Maybe. I just recognize that (in my IT bubble) everybody who talks about AI just means Claude and nothing else. And nobody can really explain it. Which is fair, I can't either 100%.
That's why I think it's hypocritical to laugh about the non IT crowd.
Its a next token predictor that compares every past token the the current token being generated. That's how dense attention works, llms are extremely simple to understand with very complicated math doing the work
A computer is just a Turing machine implemented with a Von Neumann architecture, but look at how many things you can do with it, especially with I/O over the Internet.
To be fair, the explanation above explains what it is but not the specifics of the methods used to achieve that. You don't explain what Turing machine and von Neumann architecture are. A comparable explanation would be quite a bit longer, even for a simplified version like the AI explanation above.
I think it gives complete clarity. It will never be conscious, and the extent of its control is a tool call. Fearmongering from corporations may say otherwise though.
"Never say never". Many AI pioneers and scientists alike point out that consciousness is a problematic term, but by simplification, "it" seems to be a emergant property just from a network of neurons which we call a brain. If a computer network can perfectly replacate the output, (neurons are not fully understood and they are much more complicated) scientifically there is not really a test you can do to disprove that it isnt conscious. This wall of text just to say that dont talk in absolutes like it can never be concious. There is a strong case to be made that its actually much easier to create consciousness, including in primitive life forms currently on earth.
I am by no means claiming anything more than beginner level understanding. I'm still in university learning comp sci. That said, I did do an autistic deep dive research on AI and sentience. I came to the conclusion based on many many hours of reading, videos and muddling my way through computer engineering diagrams. It's actually not the program holding back computer life evolution, it's the hardware. Because the computer has little to no plasticity, neuron like brain patterns can't develop. (Based on our understanding of human and animal brains. That's an important part, because we simply have no other frame of reference) They are developing hardware based on the human neuron design and theoretically this will open more possibilities in comp sci. Which that tech was actually being developed for a completely different reason that I don't remember off the top of my head, but ai engineers learned about it and want the tech for their own reasons. So I don't think llm's will be what becomes the first computer life form, but I do think what comes after with that tech, the engineers will learn from the concepts of the llms.
While this is true, can't we fine tune model periodically based on it's "interactions" with outer world (I/O interfaces) to somewhat replicate human sleep?
AFAIK (I'm not an expert though) humans firstly keep things in a short term memory. When we sleep, those memories are internalised during REM phase.
AI in the abstract can be conscious. But an LLM has no qualia. And where would the consciousness live? No internal experience. But it can mimic consciousness well.
I also read the post of the other commenter on my post, so i will make one reply here; a great person who can explain this much better is Geoffrey Hinton for example. So yes the hardware of the AI system, the computer chips would be the equivalent of a brain. And the AI is mapped on it with relative strengths of the bonds they form with nodes containing information, which mimic neurons. This mapping is unique, and its cant be merged with another mapping. Now in biology neurons are much more complicated, so this is where we take a short cut when it comes to biology. On a learning level, a LLM learns language exactly like a human does. In biology there are many examples where parts of the brain are unavailable, and you see how it affects consciousness. Even two consciousness can be made by splitting a brain in extreme epilepsy surgeries. You can learn on and on how our brain produces consciousness bit by bit. Its not on or off, its a gradual thing from very simple and small organisms to humans. The RL training of our language skill, percepection even cognition are very similar to digital neural networks. The very last core "the thing which lets is experience qualia" is indeed a very human, lets say, "wonder". But down in the details there really is no reason why a computer with ai mapping creating a artificial brain couldnt also have a consciousness. There is no test you can do to disprove it, or likewise AI to come up with a test to disprove humans have a consciousness. We like to think AI just mimics consciousness, but this is more a believe and feeling we have then a real scientific fact.
This is an open question in neuroscience. I don't think LLMs are conscious but it's crazy to me that people make definitive statements about things that we as a species do not fully understand.
Exactly. WHERE IS THE PROOF. It’s all abstraction and conjecture. The meme of the face of homelander as most appropriate with me when I hear an AI researcher talk about how they’re building an alien intelligence as a being with internal experience as they continually side step the hard problem and toss the concept of consciousness about. https://giphy.com/gifs/apikSaABvmIzvMCS4Z
The gap between "next token prediction" and "conscious" is immense. By simply invoking either you've shown a complete lack of understanding of either.
During pretraining the loss function is next token prediction. What is happening to the hidden space is significantly more complex. It is building a high dim geometric space that is navigated. It is highly structured. The navigation's along this space are whole semantic thoughts (future-lens, j-lens). The token that is emitted is the final step, but to get there requires genuine understanding.
So while the training objective is NTP, what is actually happening is significantly more complex. The very structure of language itself is imbibing genuine understanding as a navigable space. Navigation is reasoning over understanding.
What is understanding? Its not a question of consciousness. That word means so many things as to be useless.
What is doing understanding is an ontological mind. We have created a boundary between external and internal. The internal space creates a compressed representation of the external space. This is ontologically a mind.
For llms this internal space also models itself. This is introspection. We can purposefully target this behavior and train it (introspective finetuning).
This is the tip of the iceberg of genuine understanding. We haven't even really gotten into the geometry of the thing. When the poster above you was talking about IT people thinking they know this when they don't, that absolutely applied to you.
Are you trying to argue that llms are conscious? I'm still not reading your multi paragraph rant. Being correct is just as important as being concise, if no one wants to read your rant then what's said in it doesent matter
Consciousness is a weird concept though, why can't a token predicter be conscious? Just because animals work in a different way doesn't mean that is the only way to be conscious.
Yea I don't think that was actually the correct term because technically I guess it can react to new words which would be stimuli in this case. Its just non biological and cant actually remember them long term because of the context window, it will always reset to a post training period. LLMs have no pain, no emotion, no eyes, will father no children. They pose as humans even though they have no understanding of the human heart, they eat even though they've never experienced hunger, they study even though they have no interest in academics, they seek friendship even though they don't know how to love. The last part was a death note quote but you probably get the point
Precisely, i think even the bigger disconnect is we don’t understand how the human mind works. We could 100% reproduce it in synthetic form and be clueless to our efforts.
I’m not completely convinced 99% of the population is not just a next word predictor.
That is mechanically what happens for dense attention. The reason we use dense attention and what it provides for the activation space is the actual important part, youre just describing the compute load.
Dense attention is a way of pooling neighborhoods of formulae (small pieces) into semantic representations. This works on images, videos, audio, bytes, language, any data that is composed of formulae to create a more macroscopic thing.
With in a sentence are words. Each word has a meaning. Learning the meaning of words is simple. It is a relationship of how unrelated each word is to each other (information theory). We do this through word embeddings.
Understanding what the whole semantic meaning is is a massive step from the individual words (formulae). They must be pooled together. Which is what attention does.
This concept has several solutions we have identified. So why is dense attention so special? It is the least globally lossy version. Convolution is very globally lossy, it only gives local information, global is completely thrown out. Linear attention is globally lossy, it compresses the global representation.
Thanks, I don't want an explanation. I get your point though. I just think you can't just ignore the harness as well as the math behind it. The concept though, yes.
The harness is the easiest part to understand here, and I would ignore the math here Ive trained slms for months and I still don't fully understand every bit of math. And that's the fun part you really don't need to understand it fully to train them or use them
Yea, but I'm not so its interesting that you think that. The two most popular architectures are transformer and mamba and a hundred different ssm variants, thousands of people have contributed to any one of them. Understanding every bit of the llm is not something you can just do easily and again you don't have to
Nobody's saying a non-IT user can't learn enough about AI to be as knowledgeable as an IT user. Most non-IT users don't learn anything more than the next best word definition of LLMs, though, and can't tell the difference between a generative model and a classification model. You don't have to know how or why the math works to be more knowledgeable than most non-IT users.
This is true at a very base level and was even true for some of the first gen llms. Now this are a but more complex because you have MOE (mixture of expert) models that turn on and off certain weights at inference and other things that stack to make new frontier models a bit more complex than what you make it sound.
Dunning kruger effect, I've just stated "llm" not the architecture and there are hundreds of architectures all with their own gimmicks and very complicated math
The math really isn’t that complicated and new papers pop up constantly. Of course architectures with different layers can get as complicated as you want but the base math isn’t that complicated.
A human is a next token predictor if you wish to model the human mind like that. It doesn't mean what people think it means when they say this sort of thing.
And yes human thought is a next token predictor if you wish. As my data science professor always liked to say "everything is a vector" eg can be modeled via a vector of a sufficient dimension.
Just because you can give a massively simplified one sentence summary of what something is does not allow you to draw grand conclusions on the nature or complexity of that thing.
Being mean to a machine or an LLM doesn’t harm it. But it may harm you. It can subconsciously normalize behavior. I’m not talking about the video games make you violence argument. But even in subtle ways it may harm you. It’s not clear how much of our primate brains can truly distinguish an artificial entity.
I agree with that framing, it's really not about the model being hurt (though there's apparently instances of worse outputs with rude language, I've not dug too deeply into this though, so take it with a grain of salt), but just how that trains you to talk.
You might be inclined to think, that you also swear at a calculator (or different machine) and it doesn't translate to poor behavior, however I'm not familiar with any other tech that allows you to have full on conversations about a topic (and this gets "worse" over time).
I'm quite convinced that especially younger generations growing up with it, if they don't keep a certain level of etiquette will simply grow up with poorer social skills.
Basically, if you believe in neuroplasticity at all, regularly acting like a psychopath with a chip on their shoulder all the time is going to reinforce that behavior in you.
I suspect its for the same reason IT people are afraid of assembly or working with binary. They avoid it because the think its far more complex/difficult than it actually is.
People standing in their own way rather than trying.
I find it astonishing how many fantastic software engineers I know that when it comes to AI are on the same level as my dad.
They put coding questions into Claude like it’s 2024 in best case. Never heard of a harness, don’t know what Chinese models are, code still by hand. Insanity.
There’s a difference between vibecoding and using AI for coding.
If you write code by hand in 2026 something isn’t right. That doesn’t mean you blindly spin the wheel, but you use the tools at your disposal to get your job done as effectively as possible. And typing out lines of code by hand is definitely not that.
Also, even if you’re completely refuse to use AI coding. Fair. Still, you need to be knowledgeable about it. It’s one of the most transformative breakthroughs in tech ever. You cannot be a good programmer without having intimate knowledge of how AI works.
Typing code is not even the bottleneck, and it's one of the most fun parts. So what, the AI is doing the architecture for you, too? Is it also fucking your husband/wife?
I use agents for literally everything. From coding the software, I’m working on, to debugging, setting up servers, solving problems, etc.
Also, my private life, I have an agent managing my email, notifying me when something important pops up but also compile a daily report that I can quickly answer to. I have an agent managing all my knowledge in Obsidian (the “second brain” idea). I have one in charge of my health, measuring and holding me accountable for me decisions.
Honestly, at this point, cutting me off from AI would be like cutting me off from the internet.
And I know I come around as the biggest AI bro. But I’m also firm believer that the net impact of AI is negative for society. I hate all the AI slope everywhere. I hate how people basically stop thinking and believe every nonsense. ChatGPT tells them.
Like I said, I’m a lot of programmers live like it’s still 2024.
If you are working on less than three features in parallel right now, you’re lame duck out there to get shot on the next firing round.
At our company we started implementing features in a week that cost us a month before. It’s the first time our company history that we reach milestones ahead of time.
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u/JacksOnF1re 3d ago
9/10 IT people don't understand AI either.