I'll paste my response to your pre-edited questions:
I think atomic weapons peaked during the trinity test. The subsequent creation of bigger explosions by packing more fissile material does not meaningfully improve the technology.
Since LLMs are a generative technology, let's use a generative skill - painting. There are tons of brilliant painters, all with their own styles. What I think is consistent among master painters is their effortlessness in their craft. Honing a skill makes subsequent attempts less effortful.
To me, LLMs are more like atomic bombs than master artisans. To get better results, add neural nets. What would be meaningful to me, is to constrain models to a fixed amount of compute, run them on any of the copious amount of benchmarks, and see if they get the same scores at increasingly faster speeds. This, at least to me, signals mastery.
On the date for evaluation, I will set my own prediction instead, that AI labs will not become profitable, local models will drain their moat.
Taken on its own, I will concede that Zitron's predictions are wrong, but it is exactly why I bring up market irrationality. The multiple rounds of funding is propping up the unsustainable business model. Without it, user numbers and revenue can't grow.
I forsee real innovation in the AI space after the bubble pops.
Just this morning, I had to read through an LLM response about how a PC8-M5 fitting has a high flow rate because it connects to an 8mm tube, completely ignoring that the threaded M5 on the other end will only have space for a 2mm hole, so it still seems quite similar to the LLMs of 2020.
I think atomic weapons peaked during the trinity test. The subsequent creation of bigger explosions by packing more fissile material does not meaningfully improve the technology.
Since LLMs are a generative technology, let's use a generative skill - painting. There are tons of brilliant painters, all with their own styles. What I think is consistent among master painters is their effortlessness in their craft. Honing a skill makes subsequent attempts less effortful.
To me, LLMs are more like atomic bombs than master artisans. To get better results, add neural nets. What would be meaningful to me, is to constrain models to a fixed amount of compute, run them on any of the copious amount of benchmarks, and see if they get the same scores at increasingly faster speeds. This, at least to me, signals mastery.
On the date for evaluation, I will set my own prediction instead, that AI labs will not become profitable, local models will drain their moat.
Taken on its own, I will concede that Zitron's predictions are wrong, but it is exactly why I bring up market irrationality. The multiple rounds of funding is propping up the unsustainable business model. Without it, user numbers and revenue can't grow.
I forsee real innovation in the AI space after the bubble pops.
Just this morning, I had to read through an LLM response about how a PC8-M5 fitting has a high flow rate because it connects to an 8mm tube, completely ignoring that the threaded M5 on the other end will only have space for a 2mm hole, so it still seems quite similar to the LLMs of 2020.