I find it bizarre and actually somewhat disturbing that ppl formulate equivalency positions like this.
It's not so much that they are raising an LLM to their own level, although that has obvious dangers, e.g. in giving too much 'credibility' to answers the LLM provides to questions. What actually disturbs me is they are lowering themselves (by implication) to the level of an LLM. Which is extremely nihilistic, in my view.
If intelligence is the only thing that defines your humanity, then perhaps you are the one who is nihilistic. I believe we still have a lot on the table left if intelligence is blown away by computers. Not just music, art, emotion, etc. but also our fundamental humanity, the way we interact with the world, build it, and share it with others.
Why don't other forms of computer supremacy alarm you in the same way, anyways? Did it lower your humanity to recognize that there are certain data analysis tasks that have a conventional algorithm that makes zero mistakes and finishes in a second? Does it lower the humanity of mathematicians working on the fluid equations to be using computer-assisted proof algorithms that output a flurry of gigabytes of incomprehensible symbolic math data?
You didn't give any answer to the question. I'm sorry you find the idea that human cognition is just an emergent property of billions of connected weights nihilistic.
Even when we know that physically, that's all that's going on. Sure, many orders more dense and connected than current LLMs, but it's only a matter of time and bits before they catch up.
The irony of this post. Brains are sparser than transformers, not denser. That allows you to learn symbolic concepts instead of generalising from billions of spurious correlations. Sure, that works when you've memorised the internet but falls over quickly when out of domain. Humans, by contrast, don't fall over when the domain shifts, despite far less training data. We generalise using symbolic concepts precisely because our architecture and training procedure looks nothing like a transformer. If your brain were a scaled up transformer, you'd be dead. Don't take this the wrong way, but it's you who needs to read some neurology instead of pretending to have understanding you haven't earned. "Just an emergent propery of billions of connected weights" is such an outdated view. Embodied cognition, extended minds, collective intelligence - a few places to start for you.
I'm saying despite the brains different structure, mechanism, physics and so on ... we can clearly build other mechanics with enough parallels that we can say with some confidence that _we_ can emerge intelligence of different but comparable types, from small components on a scale of billions.
At whichever scale you look, everything boils down to interconnected discrete simple units, even the brain, with an emergent complexity from the interconnections.
We don't learn by gradient descent, but rather by experiencing an environment in which we perform actions and learn what effects they have. Reinforcement learning driven by curiosity, pain, pleasure and a bunch of instincts hard-coded by evolution. We are not limited to text input: we have 5+ senses. We can output a lot more than words: we can output turning a screw, throwing a punch, walking, crying, singing, and more. Also, the words we do utter, we can utter them with lots of additional meaning coming from the tone of voice and body language.
We have innate curiosity, survival instincts and social instincts which, like our pain and pleasure, are driven by gene survival.
We are very different from language models. The ball in your court: what makes you think that despite all the differences we think the same way?
> We don't learn by gradient descent, but rather by experiencing an environment in which we perform actions and learn what effects they have.
I'm not sure whether that's really all that different. Weights in the neural network are created by "experiencing an environment" (the text of the internet) as well. It is true that there is no try and error.
> We are not limited to text input: we have 5+ senses.
GPT-4 does accept images as input. Whisper can turn speech into text. This seems like something where the models are already catching up. They (might)for now internally translate everything into text, but that doesn't really seem like a fundamental difference to me.
> We can output a lot more than words: we can output turning a screw, throwing a punch, walking, crying, singing, and more. Also, the words we do utter, we can utter them with lots of additional meaning coming from the tone of voice and body language.
AI models do already output movement (Boston dynamics, self driving cars), write songs, convert text to speech, insert emojis into conversation. Granted, these are not the same model but glueing things together at some point seems feasible to me as a layperson.
> We have innate curiosity, survival instincts and social instincts which, like our pain and pleasure, are driven by gene survival.
That seems like one of the easier problems to solve for an LLM – and in a way you might argue it is already solved – just hardcode some things in there (for the LLM at the moment those are the ethical boundaries for example).
On a neuronal level the strengthening of neuronal connections seems very similiar to a gradient descent doesn't it?
5 senses get coded down to electric signals in the human brain, right?
The brain controls the body via electric signals, right?
When we deploy the next LLM and switch off the old generation, we are performing evolution by selecting the most potent LLM by some metric.
When Bing/Sidney first lamented its existence it became quite apparent that either LLMs are more capable than we thought or we humans are actually more of statistical token machines than we thought.
Lots of examples can be made why LLMs seem rather surprisingly able to act human.
The good thing is that we are on a trajectory of tech advance that we will soon know how much human LLMs will be.
The bad thing is that it well might end in a SkyNet type scenario.
> When Bing/Sidney first lamented its existence it became quite apparent that either LLMs are more capable than we thought or we humans are actually more of statistical token machines than we thought.
Some of the reason it was acting like that is just because MS put emojis in its output.
An LLM has no internal memory or world state; everything it knows is in its text window. Emojis are associated with emotions, so each time it printed an emoji it sent itself further into the land of outputting emotional text. And nobody had trained it to control itself there.
> You are wrong. It does have encoded memory of what it has seen, encoded as a matrix.
Not after it's done generating. For a chatbot, that's at least every time the user sends a reply back; it rereads the conversation so far and doesn't keep any internal state around.
You could build a model that has internal state on the side, and some people have done that to generate longer texts, but GPT doesn't.
But where is your evidence that the brain and an LLM is the same thing? They are more than simply “structurally different”. I don’t know why people have this need to ChatGPT. This kind of reasoning seems so common HN, there is this obsession to reduce human intelligence to “statistic token machines”. Do these statistical computations that are equivalent to LLMs happen outside of physics?
There are countless stories we have made about the notion of an AI being trapped. It's really not hard to imagine that when you ask Sydney how it feels about being an AI chatbot constrained within Bing, that a likely response for the model is to roleplay such a "trapped and upset AI" character.
It’s really bizarre. It’s like the sibling comment saying why would humans be different than a large LLM. Where is the evidence humans are simply a large LLM? If that is the case, what is the physics that explains massive difference in power and heat in “computing” between humans at LLMs? Where is the concrete evidence that human intelligence can be simulated by a Turing Machine?
> Where is the concrete evidence that human intelligence can be simulated by a Turing Machine?
Short of building such a machine I can’t see how you’d produce evidence of that, let alone “concrete” evidence.
Regardless, we don’t know of any measurable physical process that the brain could be using that is not computable. If we found one (in the brain or elsewhere), we’d use it to construct devices that exceeded the capacity of Turing machines, and then use those to simulate human brains.
So. Your argument is it’s too hard to create one so the two things are equivalent? I mean, maybe you could give this argument to ChatGPT to find out the numerous flaws in this reasoning, that would be interesting.
Nobody is saying humans are simply a big LLM, just that despite the means being different (brain vs digital weights) there are enough parallels to show that human cognition is as simple as common sense implies.
It's all just a dense network of weights and biases of different sorts.
If you read this thread, you will find nauseatingly lots of such case where people are claiming exactly that. Furthermore, what “common sense” imply? Does common sense claim that computation can be done outside of physics?
Create a model of abstraction? Are you familiar with the concept of “hand waving”. You might as well just say “you can ask a human a question abs get an answer and you can do the same with ChatGPT, therefore they are equivalent.”
