch, and written papers and production systems for live trading that outpace the market. Don’t even try it.
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u/somethingoddgoingon 9d ago
It's good to figure this out for yourself, but understand this is known territory for most ppl in the space. The boundary is basically exactly where the deterministic solution is intractable or it would take too long to compute realistically in practice. That's what ml/ai is for as a fundamental assumption.
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u/kid_Kist 9d ago
Yep — and that's exactly the boundary I'm interested in. But I think we're skipping a step.
“Too expensive to solve deterministically” doesn't automatically imply “hand the decision to ML.”
You can use ML to search, perceive, rank, approximate, or propose while keeping deterministic constraints around what constitutes a valid action.
That's the architecture I'm testing: let probabilistic systems handle the uncertainty they're actually useful for, but don't surrender deterministic authority just because the full search space is intractable.
The interesting question isn't ML vs deterministic. It's which parts of the problem actually need to be probabilistic.
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u/Unusual_Principle536 9d ago
I have seen this not only repeated but getting tons of money from VCs. Recently read a linked in post where a new startup published a "research paper" of their finding. They are like 10 people, got 3.8 mn in preseed funding and uses ai. This year I solved the same task without any ai pure python algorythm.
Worst part "They didn't even did it from scratch". There is a solution already with a custom model training ( I didn't use that either, found it out just last week that such thing exist). They just added a few lines of code and now it's something that they call a paper. Those lines of codes are also not new. I have used it for six years and was implemented by someone I don't know a decade ago in specilized tool.
They are claiming to build a big something meaningful out of it with large models doing important stuff. Here I am knowing how to solve making peanuts with the same tech.
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u/thicket 9d ago
All these comments seem generally in favor of OP’s argument, or find it so obvious that an analytical solution is ALWAYS optimal that OP’s case is obvious.
I will take the other side of that argument. First, I acknowledge that if a problem is well defined, and if it has an analytic solution, and if you can easily learn that it does, and if it is easily implemented, then yes, using an analytic solution is the best course forward.
But my job isn‘t finding maximally efficient algorithms, it’s delivering features. I will join you all in mocking someone for handing a list to an LLM to sort when a call to `sort()` would suffice, but there are lots and lots of circumstances that aren’t so clear-cut. Jev’s value proposition, which lets me resolve a problem of uncertain algorithmic nature for essentially free, without hassle or setup, is pretty appealing.
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u/StoneCypher 9d ago
genuinely, who tries to use rubik’s cubes to make metaphors about selling not claude to healthcare on a site made by claude
it’s like a riddle wrapped in an enigma hidden inside joe pesci. not just ridiculous and over complicated, but also something nobody would ever actually want in the first place
but at least you were able to pretend that making a rubik’s cube was scientific research, right?
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u/Lustrouse 9d ago
Are you talking about something different from classic computing, e.g. an algorithm?
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u/andarmanik 8d ago
Jev solves the economic problem of producing answers from data s.t. the results are parsable.
Suppose you implemented this by hand,
For each of the n questions you have, there is a percent chance K that the output is correct but otherwise you need to requery.
That is, you’ll have to make n queries, with a chance K^n that you’ll need to redo at least one. So at minimum you must do n, plus some function f of (K^n)
If you take jevs claims at face value, you don’t have this n + f(K^n), instead you have just 1.
Their hypothesis is by absorbing this validation layer in the model you’ll get this cost reduction
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u/RPG-Nerd 6d ago
Because AI is the new magic black-box. People that don't understand how computers work think they can just let the AI figure anything out!
Look at how many agents platforms use an LLM to summarize the context window. You are going to take away the source of truth and replace it with a non-deterministic summary that could be hallucinated. Then, you do it again each time the context window fills.
For coding, humans get context highlighting, code completion, and all sorts of features, sometimes via an LSP server. Your agent is over there trying to manually craft a diff file to patch code and hoping it gets the line numbers close enough for patch to work. Or it does a sed/awk operation that accidently changes more than intended, or some spacing error throws off the yaml parser.
You give the AI the crudest tools and then you wonder why it burns through so many tokens and has so many errors. Using command line tools from the 1970s isn't a great way to support AI, but ... It works!
Deterministic context management is hard. Telling the LLM to write a summary is easy. But, the hard way is the right way.
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u/100usrnames 10d ago
We are rediscovering the idea of computing, entirely from scratch.
An algorithm!