How many AI models is too many? It depends on how you look at it, but 10 a week is probably a bit much. That's roughly how many we've seen roll out in the
If we were to relax the conditions for inclusion a bit, there would dozens: some fine-tuned existing models, some combos like Idefics 2, some experimental or niche, and so on.
Some models like ChatGPT and Gemini have evolved into entire web platforms, spanning multiple use cases and access points.
Other large language models like LLaMa or OLMo — though they technically share a basic architecture — don’t actually fill the same role.
There’s some deliberate confusion about these two things, because the models’ developers want to borrow a little of the fanfare associated with major AI platform releases, like your GPT-4V or Gemini Ultra.
There were papers, models, and research constantly coming out, and conferences like SIGGRAPH and NeurIPS were filled with machine learning engineers comparing notes and building on one another’s work.
The simple truth is that none of these models is going to be that kind of big step, since OpenAI’s advance was built on a fundamental change to machine learning architecture that every other company has now adopted, and which has not been superseded.
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This is the best summary I could come up with:
If we were to relax the conditions for inclusion a bit, there would dozens: some fine-tuned existing models, some combos like Idefics 2, some experimental or niche, and so on.
Some models like ChatGPT and Gemini have evolved into entire web platforms, spanning multiple use cases and access points.
Other large language models like LLaMa or OLMo — though they technically share a basic architecture — don’t actually fill the same role.
There’s some deliberate confusion about these two things, because the models’ developers want to borrow a little of the fanfare associated with major AI platform releases, like your GPT-4V or Gemini Ultra.
There were papers, models, and research constantly coming out, and conferences like SIGGRAPH and NeurIPS were filled with machine learning engineers comparing notes and building on one another’s work.
The simple truth is that none of these models is going to be that kind of big step, since OpenAI’s advance was built on a fundamental change to machine learning architecture that every other company has now adopted, and which has not been superseded.
The original article contains 803 words, the summary contains 177 words. Saved 78%. I’m a bot and I’m open source!