It's as if language is itself the latent space for these psychophysical tasks, especially compositional instruction. Their description of it as a scaffolding also seems apt.
i've always assumed that language was required to give your brain the abstractions needed to reference things in the past compared to your current perception (aka now), like an index. if you think about your earliest memories, they almost certainly came after language. i'd be interested to know if any of the documented 'wild child' cases (infants 'raised by wolves') ever delved into what the children remembered before, after being taught language as an adolescent.
There were efforts to teach them language as adolescents, but they didn't acquire it - as far as we know, it's not possible to acquire language if you don't do it as an infant.
This is similar to other brain functions that aren't present at birth and require stimulation, such as sight. That is, if your eyes are forced closed for the first few months of your life, you will never be able to see, even if later they are uncovered, and keep working perfectly. The brain functions responsible for interpreting visual signals can only develop if they get visual signals in a (quite short) developmental window - and we know this with quite a bit of certainty from quite cruel animal studies.
Language acquisition is not proven to be the same, as the required studies would be deeply unethical, but the few experiences with feral children are highly suggestive that the same applies.
i just had the uncomfortable thought that it's possible a disease could kill off everyone older than 1, sterilizing the species to language at some scale. for the few that survive, their world would be feral.
>> The very idea of NNs is that it's not perfect, it is messy and not optimal, but is very generalizable.
Newton: Do you need more than that to describe the speed of a thrown baseball on a train? No. DO you you need more than newton to get to the moon? No. Is it going to be accurate at high speed in a large scale system (anything traveling near C)? NO, it fails spectacularly.
NN's are great at simulation, language, weather... But what people using them for weather seem to understand and the ML folks (screaming about AI and AGI) dont is that simulation is not a path to emulation. Lorenz showed that there were limits in weather, that most other disciplines have embraced these limits.
The entire innovation (discovery?) of LLMs is that a good formula for the task of sequence completion turns out to also be a good formula for a wide range of AI tasks. That emergent property is why language models are called language models.
That's an inductive bias, not an emergent property. The inductive bias of transformers is that they're good at integrating global context from different parts of a sequence without a particular bias towards recent time steps or localized regularities. And that happens to be a good fit for many (but not all) real-world sequence learning tasks.
The "emergent property" aspect is when LLMs are good at a task at scale X*3 but were incompetent at scale X.
The usefulness is why the term is so widespread in familiarity but I think the term would have existed to describe the linguistic mapping even if they hadn't proven to have direct problem-solving capabilities.
I'm not pretending to understand half the words uttered in this discussion but I'm constantly reminded of how much it helps me to articulate things (explain them to others, write them down, etc) to understand them. Maybe that thinking indeed happens almost entirely on a linguistic level and I'm not doing half as much other thinking (visualization, abstract logic, etc.) in the process as I thought. That feels weird.
Or is the real thinking sub-linguistic and “you” and those you talk to are the target audience of language? Sentences emerge from a pre-linguistic space we do not understand.
I do find it funny that this discussion thread has tried to represent language as a universal form of thought when it would be messy to encode the inner workings of a LLM (the weightings/relationships) themselves as natural language.
You could sort of represent the deterministic contents of an LLM by compiling all the algorithms and training data in some form, or maybe a visual mosaic of the weights and tokens, or what have you...but that still doesn't really explain the outcome when a model is presented with novel strings. The patterns are emergent properties that converge on familiar language--they're something deeper than the individual words that result.
