I’m going to say x=7, y=10. The sum x+y is not 10, because choosing the next word accurately in a complex passage is hard. The x is 7, just based on my gut guess about how smart they are - by different empirical measures it could be 2 or 40.

They’re still much closer to token predictors than any sort of intelligence. Even the latest models “with reasoning” still can’t answer basic questions most of the time and just ends up spitting back out the answer straight out of some SEO blogspam. If it’s never seen the answer anywhere in its training dataset then it’s completely incapable of coming up with the correct answer.

Such a massive waste of electricity for barely any tangible benefits, but it sure looks cool and VCs will shower you with cash for it, as they do with all fads.

You’re trying to graph something that you can’t quantify.

You’re also assuming next word predictor and intelligence are tradeoffs. They could as well be the same.

Are you interested in this from a philosophical perspective or from a practical perspective?

From a philosophical perspective:

It depends on what you mean by “intelligent”. People have been thinking about this for millennia and have come up with different answers. Pick your preference.

From a practical perspective:

This is where it gets interesting. I don’t think we’ll have a moment where we say “ok now the machine is intelligent”. Instead, it will just slowly and slowly take over more and more jobs, by being good at more and more tasks. And just so, in the end, it will take over a lot of human jobs. I think people don’t like to hear it due to the fear of unemployedness and such, but I think that’s a realistic outcome.

Wondering if Modern LLMs like GPT4, Claude Sonnet and llama 3 are closer to human intelligence or next word predictor.

They are good at sounding intelligent. But, LLMs are not intelligent and are not going to save the world. In fact, training them is doing a measurable amount of damage in terms of GHG emissions and potable water expenditure.