I can't assess this, but I do worry that overnight some algorithmic advance will enhance LLMs by orders of magnitude and the next big model to get trained is suddenly 10,000x better than GPT-4 and nobody's ready for it.
Are you assuming that this particular "progress" would be relatively innocent?
I'm not assuming that something 10k x better than GPT-4 will be good or bad; I don't know. I was just curious what exactly to be worried about. I think in the current state, LLMs are already advanced enough for bad uses like article generation for SEO, spam, scams, etc., and I wonder if an order of magnitude better model would allow for something worse.
What do you mean by "better"?
"Better" was referring to what OP wrote in the top comment. I guess 10x faster, 10x longer context, and 100x less prone to hallucinations would make a good "10k x better" than GPT-4.
Those peasants payed taxes, i.e. some of their work was exploited by an army or a priest rather than hunger, and as you mention, they did not work "12 hrs/7 days per week".
Do you have a better example?
The way the habitat got destroyed by humans is stupid because it might put us in danger. You can call me "speciesist" but I do care more for humans rather than for a particular other specie.
So I think progress should be geared towards human species survival and if possible preventing other species extinction. Some of the current developments are a bit too much on the side of "I don't care about anyone's survival" (which is stupid and inefficient).
How do you come to the conclusion "stupid" rather than evil? Aren't we very aware of the consequences of how we are currently organising human societies, and have been for a long time?
If and when something radically better comes along, say an alternative to back-propagation that is more like the way our brains learn, it will need a lot of scaling and refinement to catch up with the then-current LLM.
I would worry if I'd own Nvidia shares.
1. A new architecture would make all/most of these upcoming Transformer accelerators obsolete => back to GPUs.
2. Higher performance LLMs on GPUs => we can speed up LLMs with 1T+ parameters. So, LLMs become more useful, so more of GPUs would be purchased.
There's no guarantee that that is what would happen. The right (or wrong, depending on your POV) algorithmic breakthrough might make GPU's obsolete for AI, by making CPU's (or analog computing units, or DSP's, or "other") the preferred platform to run AI.
1. Tight integration necessary for fundamental compute constraints like memory latency.
2. Economies of scale
3. Opportunity cost to AI orgs. Meta, OpenAI etc want 50k h100s to arrive in shipping container and plug in so they can focus on their value-add.
Everyone will have to readjust to this paradigm. Even if next get AI runs better on CPU, Intel won't suddenly be signing contracts to sell 1,000,000 xeons and 1,000,000 motherboards etc
Also, Nvidia have 25bn cash in hand and almost 10 billion yearly r&d spend. They've been an AI-first company for over a decade now, they're more prepared to pivot than anyone else
Edit: nearly forgot - Nvidia can issue 5% new stocks and raise 100B like it's nothing.