This is interesting. The blog post links several papers, and I recommend reading them.
Responses here however seem not commensurate with the evidence presented. Two of the papers[0][1] that provide the sources for the illustration in the blog post are about research conducted on a very small group of subjects. They measure neural activity when listening to a 30 minutes podcast (5000 words). Participants tried to guess next words. All the talk about "brain embedding" is derived from interpreting neuronal activity and sensor data geometrically. It is all very contrived.
Very interesting stuff from a neuroscience, linguistics and machine learning perspective. But I will quote from the conclusion of one of the papers[1]: "Unlike humans, DLMs (deep language models) cannot think, understand or generate new meaningful ideas by integrating prior knowledge. They simply echo the statistics of their input"
[0] Alignment of brain embeddings and artificial contextual embeddings in natural language points to common geometric patterns (https://www.nature.com/articles/s41467-024-46631-y)
[1] Shared computational principles for language processing in humans and deep language models (https://www.nature.com/articles/s41593-022-01026-4)
I view this as compelling evidence that current models are more than "stochastic parrots," because as the OP shows, they are learning to model the world in ways that are similar (up to a linear transformation) to those exhibited by the human brain. The OP's findings, in short:
* A linear transformation of a speech encoder's embeddings closely aligns them with patterns of neural activity in the brain's speech areas in response to the same speech sample.
* A linear transformation of a language decoder's embeddings closely aligns them with patterns of neural activity in the brain's language areas in response to the same language sample.
I need you guys help.
Is there some theorem stating something like random few-hot vectors can always be combined linearly to match any signal with a low p-value?
I thought I encountered it sometimes in my experiments and that this might be happening in this llm x neuroscience trend of matching llm internals to brain signals.
Did they try to predict a person's thoughts ? That would be more compelling to me than 500ms delay between the model prediction and the spoken word.
It is somewhat ironic that they had to use an OpenAI model for this research. At the same time, this gives nice continuity from earlier works that demonstrated similar, smaller scale, results using GPT-2.
Could this lead us to being able to upload our brains onto computers? To kill death. Very cool.
ok, that pretty cool research from Google, hope this leads to even more discoveries around the brain, hopefully it's time we get a better understanding of our brains and how to hack them.
So its neuronal activity from intercranial electrodes, during an active conversation. And, they found there are causal chains type patterns in the neuronal activity to produce the speech (and presumed thought) in the conversation which compare "favourably" with the LLM.
Ok. I buy it. The sequencing necessary to translate thought to words, necessarily imposes a serialisation which in consequence marshalls activity into a sequence, which in turn matches the observed statistically derived LLM sequences.
I tend to say the same things. I often say "this AGI is bullshit" and the ocurrence of Bullshit after the acronym AGI is high. I would be totally unsurprised if the linear sequence of neuronal signalling to both think, and emote as speech or even "charades" physical movements to say "AGI is bullshit" would not in some way mimic that of an LLM, or vice versa.
My mildly grumpy opinion: this is not the first paper to show correlation between brain activity and the layers of a transformer. I know that Wang et. al (2024) have done it last year[1], but I doubt they're the only ones - I just have them in my head because I was reading their paper last week. Bonus fact: Wang et. al's paper also shows that test scores are a relevant factor in said correlation.
The point that always comes to mind is: correlation does not imply causation. I guess the main contribution would be a better mapping of the areas of the brain associated with speech production, but jumping from "these two things correlate" to "these two things are essentially the same" seems to me a bit of a stretch.
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I find the OP very difficult to comprehend, to the point that I question whether it has content at all. One difficulty is in understanding their use of the word "embedding", defined (so to speak) as "internal representations (embeddings)", and their free use of the word to relate, and even equate, LLM internal structure to brain internal structure. They are simply assuming that there is a brain "embedding" that can be directly compared to the matrix of numerical weights that comprise an LLM's training. That seems a highly dubious assumption, to the point of being hand-waving.
They mention a profound difference in the opening paragraph, "Large language models do not depend on symbolic parts of speech or syntactic rules". Human language models very obviously and evidently do. On that basis alone, it can't be valid to just assume that a human "embedding" is equivalent to an LLM "embedding", for input or output.