[ExI] LLMs and Computability

[efc at swisscows.email]

At the risk of "playing with words", if the llms are trained on data which 
was created (in whole or in part) by humans, based on the humans mind and 
models, then perhaps it could be argued that th llms "inherited" the 
models of the original creators of the training data?

Best regards,
Daniel


On Wed, 19 Apr 2023, Jason Resch via extropy-chat wrote:

> 
> 
> On Wed, Apr 19, 2023, 5:51 PM Darin Sunley via extropy-chat <extropy-chat at lists.extropy.org> wrote:
>       There's a running thread in LLM discourse about how LLMs don't have a world model and therefore are a non-starter on the
>       path to AGI.
> And indeed, on a surface level, this is true. LLMs are a function - they map token vectors to other token vectors. No cognition
> involved.
> 
> And yet, I wonder if this perspective is missing something important. The token vectors LLMs are working with aren't just
> structureless streams of tokens. they're human language - generated by human-level general processing.
> 
> 
> That seems like it's the secret sauce - the ginormous hint that got ignored by data/statisticis-centric ML researchers. When
> you learn how to map token streams with significant internal structure, the function your neural net is being trained to
> approximate will inevitably come to implement at least some of the processing that generated your token streams. 
> 
> It won't do it perfectly, and it'll be broken in weird ways. But not completely nonfunctional. Actually pretty darned useful, A
> direct analogy that comes to mind would be training a deep NN on mapping assembler program listings to output. What you will
> end up with is a learned model that, to paraphrase Greenspun's Tenth Rule, "contains an ad hoc, informally-specified,
> bug-ridden, slow implementation of half of" a Turing-complete computer.
> 
> The token streams GPT is trained on represent an infinitesimal fraction of a tiny corner of the space of all token streams, but
> they're token streams generated by human-level general intelligences. This seems to me to suggest that an LLM could very well
> be implementing significant pieces of General Intelligence, and that this is why they're so surprisingly capable.
> 
> Thoughts?
> 
> 
> 
> I believe the only way to explain their demonstrated capacities is to assume that they create models. Here is a good explanation by
> the chief researcher at OpenAI:
> 
> https://twitter.com/bio_bootloader/status/1640512444958396416?t=MlTHZ1r7aYYpK0OhS16bzg&s=19
> 
> As designed, a single invocation of GPT cannot be Turing complete as it has only a finite memory capacity and no innate ability for
> recursion. But a simple wrapper around it, something like the AutoGPTs, could in theory transformer GPT into a Turing complete
> system. Some have analogized single invocations of GPT as like single instructions by a computer's CPU.
> 
> (Or you might think of it like a single short finite time of processing by a human brain)
> 
> Jason 
> 
>