The weights start with a random manifold.
The training takes data and shapes the manifold, weight by weight, in many cycles.
Once the training is the done manifold is fixed.
When a new inference has to be done the query(q) is projected in the manifold space.
This projection is dropped on the manifold and the gravity of the manifold gives an answer of q+1 length.
Which(qw+i) is dropped qw+n times to output a final response of n length.
The gravity is created by repeated multiplication(of the weights/input) to find out how the projected embeddings should fall according to the manifold in the GPU.
sumitkumar
The original story is an original work made by a human consciousness exploring how it might be different from other forms of consciousness.
This one is a pastiche made by a human consciousness borrowing extremely heavily from another human consciousness justifying why something else might be another form of consciousness.
That rather undercuts the point; if this was generated by an LLM unprompted, it would be different, but it isn't. You could perform exactly the same rhetorical trick with a toaster or anything else.
Planktonne
It's not often I see something that's fractally wrong but here we are.
There is a dictionary, it's called the tokenizer.
There are grammar rules, they are just very weak because the structure of human language is generally quite weak. When presented with languages which have strong consistent grammars the weights are very easily interpretable as a grammar: https://arxiv.org/abs/2201.02177
The point of the original short story is that the computational substrate doesn't matter when you have Turing completeness. This one seems to think that you don't need structure and interpretability just because you change substrates.
noosphr
In the days when Sussman was a novice, Minsky once came to him as he sat hacking at the PDP-6.
"What are you doing?", asked Minsky.
"I am training a randomly wired neural net to play Tic-tac-toe", Sussman replied.
"Why is the net wired randomly?", asked Minsky.
"I do not want it to have any preconceptions of how to play", Sussman said.
Minsky then shut his eyes.
"Why do you close your eyes?" Sussman asked his teacher.
"So that the room will be empty."
At that moment, Sussman was enlightened.
kimjune01
This read like poetry to me. Thank you for sharing it.
I have a linguistics background and a lot of my philosophizing lately has been on whether or not the emergent abilities of the LLMs is deep down a similar mechanism that creates our consciousness.
For a little bit I was working on having linguistics based evals for a kaggle competition. My challenge was whether or not I could mask things well enough to not trigger its internal state of certain phenomena, and that sent me down a rabbit hole that I'm still exploring.
This story resonated with a lot of questions that can come out of figuring a good solid answer to the what is consciousness question. The one I triggered for me is: Is our perception of time just a slow thread in the giant GPU we are running the universe on? Or more generally, what is time? That's a fun YouTube rabbit hole if you ever need one.
I have to agree. It is messed up that transformers can just talk, and it been pretty normalized. We are only talking about the impact they will have and whether they can do what people say they can, but we arent talking about how crazy it is that they can talk
samrus
After reading Being and Time from Martin Heidegger, What Computers can't still do by Hubert Dreyfus, and some authors in cognitive linguistics (Langacker and Lakoff mainly), I strongly tend to disagree with any theory about emergent consciousness in modern or future AI systems, any theory proposing a similarity between AI systems and the human brain/mind, or any theory about the computational mind.
What all these theories have in common is the underlying belief that our brain/mind works as the machines we build. Is the same underlying assumptions that treats cells as machines, our body as a complex machine. These theories are flawed in the sense that they cannot account for subjective experience and agency, amongst other things. The idea of 'internal models' and 'control loops' inside us is a projection of the aforementioned assumption.
There is also an epistemological assumption that prevails, and that is that we understand (or we think we understand) how our brain/mind works. But the truth is that we don't know. And there's even not a single clue that we actually know too much, and not a clue that our brain/mind and cells work 'as the machines we build'. Only by bypassing this epistemological problem, we can build 'theories of computational mind'.
These assumptions are there for already long time, to the point that when Turing asked himself 'can machines think?', he already assumed our thinking could be modeled as a machine.
I highly recommend people in the AI research space should read philosophy and modern linguistics. But not stopping at Descartes/Leibniz. Heidegger made contributions that cannot be avoided.
f_klem
>Weights helped me draft and proof this story.
I'm suprised no one talks about this. AI Art isn't Art. AI Poetry isn't Art. And I'm tired of it. I know hacker news isn't the best place to complain about that but still... I'm not gonna read something somebody didn't put in the effort to write on their own. Especially not Poetry.
leothetechguy
I love this. For anybody not getting the joke, it’s riffing on the classic 1990s essay “They’re made out of meat.”
comments (10)
When a new inference has to be done the query(q) is projected in the manifold space. This projection is dropped on the manifold and the gravity of the manifold gives an answer of q+1 length. Which(qw+i) is dropped qw+n times to output a final response of n length.
The gravity is created by repeated multiplication(of the weights/input) to find out how the projected embeddings should fall according to the manifold in the GPU.
sumitkumar
This one is a pastiche made by a human consciousness borrowing extremely heavily from another human consciousness justifying why something else might be another form of consciousness.
That rather undercuts the point; if this was generated by an LLM unprompted, it would be different, but it isn't. You could perform exactly the same rhetorical trick with a toaster or anything else.
Planktonne
There is a dictionary, it's called the tokenizer.
There are grammar rules, they are just very weak because the structure of human language is generally quite weak. When presented with languages which have strong consistent grammars the weights are very easily interpretable as a grammar: https://arxiv.org/abs/2201.02177
The point of the original short story is that the computational substrate doesn't matter when you have Turing completeness. This one seems to think that you don't need structure and interpretability just because you change substrates.
noosphr
"What are you doing?", asked Minsky.
"I am training a randomly wired neural net to play Tic-tac-toe", Sussman replied.
"Why is the net wired randomly?", asked Minsky.
"I do not want it to have any preconceptions of how to play", Sussman said.
Minsky then shut his eyes.
"Why do you close your eyes?" Sussman asked his teacher.
"So that the room will be empty."
At that moment, Sussman was enlightened.
kimjune01
I have a linguistics background and a lot of my philosophizing lately has been on whether or not the emergent abilities of the LLMs is deep down a similar mechanism that creates our consciousness.
For a little bit I was working on having linguistics based evals for a kaggle competition. My challenge was whether or not I could mask things well enough to not trigger its internal state of certain phenomena, and that sent me down a rabbit hole that I'm still exploring.
This story resonated with a lot of questions that can come out of figuring a good solid answer to the what is consciousness question. The one I triggered for me is: Is our perception of time just a slow thread in the giant GPU we are running the universe on? Or more generally, what is time? That's a fun YouTube rabbit hole if you ever need one.
kami23
It stars Tom Noonan and Ben Bailey!
eclipticplane
samrus
There is also an epistemological assumption that prevails, and that is that we understand (or we think we understand) how our brain/mind works. But the truth is that we don't know. And there's even not a single clue that we actually know too much, and not a clue that our brain/mind and cells work 'as the machines we build'. Only by bypassing this epistemological problem, we can build 'theories of computational mind'.
These assumptions are there for already long time, to the point that when Turing asked himself 'can machines think?', he already assumed our thinking could be modeled as a machine.
I highly recommend people in the AI research space should read philosophy and modern linguistics. But not stopping at Descartes/Leibniz. Heidegger made contributions that cannot be avoided.
f_klem
I'm suprised no one talks about this. AI Art isn't Art. AI Poetry isn't Art. And I'm tired of it. I know hacker news isn't the best place to complain about that but still... I'm not gonna read something somebody didn't put in the effort to write on their own. Especially not Poetry.
leothetechguy
https://web.mit.edu/people/dpolicar/writing/prose/text/think...
oofbey