I doubt there is one root cause of Alzheimers (except maybe in some genetic cases), and this is likely not a panacea, but sounds like it may assist some of the key processes involved in breakdown.

Root behaviors related to sleep quality and quantity, diet, exercise, infection, environmental exposure and stress, as well as genetics, likely all contribute.

But, waste clearing and neuro-inflammation seem to be core processes involved in the progression of the pathology, and improving natural vascular waste clearance seems like a logical place to find at least a small improvement in progression and symptoms...

Analogy: if someone puts metal shavings in an engine, having a better oil filter won't prevent all damage caused by the person putting the metal shavings there (nor will it halt the process), but it will reduce the damage by getting those shavings out of circulation before they have a chance to make even more repeated passes through the engine and do even more more damage. Improved vascular waste clearance is likely only a small piece of the puzzle, like having good oil pressure and filtration, but that doesn't mean it's irrelevant just because it doesn't prevent the other upstream root causes!

"If this is flight, it's really boring. We can't even build a mechanical sparrow that can lay eggs and catch flies. You're telling me we can't run sparrow.exe but we've created flight?"

We didn't build something that flies by flapping its wings until 2010. We'd been building functional airplanes for more than 100 years before we were able to build something that worked in a (more primitive, but) similar way to how a sparrow flies.

I'm sorry that modern machine intelligence is so boring to you.

It isn't boring to me, I'm fascinated both by the ways I'm still far more capable than trillion parameter LLMs, and also by the ways they are already far more capable than I am.

FWIW, while I am not bored by nascent machine intelligence, I am bored by predictable human reactions to it: greed, exploitation, hubris, etc.

When you got a reply illustrating how incorrect that claim from your first argument was, you shifted to focusing on the other argument (the one I actually happen to agree with - the cost to society of hitching increasing dependency on big tech will make the social media harms look like childs play).

I think your argument will be better received if you focus on the very valid concerns of societal harms, and acknowledge the ways LLMs are tremendously capable, without downplaying that.

I'm with the person you replied to in seeing how capable LLMs can be when you spar with them appropriately. They confabulate, but that's your job to catch as a sparring partner. But they do bring useful knowledge of thousands of PhDs into conversations - and even if you're among the most erudite humans on the planet, this is still an asset in intellectual search for truth on many topics.

Back to the genuine problems, and they are many: this power, concentrated in the hands of big tech, is a multiplier on the power already concentrated there, with many new capabilities - especially scary being the capabilities for subtly influencing and manipulating both individual and group behavior - for profit or otherwise - by the companies or their customers, or governments, or... The possibility space of harm and abuse is large..

On net, I think we should all be pushing to educate everyone around us on the pros, the nuances, the risks, and the big cons, and working to try to build a future of offline models rather than subscription service dependency..

Now, take that for loop, and replace the implementation of decode(context, ++position) and pass it to a human who was bored enough to play along and use a notebook to organize their thoughts and translate them to/from this encoding (you might write a helper function to do this for the human in the front-end of the new decode() impl, but the data flow in and out of decode() will remain the same):

decode(context, position)

{

  cached string_answer = ask_human_question_via_context(context);

  return decode_human_answer_to_tokens(cached string_answer, position);

Is the output you get not thinking anymore because it passed through this harness? Did the human's mind somehow get reduced to mere interpolation?

The human mind is still a human mind. Putting a simple harness in front of a mind does not affect its fundamental properties.

In an LLM, decode() is calling into a trillion parameter connectome.

And sure, it does, but the person you're replying to was trying to understand why it also seems to reason about the query to give an answer consistent with it, despite not being trained on that query or answer. Your answer seems to imply that its just another slick complex encoding.

But the emergent property of trillions of digital neurons predicting the next token is that in the process of being trained to do so, they can also learn to reason.

At some scale, it is efficient to encode cognition which is capable of mimicing the cognition which generated the input tokens.

All of life arises (maybe) from very simple subatomic particles, and at each stage you can repeat this refrain, complexity increasing as you stack.

You're caught up on the mechanics of token processing (floating point matrix ALU math) and ignoring the context that p(next token) as a function being "computed" is doing so over a trillion parameters. You can poorly train a model, sure, but assuming you don't indoctrinate it too much, properties like cognition emerge - it learns to reason; why? Reasoning is more efficient and compact than memorizing answers.

If everyone defects, the system breaks down. Morality is good and it is actually logical - it solves the prisoners dilemma and pushes cooperation instead of defection. It also reduces harm and has lots of other good properties. I feel that how we affect others matters, but even if you're just a sociopath doing the math, defecting is a strategy that burns things down at scale, not a smart one. Tit for tat with forgiveness is not only morally aligned, but also more prosperous in scenarios that aren't just one-off interactions with strangers.

For me personally, it's a tradeoff of generating the first pass code 10x more quickly, but then deeply knowing and validating the code is then 10-20x more work than it would have been if I'd written it myself (and if time is of the essence, then there's the option of shallow validation/understanding in exchange for speed - which is a compromise in rigor and path towards tech debt). In the end, none of this seems like a net win (unless you don't care about quality), and it is much less enjoyable.

TL;DR; While LLMs are faster to spit out first pass code, by the time I've validated and fixed the LLM's first-pass work, I could've had my "by-hand" implementation done correctly, and had much deeper understanding out of the box. Net loss.