We’re one step closer to reading an octopus’s mind
arstechnica.com
arstechnica.com
https://youtu.be/PVuSHjeh1Os?t=1168
Also this: "Flashes of Insight: Whole-Brain Imaging of Neural Activity in the Zebrafish"
Hint: they must have
Life really is short. If our lifespan were twice or ten times longer, the whole thing would be a different ballgame.
Keeping yourself alive for 40 years. Not a minor victory.
Think it through: If a long lived social octopus species regularly evolved and made civilization that collapsed its species, then the only similar species left would be one that is absurdly solitary and absurdly short lived. Think about it. Evolution is counterintuitive.
Octopuses will never be able to evolve anything similar to an human societies. To start because they are fiercely solitary and glad to jump to cannibal behavior at the first opportunity. Is a non-social species that will not cooperate with other individuals. This is not a question of how much time you put in the mix. They are a different animal that wants different things than us.
You could probably say similar things about early humans.
Cephalopods, on the other side, had never been reported cooperating with other species. We still don't know a lot about many species of course so that could change, but even the squids that are strongly gregarious do it in a lose "save yourself" way.
Think it through: If a long lived social octopus species regularly evolved and made civilization that collapsed its species, then the only similar species left would be one that is absurdly solitary and absurdly short lived. Think about it. Evolution is counterintuitive.
https://clickhole.com/the-future-is-here-elon-musk-stuck-a-g...
But, considering that the most massive LLMs do not even attempt to abstract and manipulate even basic concepts (they just model likely sequences of words using insanely large data sets), that would be a definitive "No.".
Plus, the fact that we have no idea how to map even human neural activity patterns to concepts, also "No.", it'd be even harder to figure out the octopus' conceptual map.
Converting the brain pattern vectors back to words could be difficult, but might be possible if each brain pattern vector was annotated with a description of the organism's current environment and the organism's current actions.
There's a pattern called "embedding search" - you precalculate a set of embeddings for a corpus of text. Then to do a search, you calculate embeddings for your search string. Then you can find the closest vector in that N-dimensional space, which finds you the semantically closest neighbor from the original corpus.
For an embedding search - the OpenAI Embeddings API gives you a ~1500 dimension output vector. When a LLM is working with input text as a vector, I am not sure what the tokenizer is actually feeding into the model. Hopefully someone else can chime in!
Thank you and your time for writing that out.
Now, even if all the model does is 1) turn your prompt into high-dimensional vectors, 2) run an adjacency search to find the vectors in the latent space nearest to your vector, and 3) translate those vectors back to tokens, it's more than enough for it to work with concepts. Again, we're talking 1000 to 100 000 dimensional vectors here. Any kind of semantic similarity between words you can think of (tree - green - grass, tree - tall - skyscraper, tree - data structure, tree - files, etc.) can fit in there - the relevant words (tokens) will be close together along some dimensions. So, if you pick a vector somewhere in the latent space, and look around (in hundred thousand dimensions) for its nearest neighbors, the group of points you'd be looking at is, IMHO, a concept in its raw form.
And indeed, mathematically, they are the same as vector in physics, just with a lot more dimensions. And indeed there is a concept of magnitude (norm) and direction. Some processes only use the "direction", throwing away the magnitude through a process called normalization, some don't care, and some actually make use of the magnitude.
then how does this work?
H: translate the following sentence to instead use olympic curling related metaphors ( do not explicitly mention curling, and do not use similes in forming the translation ): man:"I've been spinning my tires all day without getting anywhere" woman:"running out of gas?" man:"nah, I'm gassed up, geared up, oiled and revving. I just can't seem to get in the right lane, you know?" woman:"anything knocking?" man:"nah, I'm tuned just fine. I can't seem to get my GPS to connect, you know?" woman:"take a few laps without a destination, see if you can figure out the lay of the land" man:"you know, that might help. thanks barbara"
R: man: "I've been sweeping the ice all day without hitting the target."
. woman: "Losing momentum?"
. man: "Nah, I've got the energy, the technique, and the focus. I just can't seem to find the right path, you know?"
. woman: "Anything coming in your way?"
. man: "Nah, I'm on point with my aim. I just can't seem to get my strategy to align, you know?"
. woman: "Maybe glide a few times without aiming specifically, see if you can figure out the ice conditions."
. man: "You know, that might help. Thanks, Barbara."
( my apologies to anyone that has seen me paste this same example more than once, I find it a very good and succinct example for the topic )
In the middle of the road, it outputs very sensible, grammatical, and mostly accurate text. It looks impressive, and can be genuinely helpful.
As the same questions get into more obscure stuff, it starts making errors, then on anything near advanced cases, it hallucinates/confabulates/lies.
Even with simple math, you get this:
Q: what is 17 plus 34? A: 17 plus 34 equals 51. (correct) Q: what is 297438 plus 927465? A: 297438 plus 927465 equals 1224903. (correct) Q: what is 38460 times 2398756? A: 38460 times 2398756 equals 9218928960. (wrong, correct is 92256155760)
It is merely outputting a very grammatical word salad that has the same relationship to the actual concepts as a broken clock to the time (right twice a day; this is just better with insanely more parameters).
In the areas where it has ingested massive amounts and has billions of relevant vectors, it'll output good stuff. But in the edges, where the input is more scarce, it'll just guess, and that is were you see that there is no abstraction there.
I've observed it doing the same thing with legal and scientific topics that I have or know experts. When there is a great body of text that it has ingested, the predictions and grammar engine and idiom are amazing, sometimes astonishing, and often useful.
But the kinds of errors show that it has no shred of a concept. It if did, it would not have the kind of problems with facts. We would not get entirely hallucinated/confabulated references to entire papers, authors, APIs. etc., including math answers. We'd get something like "I should have that knowledge, but it is out of scope".
Your example is very cool, but pretty much the same as translating from English to French, it's just car idiom to curling idiom. When the LLM translates "Car" to "voiture", it is not abstracting the concept of a car, it is making a prediction. When we translate as novices in a second language, we have to hunt around for the vocabulary equivalent, and when we speak a second language, we aren't translating, but speaking from our own concepts. LLMs are not human, and they learn and produce nothing like humans, but humans have an extremely powerful tendency to anthropomorphize and infer qualities that are not there.
Also, from an entirely different angle, in every paper on LLM technology I've read or skimmed, I've seen zero reference to any technology that would abstract and wield concepts.
So, I find it extremely unlikely that these models even come close to anything resembling use of abstractions.
ChatGPT (and I assume other LLMs as well) uses an embeddings layer.
This is how it can translate concepts between domains. It is working with abstractions in a high dimensional semantic space.
they say: >>Embeddings are numerical representations of concepts converted to number sequences, which make it easy for computers to understand the relationships between those concepts.
Beyond that claim, there is nearly no information on how this works, and the examples give hints of not any kind of concept, but an additional abstraction layer of clustering words with similar meanings. It is not the abstraction of an understanding, but a more far-reaching thesaurus lookup.
(edit) It's an astonishingly good map of words, and this makes it better. The clusters may sort of map resembling concepts, but the kinds of errors it makes show that it's not actually wielding concepts to think.
That said, a journey of a thousand miles begins with a single step, and this looks like a good step.
Singularities are a wild ride.