I took humans thousands of years, then hundreds of years, to come to terms with very basic concepts about numbers.
Its amazing to me when people talk about recombining things, or following up on things as somehow lesser work.
People can't separate the perspective they were given when they learned the concepts, that those who developed the concepts didn't have because they didn't exist.
Simple things are hard, or everything simple would have been done hundreds of years ago, and that is certainly not the case. Seeing something others have not noticed is very hard, when we don't have the concepts that the "invisible" things right in front of us will teach us.
It's why the invention of teaching has been so important. Took a long time for humans to develop calculus. A long time to then refine it and make it much more useful. But then in a year or two an average person can learn what took hundreds of years to invent. It's crazy to equate these tasks as being the same. Even incremental innovation is difficult. You have to see something billions of people haven't. But there's also paradigm shifts and well... if you're not considered crazy at first then did you really shift a paradigm?
When people say this what they mean is that we've had plausibly useful LLMs for around three years, and I would say that is basically true. The stuff before 2023 could barely be classified above the level of an interesting toy.
OpenAI themselves must not have a "reasonable definition of L", then. Their own papers and press releases refer to GPT-2 (from 2019) as a "large language model".
Yes, and 1.5 billion parameters meets no reasonable current definition of large. It would be considered a tiny language model. OpenAI themselves refer to their small/fast models as small models all over their documentation.
Sure we do, since Fei-Fei Li and team created that annotated dataset, which allowed to train first LLMs. So LLMs are here for more than a decade already.
I took humans thousands of years, then hundreds of years, to come to terms with very basic concepts about numbers.
Its amazing to me when people talk about recombining things, or following up on things as somehow lesser work.
People can't separate the perspective they were given when they learned the concepts, that those who developed the concepts didn't have because they didn't exist.
Simple things are hard, or everything simple would have been done hundreds of years ago, and that is certainly not the case. Seeing something others have not noticed is very hard, when we don't have the concepts that the "invisible" things right in front of us will teach us.
Anyone in the arts is aware that creativity is not the new, it is the repackaging of what already exists into something that is itself new.
Except for "Being John Malkovich". That movie was way out there on its own.
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It's why the invention of teaching has been so important. Took a long time for humans to develop calculus. A long time to then refine it and make it much more useful. But then in a year or two an average person can learn what took hundreds of years to invent. It's crazy to equate these tasks as being the same. Even incremental innovation is difficult. You have to see something billions of people haven't. But there's also paradigm shifts and well... if you're not considered crazy at first then did you really shift a paradigm?
When people say this what they mean is that we've had plausibly useful LLMs for around three years, and I would say that is basically true. The stuff before 2023 could barely be classified above the level of an interesting toy.
When people say this what they mean is that we've had plausibly useful LLMs for around three years, and I would say that is basically true.
No, we haven't, for any reasonable definition of L.
OpenAI themselves must not have a "reasonable definition of L", then. Their own papers and press releases refer to GPT-2 (from 2019) as a "large language model".
https://openai.com/index/better-language-models/
Yes, and 1.5 billion parameters meets no reasonable current definition of large. It would be considered a tiny language model. OpenAI themselves refer to their small/fast models as small models all over their documentation.
Sure we do, since Fei-Fei Li and team created that annotated dataset, which allowed to train first LLMs. So LLMs are here for more than a decade already.
You are confused by what the L and L mean in LLM, or which data set she created, or both, or in general.
Fine, 8 years? That's not a long time