ConsciousCode

Daily reminder that Firefox is customizable to the point of removing Mozilla's telemetry and making it look and feel almost like Chromium. And no, de-Googled Chromium probably isn't enough because preliminary code for implementing WEI has been pushed upstream (basically they added the code which makes it possible for WEI to be implemented, strongly suggesting they're intending to actually implement it upstream and not in Chrome)

Why discs instead of cartridges, which are currently the superior physical option? I personally try to buy physical whenever possible, because I don't trust companies to not ban my account and flush hundreds of dollars of games down the toilet, and it generally feels better to have just that little extra bit more ownership over my own property.

I for one applaud her ingenuity in using that as an excuse to treat herself

I hope my reply didn't come off as too caustic - I thought your reply with an open request for discussion was refreshing regardless of the common misconception. You're not bad for being wrong, and I do enjoy sperging about these things. I didn't intend to demean you, just in case it came across like that (if not just ignore this - I guess I'm overthinking it 🤔).

Let me flip it around again - humans regularly "hallucinate", it's just not something we recognize as such. There's neuro-atypical hallucinations, yes, but there's also misperceptions, misunderstandings, brain farts, and "glitches" which regularly occur in healthy cognition, and we have an entire rest of the brain to prevent those. LLMs are most comparable to "broca's area", which neurological case studies suggest naturally produces a stream of nonsense (see: split brain patients explaining the actions of their mute half). It's the rest of our "cognitive architecture" which conditions that raw language model to remain self-consistent and form a coherent notion of self. Honestly this discussion on "conceptualization" is poorly conceived because it's unfalsifiable and says nothing about the practical applications. Why do I care if the LLM can conceptualize if it does whatever subset of conceptualization I need to complete a natural language task?

AI is being super overhyped right now, which is unfortunate because it really is borderline miraculous, yet somehow they've overdone it. Emergent properties are empirical observations of behaviors they're able to at least semi-consistently demonstrate - where it becomes "eye of the beholder" is when we dither on about psychology and philosophy about whether or not they're some kind of "conscious" - I would argue they aren't, and the architecture makes that impossible without external aid, but "conscious(ness)" is such a broad term that it barely has a definition at all. I guess to speedrun the overhype misinformation I see:

• "They just predict one token at a time" is reductive and misleading even though it's technically true - the loss function for language modeling inevitably requires learning abstract semantic operations. For instance, to complete "The capital of France is" a language model must in some way "know" about countries, cities, and the ontology of France.
• "It's just a chatbot" - ChatGPT is a chatbot, GPT-4 is a language model. Language models model how the likelihood of words and language changes over time. When I said "causal" before, this is an arbitrary restriction of the math such that the model only predicts the "next" word. If you remove this restriction, you can get it a sentence with a hole in it and it'll tell you what words are most likely to be in that hole. You can think of it as being like a physics model, which describes how objects change over time. Putting these into a "generative" context allows you to extract latent semantic information generalized from the training corpus, including higher-order relationships. tl;dr "chatbot" is the first and least interesting application - anything which relates to "understanding" natural language is a potential application.
• "Hallucinations show that they're broken" - Hallucinations are actually what you'd expect from these sorts of models. If I had to broadly class the sorts of hallucinations I see, they would be:
  1. Model inaccuracy - Inevitable, but not the only reason. Essentially it failed to generalize in that specific way, like SD and hands.
  2. Unlikely sampling - It's possible the code which picks the next word given the probability distribution accidentally picks one (or a series) with a very low chance. When this happens, the LLM has no way to "undo" that, which puts it in a very weird position where it has to keep predicting but it's already in a space that shouldn't really be possible. There are actually some papers which attempt to correct that, like adding an "undo token" (unfortunately can't find the paper) or detecting OOD conditions
  3. Extrapolation - Especially for the earlier models with small context windows, if it needs information which is now outside that window it's still modeling language, just without the necessary context. Without this context, it will instead pick one at random and talk about something unrelated. Compare this to eg dementia patients.
  4. Imagination - When you give it some kind of placeholder, like "<...>", "etc etc etc" or "## code here ##", most text in the training data like that will continue as if there was information in that place. Lacking context, just like with "extrapolation", it picks one at random. You can mitigate this somewhat by telling it to only respond to things that are literally in the text, and GPT-4 doesn't seem to have this problem much anymore, probably from the RLHF.
  5. Priming - If you prompt the LLM authoritatively enough, eg "find me a case that proves X" which implies such a case exists, if it doesn't know of any such case, it will create one at random. Essentially, it's saying "if there was a case that proved X it would look like this". This is actually useful when properly constrained, eg if you want it to recursively generate code it might use an undefined function that it "wishes" existed.
• "GPT-5 could be roko's basilisk!" - No. This architecture is fundamentally incapable of iterative thought processes, for it to develop those itself would require trillions more parameters, if it's even possible. What's more, LLMs aren't utility-maximizers or reinforcement learning agents like we thought AGI would be; they do whatever you ask and have no will or desires of their own. There's almost 0 chance this kind of model would go rogue, offset only slightly by people using RLHF but that's human-oriented so the worst you get is the model catering to humans being dumb.
• "They tek er jerbs!" - Yes, but not because they're "as good as humans" - they are better when given a specific task to narrowly focus on. The models are general, but they need to be told exactly what to do, which makes them excellent for capitalism's style of alienated labor. I would argue this is actually be desirable if working wasn't tied to people's privilege to continue living - no living human should have to flip burgers when a robot can do it better, otherwise you're treating the human like a robot.

I'll add more if I see or think of any. And if you have any specific questions, I'd be happy to answer. Also I should note, I'm of course using a lot of anthropomorphizing language here but it's the closest we have to describing these concepts. They're not human, and while they may have comparable behaviors in isolation, you can't accurately generalize all human behaviors and their interactions onto the models. Even if they were AGI or artificial people, they would "think" in fundamentally different ways.

If you want a more approachable but knowledgeable discussion on LLMs and their capabilities, I would recommend a youtuber named Dave Shapiro. Very interesting ideas, he gets a bit far into hype and futurism but those are more or less contained within their own videos.`

How about this, then. You've proposed that LLMs are not capable of conceptualizing, while I propose that the specifics of the internals don't matter in this case because LLMs are made of dozens of layers which can easily explain higher orders of abstraction, and they exist as black boxes beyond the mechanics of the model. For the record, I personally know the mathematics and mechanics of how they work as I've written my own implementations (and I can answer any specific questions you might have). Is there an experiment you can propose which would falsify your assertion that LLMs cannot conceptualize? I'm taking for granted that they can as the null hypothesis because they can readily produce outputs that appear for all intents and purposes to conceptualize.

Did you try this with an LLM? Because GPT-4 analyzes it exactly the same way you did and then some:

List everything wrong in the sentence "The king of Italy is completely bald because his hair is currently naturally green."

The sentence "The king of Italy is completely bald because his hair is currently naturally green" contains several issues:

1. Factual Inaccuracy: As of my last update in September 2021, Italy is a republic and does not have a king.
2. Logical Contradiction: The sentence states that the king is "completely bald" but then goes on to describe the color of his hair. If he is bald, he wouldn't have hair to describe.
3. Causality Issue: The sentence implies that the king is bald because his hair is green, which doesn't make logical sense. The color of one's hair doesn't cause baldness.
4. Ambiguity in "Naturally": The word "naturally" could be interpreted in multiple ways. It could mean that the green color is natural to him (not dyed), or it could mean that the hair turned green on its own. The ambiguity could lead to confusion.
5. Tense Mismatch: The sentence uses "is completely bald" (present tense) and "is currently naturally green" (also present tense) for the hair, which is contradictory.
6. Redundancy: The word "currently" may be considered redundant if the sentence is understood to be describing a present condition.
7. Clarity: The sentence overall is confusing and lacks clarity due to the contradictions and ambiguities mentioned.

In summary, the sentence has issues ranging from factual inaccuracies to logical contradictions and ambiguities.

Part of what is surprising about LLMs is they have emergent properties you wouldn't expect from them being autocomplete on steroids. As it turns out, reducing the loss function for natural language readily generalizes to higher-order abstraction and conceptualization. There do need to be additional layers, for instance allowing an internal monologue, the ability to self-censor or self-correct, and mitigation for low-probability sampling (all of these being inherent limitations with the architecture), but apparently conceptualization is less special than we'd like to think.

LLMs are not created to chat, they're literally what the name says - language models. They are very complex statistical models of the joint causal probability of all possible words given the previous words in the context window. There's a common misconception that they're "made for chat" by the wider public because ChatGPT was the first "killer application", but they are much more general than that. What's so profound about LLMs to AI and NLP engineers is that they're general purpose. That is, given the right framework they can be used to complete any task expressible in natural language. It's hard to convey to people just how powerful that is, and I haven't seen software engineers really figure this out yet either. As an example I keep going back to, I made a library to create "semantic functions" in Python which look like this:

@semantic
def list_people(text) -> list[str]:
    '''List the people mentioned in the given text.'''

That is the entire function, expressed in the docstring. 10 months ago, this would’ve been literally impossible. I could approximate it with thousands of lines of code using SpaCy and other NLP libraries to do NER, maybe a dictionary of known names with fuzzy matching, some heuristics to rule out city names or more advanced sentence structure parsing for false positives, but the result would be guaranteed to be worse for significantly more effort. Here, I just tell the AI to do it and it… does. Just like that. But you can’t hype up an algorithm that does boring stuff like NLP, so people focus on the danger of AI (which is real, but laymen and news focus on the wrong things), how it’s going to take everyone’s jobs (it will, but that’s a problem with our system which equates having a job to being allowed to live), how it’s super-intelligent, etc. It’s all the business logic and doing things that are hard to program but easy to describe that will really show off its power.

How does one know if they're demiromantic or just aromantic? I know I'm demisexual because I can sort of "simulate" having an emotional connection to an imagined fantasy, but romance doesn't have a space we're expected to intentionally simulate, and I've never experienced any sort of romance (though I haven't been close to that many people).

Huh neat, I'll have to squirrel that knowledge nugget away just in case that ever becomes viable. Another snag in my situation which I expect a lot of people my age experience though, the longest I've ever been employed at any company is my current job for 4 years (often not by choice - the current job just fired 1/4 of the staff for "budget reasons" on what should be a high-margin contract), and I've lived in almost as many locations as I have years. Buying a home would tie me down to one location, and my life has thus far been too volatile to trust that I could stay in one place without needing to move 5 years later. Plus, I was a kid during the 2008 financial crisis so I saw first hand my parents buy a big house past their means only to lose it within a couple years. That one is more irrational/emotional but worth mentioning.

Then there's the hopium that the proletariat will be pressed by late-stage capitalism into ☭Glorius Revolution☭(TM) and/or AI removes labor from the labor-consumer-owner equation and causes economic collapse (can't be a consumer if you don't have money from labor, can't be an owner if no one consumes, can't be labor if no one can hire you), and buying a house will mean a big expense that gets nationalized by the end of the decade. Probably not. But I also have no idea what the next 20 years looks like.

Any law with fines as a punishment is a law only the poor have to abide by, at the very least. A lot of laws which are explicitly meant to target wealthy entities like corporations or billionaires have their fines set comically low. Think car manufacturers regularly calculating that it's cheaper to pay fines than it is to recall thousands of cars with deadly manufacturing faults.

To be Fair and Balanced (R)(TM) to him, popular media really fed into his ego as the "real life Iron Man", from Tony Stark literally addressing him in a Marvel movie, Star Trek listing him among the great geniuses (lmfao), etc people with personality disorders like definitionally-all-billionaires are going to get caught up in their own ~~farts~~ hype.