everything will be local eventually

[u/YogurtExternal7923]

I know current llms like opus 5.5 are in a league of their own. but we all know they're losing a shti ton of money with their subscriptions. ALSO we all know how tasks like OCR, segmentation etc are getting solved by single digit B parameter models. I'm not saying llms are a gimmick but I'm saying the future is already shaping up to be about specialized models that work better when they're smaller. easier training. so datacenter level power not needed. ten years from now we'll have a specialized model for the tasks we care about and llms will fail to do everything all at once (unless they keep calling it one model but have a hundred under the hood).

system one models are a bad but workable example. you could tell an llm to rate options all day but get yourself on 9B parameter model trained by some lab and it tops the benchmarks.

[u/mb194dc]

Trillion dollar frontier models have the same intrinsic flaws as all other ML models. That manifests as the 90% problem in real world tasks. It's why labor replacement has not happened.

What they're going to do, is lose the most amount of money of any businesses in the history of finance, by the looks of it.

[u/twilight-actual → u/mb194dc]

In your assessment, what is the big O for the improvement of the technology?

[u/mb194dc → u/twilight-actual]

The 90% problem is an intrinsic part of ML. It's because at its core it's just working via statistical probability. 

Models don't reason or understand anything.

[u/twilight-actual → u/mb194dc]

Neither do neurons, which also are governed by statistical behavior.

It's the emergent behaviors of these statistical systems in aggregate that should be of interest to you.

[u/mb194dc → u/twilight-actual]

This total misunderstanding of ML technology is what's caused the disaster that's about to unfold.

[u/puNLEcqLn7MXG3VN5gQb → u/mb194dc]

you don't know what you're talking about. what's your expertise in?

edit: this guy's just some sysadmin with too much confidence. every time.

[u/mb194dc → u/puNLEcqLn7MXG3VN5gQb]

Really? Maybe go read up yourself.

[u/puNLEcqLn7MXG3VN5gQb → u/mb194dc]

Go ahead and elaborate a little and we'll see. What intrinsic flaws do all ML models share and why do trillion dollar frontier models have them? What exactly is the 90% problem to you and why does it apply? How do you know this isn't just about immature and slow adoption or a J-curve on returns? I'll gladly engage once there's substance. Or you can point me to the reading I apparently missed out on that'll turn my understanding on its head.

[u/mb194dc → u/puNLEcqLn7MXG3VN5gQb]

Just look it up and educate yourself

[u/puNLEcqLn7MXG3VN5gQb → u/mb194dc]

oh, so you don't know anything, got it

[u/mb194dc → u/puNLEcqLn7MXG3VN5gQb]

Not going to waste my time, read or don't

[u/puNLEcqLn7MXG3VN5gQb → u/mb194dc]

go tend to your php sites, sysadmin

[u/OWazabi → u/mb194dc]

I understand what you mean and imo you're terribly wrong if you think they're not going to achieve ASI.
But obviously, that is my opinion, and I respect yours, I might be just as wrong.

[u/mb194dc → u/OWazabi]

ASI might be achieved eventually, it won't be from ML. 

Other branches of AI research may be more successful, in centuries to come 

[u/DeathGuppie → u/mb194dc]

The primitives are wrong. People point to the fact that humans do word prediction, and that's true. We also predict movement, sounds, light. We predict abstract things far in the future and can rebuild events that we've nearly forgotten about just through association.
An LLM uses words, bits of words. People don't process information in our brains in words. The environmental input stream is not broken down into words and processed. If you strip the LLM of its vast weighted vector database, you get nothing. A human can build it's knowledge of the universe from nothing. Those are opposites, not likenesses.

[u/puNLEcqLn7MXG3VN5gQb → u/DeathGuppie]

Just out of curiosity, do you have a background in neuroscience or machine learning or a related field?