fellendrone
Ironically, your best effort to inform people seems to be misinformed.
You're talking about a Markov model, not a language model with trained attention mechanisms. For a start, transformers can consider the entire context (which could be millions of tokens) rather than simple state to state probabilities.
No wonder you believe people are being 'taken in' and 'played by the ad companies'; your own understanding seems to be fundamentally misplaced.
You're confidently incorrect by oversimplifying all LLMs to a base model performing a completion from a trivial context of 5 words.
This is tantamount to a straw man. Not only do few people use untuned base models, it completely ignores in-context learning that allows the model to build complex semantic structures from the relationships learnt from its training data.
Unlike base models, instruct and chat fine-tuning teaches models to 'reason' (or rather, perform semantic calculations in abstract latent spaces) with their "conditional probability structure", as you call it, to varying extents. The model must learn to use its 'facts', understand semantics, and perform abstractions in order to follow arbitrary instructions.
You're also confabulating the training metric of "predicting tokens" with the mechanisms required to satisfy this metric for complex instructions. It's like saying "animals are just performing survival of the fittest". While technically correct, complex behaviours evolve to satisfy this 'survival' metric.
You could argue they're "just stitching together phrases", but then you would be varying degrees of wrong:
For one, this assumes phrases are compressed into semantically addressable units, which is already a form of abstraction ripe for allowing reasoning beyond 'stochastic parroting'.
For two, it's well known that the first layers perform basic structural analysis such as grammar, and later layers perform increasing levels of abstract processing.
For three, it shows a lack of understanding in how transformers perform semantic computation in-context from the relationships learnt by the feed-forward layers. If you're genuinely interested in understanding the computation model of transformers and how attention can perform semantic computation, take a look here: https://srush.github.io/raspy/
For a practical example of 'understanding' (to use the term loosely), give an instruct/chat tuned model the text of an article and ask it something like "What questions should this article answer, but doesn't?" This requires not just extracting phrases from a source, but understanding the context of the article on several levels, then reasoning about what the context is not asserting. Even comparatively simple 4x7B MoE models are able to do this effectively.
This is basically ADHD, and whilst it has its advantages, wouldn't recommend it in general.
The extreme of this might be psychedelics. A useful experience, but not necessarily something you want day to day.
Thoughts are a tool. They're like mind software.
The point of meditation isn't to stop thinking, it's to decouple yourself from them and see them from an outside perspective. In the process, perceiving that "you" are not your thoughts or your mind, and therefore direct the mind more effectively.
It's a bit like closing background processes and gaining back resources. Those thoughts take resources to 'run', and many of them are constantly spawning new ones.
When you turn your awareness on these processes, rather than them running ad hoc in the background, you can understand your self more and direct yourself better. The competing processes reintegrate.
Or perhaps it's like an unruly dog that's constantly pulling you this way or that, but you're so used to it you don't even know it's unruly. Then you start to train it, and sooner or later you become a unit, working in coordination.
Neurologically, it's likely modulating the default network, which some theorise is the neural correlate of the ego/self: https://en.wikipedia.org/wiki/Default_mode_network
> Meditation – Structural changes in areas of the DMN such as the temporoparietal junction, posterior cingulate cortex, and precuneus have been found in meditation practitioners. There is reduced activation and reduced functional connectivity of the DMN in long-term practitioners. Various forms of nondirective meditation, including Transcendental Meditation and Acem Meditation, have been found to activate the DMN.