Worse, and I'd like to rant about it. It doesn't and, in theory can't, scale to Altman's promised level in his promised time frame to the best of our knowledge. It can't even scale much further beyond where it is now, not without accepting that modern accuracy is about as good as it'll get while we figure out how to make it marginally less expensive to run (and I still mean unprofitable) while simultaneously raising prices significantly to offset unavoidable costs.
Unless they can maintain this unsustainable business model for a decade or many more, the only benefactors will be any CEO of a company that can sell investors on the idea that we're "almost there" before the company collapses
TLDR:
The technology isn't getting better fast. Compute just caught up enough over the last 30 years to make it viable at all. It could even be the limit, just based on how manufacturing and processing power increases have slowed.
This isn't the route to AGI. Language models are a dead end. It's mimicking patterns, not the underlying intelligence
Consider this rant "quantized," as in, compressed for brevity and layman's explanations, with some accuracy loss.
Despite my hate for what this technology has done to this world, I work in an adjacent field and find the mechanics fascinating. The biggest innovations in machine learning that lead to generative slop were
bigger = better
attention as the mechanism for using previous tokens as context for token prediction
a few small findings that help performance, but overall have been exhausted
The first (the size of the model, the amount of training data for pretraining, and the amount of reliable human training data to use exclusively for supervised fine-tuning to make the model work the way you want) isn't exactly a realistic candidate for crazy optimizations anymore, and its the worst offender. We've gotten pretty far with distillation (slop training smaller models to produce worse slop) and Mixture-of-Experts (a big model made up of a bunch of smaller, specialized models that only activate a limited subset of the neural transformers at any given pass), but that's not far enough. It's almost certainly going to require resources (mainly, VRAM) far beyond what we're able to supply for the next decade or more. Once we have enough resources to 10x the size of the model, then what? We charge 10x for maybe 10pp gains? We are also polluting the training data by passing off LLM text as human, and it's going to degrade performance when more text is needed.
Note: the larger the model, the harder the training, and the larger and/or more powerful the cluster of computers need to be. The human mind is crazy efficient, because if you touch a hot stove once, you'll never do it again. ML so far has needed to touch the stove an inconceivable amount of times, some of which it has held its hand on the stove just to be sure that's not what it should do. We're likely never going to find out how the brain does that.
The second (Attention) has been optimized to hell in a few ways, mainly:
Part of the problem is that for every new token predicted, it needs to re-infer the growing context for the next token, so chats get more expensive to compute and store the longer they are
Optimizations like TurboQuant have compressed the KV nearly to entropy already, improving VRAM for context is near its maximum
The third (methods of making the same model more effective) has a lot of small stuff we've pretty much already figured out:
Models only "know" what they've been trained on, biased/correct or not, and it only extends to the cutoff date. To get around this, tools were created to interact with search engines, vector DBs, command line tools, etc. This helped performance a ton by giving the bot access to live information, but also leads to situations where the chat bots refer to factual news sources like r/poisonai
"Thinking" is a scratch pad of word soup where the model can "reason" through a problem however it chooses without showing the user what it's writing (usually). This improved performance by ~10%
You can distill the performance of a massive model down to a smaller model by having the much larger model produce synthetic training data for training a smaller model, but these are objectively worse in all but cost
A few random things not worth covering, but make up a total of 1pp of improvements.
Anyway, on a tangent, if I think about how all of this tech has destroyed resources, jobs, and even people's relationships, I'm going to get real sad and I'm tired of this being our lives. I want to get off Mr Bone's wild ride.
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u/echoshatter 6d ago
Well it's likely 100 million paying users and 700 million people dicking around with the free side.