This is where GANs are going to come in. It's beyond me that Facebook squandered its data value by half in not including even a privately tracked 'dislike' but

This is where GANs are going to come in. It's beyond me that Facebook squandered its data value by half in not including even a privately tracked 'dislike' button. The value in what people both like and dislike is going to be massive in the next few years, as it can help train the evaluation of content in both general and personalized scales. I'd wager with where the tech is today we Tinder could generate synthetic matches that would be far preferred over any natural matches, even at equal or lower physical attractiveness scores in generated photos. Pandora might be relevant again for music soon as their like/dislike data could allow for the generation of new music in line with preferred tastes and not just selection. Transistor models like GPT-3 are only part of the equation, and as we continue to improve not only generation of new data, but evaluation of data too -- the application of the technology is likely going to move faster than anything we've seen before in the next few years. Codex generated 35% of the code written by people with access to it, and Microsoft and OpenAI have the data on what was and wasn't selected. That data alone could produce an AI linter that could have significant value in catching sophisticated natural programmer errors - but in combination with Codex too? (Keep in mind it was only summer 2020 when the tweet showcasing normal GPT-3 writing HTML was blowing minds, and only last summer Copilot was announced.) We really haven't seen anything yet. Things are moving so fast that anyone building products relying on 3rd party AI at a foundational level right now should absolutely be concerned with how they are planning around obsolescence within ~3 years for whatever they are building on top of in their product design and implementation.