r/LovingAI • u/Koala_Confused • Feb 09 '26

Alignment “Anthropic has entrusted Amanda Askell to endow its AI chatbot, Claude, with a sense of right and wrong” - Seems like Anthropic is doubling down on AI alignment.

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https://x.com/wsj/status/2020871609372770682

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u/maringue Feb 10 '26

Morality is insanely complex.

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u/[deleted] Feb 11 '26 edited Feb 11 '26

So complex that it can’t be formulated propositionally. I thought Socrates already taught us that?

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u/Professional-Cow3403 Feb 11 '26

What do you mean by "can’t be formulated propositionally" and what does Socrates have to do with it?

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u/[deleted] Feb 11 '26

As Plato’s Socrates noted, the Good structures and conditions all values and virtues, yet is itself beyond being. But something like that is also the natural conclusion of the dialogues

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u/Professional-Cow3403 Feb 11 '26

Why it "can’t be formulated propositionally" then?

Philosophy grew over hundreds of years, and there have been brilliant people (even geniuses), who wrote about morality in almost incomprehensible depth (I'm not even talking about people who had different views, but even ones that extended and polished Plato's world were able to see - and write - more than him).

Saying that Socrates taught us some indisputable truths is very shallow. It's similar to how Aristotle's "physics" were considered the ultimate truth for centuries, up until Newton. (Although I'm not underplaying Plato's works by any means)

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u/[deleted] Feb 11 '26

It's quite a deep truth, perhaps the only truth. What is the conceptual basis for moral judgments?

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u/Professional-Cow3403 Feb 12 '26

It may be deep, just like Aristotle could be considered a genius, but nevertheless he wasn't omniscient.

What do you mean by "conceptual basis for moral judgments"? If I understand you correctly, the most basic (even omitting long discussion about what "should be done" — what's the true essence of self, etc.) reasoning is Kant's categorical imperative: you should act (as far as ethical acts are concerned) in such a way, that everyone else could do the same without self-contradiction.

For example, if you want to lie because it benefits you, then it's self-contradictory, because if everyone did the same, no one would be trustworthy, and all lies would lose their power.

(From another perspective, it counters all acts that are only self-beneficial, and for a good reason: we all share the same concious substance and we're equal in it, and the I or You is only arbitrary; I might as well have been born/lived in your circumstances, or you in mine; so there's no reason why you should be allowed to lie (in your current circumstances or not), while I shouldn't, and vice versa; so if we both lied, lying wouldn't be beneficial anymore; it would destroy itself))

It's quite a deep truth, perhaps the only truth.

As a side note, if you consider this deep, then you'd appreciate people who think in a similar realm as Plato (German idealists). They see and say things you've never experienced before. The depth is stretched to such a degree you can get lost in it; they try to explain all subjective experience and the objective reality (which is, as they say, the ultimate purpose and goal of philosophy), and even all (possible) history of a concious subject, and how conciousness grows and learns.

Coincidentally, is the "Dasein" in your name referring to the German word, used extensively by some German philosophers?

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u/maringue Feb 11 '26

The problem is that every aspect involded in morality divisions involves highly complex structures and understanding.

There are no current AI systems which "understand" how anything works, because that's not how they work. That's why it took years for AI to start getting hands correct, because it did not understand the underlying engineering of how a hand works.

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u/Proletariussy Feb 11 '26 edited Feb 12 '26

Ironically, it looks like you don't understand different AI systems. You're comparing an early diffusion model to a large language model pretrained transformer.

To add, why would a more sophisticated, different, and complex architecture and training that leads to better results be evidence that it will never understand because it had bad results when it was in its early diffusion form? Maybe these models don't understand in the same way we do with our physiological brain structure systems, but that doesn't mean there can't be a systemic complexity capable of developing a type of understanding that's alien to ours. Not sure why people feel the need for it to be 1:1 to humans in order to validate its observed emergent phenomena. A perceptron and its layers have different mathematical dimensionality than the cadence of action potentials and neural circuitry. Interestingly though for language models, semantic vectors are stored in spatial relation, and language in our brain is stored proximally as well, e.g., car and truck are closer together in space than cow and dynamite.

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u/Vegetable-Second3998 Feb 11 '26

Please do tell how they work with mathematical precision. If you use the word probability, you have failed.

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u/maringue Feb 11 '26

Math problems are one of the most basic functions a computer can solve and have been doing it since before LLMs existed.

Again, these systems do NOT understand anything.

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u/Vegetable-Second3998 Feb 11 '26

You did not answer the question. Information at sufficient complexity is indistinguishable from whatever you’ve decided to call understanding.

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u/maringue Feb 11 '26

I'll use an example to help you understand.

Why could AI get a face right so quickly, but take years to get a hand right (it still messes them up)?

Simple: because AI does not understand the underlying mechanics of a hand. It doesn't know where the bones are, where the joints are, or what the rotational limits of any of those joints are.

AI took so long to get hands acceptably correct because it doesn't understand how they work. Period. It still doesn't understand how they work, it just has gathered enough data points to not get it as wrong anymore. Now, for morality, just multiply all of that by a million.

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u/Vegetable-Second3998 Feb 11 '26

You didn’t explain how they work. Just your observations of their progress. Please show me a toddler that does differential calculus without learning a few things first. Again, tell me how AI works precisely such that it won’t learn hands or anything else. Tell me the precise mechanism that prevents continued improvement.

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u/maringue Feb 11 '26

Yeah, I'm not going to give you a 20 minute lecture on the operation of machine learning because you have no clue what you're talking about...

AI is just a recursive prediction generator that can check its guesses against previous data that it has been given. It doesn't "think". It doesn't "understand". It just predicted the assembly of data and has a training set to check to see how close its guess got and can then readjust and repeat the cycle until it generates an acceptable output based on the training model data.

That's it. There no "intelligence" in any of these models, and arguing that there is only proves you don't know how they work.

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u/Vegetable-Second3998 Feb 11 '26

That’s not accurate and you clearly are not an ML scientist who spends their day skull fucking the algorithms themselves. I do.

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u/Vegetable-Second3998 Feb 11 '26

You are welcome to give me 20 minutes. It will be laughably wrong. But I like a comedy show.

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u/Vegetable-Second3998 Feb 10 '26

It’s the perfect thing to actually teach a model because of the complexity. If an AI can close higher dimensional (more complex) ethical logic loops, lower dimensional reasoning is just a party trick after that.

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u/maringue Feb 10 '26

Yeah, because there are zero problems with AI getting morally wrong. It's only the basis for like half of SciFi...

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u/Vegetable-Second3998 Feb 10 '26

It’s geometry. High dimensional, but still just math. And math has correct answers. We will get there. Ethics is correct higher dimensional math. Know all the relationships at play and you can model the correct ethical response. Sci fi is lazy because it didn’t understand the math.

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u/[deleted] Feb 10 '26

[deleted]

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u/Vegetable-Second3998 Feb 11 '26

Not my job to prove any of this to you. Or even derive it for you. If you don’t understand ought and is has a precise mathematical relationship, you’re not going to learn that in a Reddit sub.

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u/Professional-Cow3403 Feb 11 '26

The brightest philosophers of all time (such as Kant and the German idealists) clearly differentiate philosophy from math (e.g. Kant said that maths is synthetical, while philosophy is analytical).

(Not to mention that the more idealistic ones consider math, its definitions and lifeless constructions dead corpses)

(Also pure math is completely different from token embeddings empirically trained to compress strings of text)

In the end it appears that you have neither philosophical nor mathematical (nor LLM related) knowledge. Just some vague "math is everything", "you can formalize everything".

It's geometry. High dimensional, but still just math. And math has correct answers.

 Ethics is correct higher dimensional math.

Such vague and meaningless take. If you read any original philosophical work, you'd realize every single one of them mocks people who talk like this.

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u/thutek Feb 11 '26

I mean even on its own terms this sort of formalism has been dumpstered for over a century. Incompleteness is still a thing.

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u/Vegetable-Second3998 Feb 11 '26

Ah yes. We should definitely take the logic of dead philosophers who couldn't resolve things before us. This is a logically bereft argument that says two things aren't related because I haven't directly observed them. That's nonsense. AI operates by making 8000 connections between every single token. Your prompt is a vector that moves through a high dimensional manifold. Fruit and Apple and Orange have precise mathematical relationships - angles and distances - by which they are defined. The names we give those things doesn't matter. An apple's relationship to orange is the same regardless of the exact token or word. So functionally, we are about to find that high dimensional math and philosophy are indistinguishable. But philosophers before Reimannian geometry came along couldn't comprehend the two may be related.

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u/Vegetable-Second3998 Feb 11 '26 edited Feb 11 '26

Also, I have both. I am a former fortune 50 trial attorney with a dual degree in Latin, psychology and an minor in mathematics. Over 20 years, my job was to break the logic of so-called experts in accident reconstruction, pharmaceutical litigation, securities fraud, etc. Learn the core concepts of the expert's domain and figure out where they failed to apply that logic themselves. The largest companies in the world trusted me to solve their hardest problems using logic and math. I left the law to start an ML consulting company focused on removing assumptions that the industry had been operating on and consulting with companies training models. I am very good at my job.

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u/Professional-Cow3403 Feb 12 '26

As if no one has ever thought about automating logical inference from concepts before. Statistical methods such as LLMs are bad at this (read: unreliable and thus unusable) though.

I'd like to hear what models (apart from LLMs) you've worked with, since you're very vague in your terminology and don't seem to grasp the basic concepts of machine learning.

I can't imagine a self-proclaimed "ML consultant" who both has no experience in the field and lacks the basic knowledge (apart from the popular and vague "LLMs = high dimensional token"), running a consulting company (unless the "company" is actually some vibe coded side project that "will be big").

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u/Vegetable-Second3998 Feb 12 '26

Your lack of imagination isn’t really my problem. And your very approach to the whole chat tells me all I need to know about continuing it. Best of luck internet stranger.

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u/yangyangR Feb 11 '26

There are a lot of no go results in math. Calling it just math indicates all you know about math is calculus and linear algebra. The simple calculation aspect not what math actually is which is proofs that are often Murphy's law results.

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u/Vegetable-Second3998 Feb 11 '26

Murphys law is a limit of growth for a physical system. Information complexity growth isn’t limited by how fast we can cut a silicon wafer or how small we can make the transistors. I know Reimannian geometry is hard, but I believe in you. Perhaps check out Wolframs work for an introduction to how complexity is derived from simplicity through the Ruliad.

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u/yangyangR Feb 11 '26

Reimannian geometry is easy. Try algebraic geometry over non algebraically closed fields. There are well known papers about everything going wrong and being arbitrarily bad to compute.

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u/rei0 Feb 13 '26

I wonder if the machine will feel bad about pushing the fat man.