by processing the videos in TT, looking at faces of the people, extrapolating their emotional state in the moment..
..the dataset could include the "emotional state" of the situation, the scene showed in the video. Then you'd have a dataset which would include "emotions" precisely described and associated to even a precise geographical location (i.e. the humor in Thailand would be different than the humor in Manhattan, NY),
Then you could train a LLM with a human emotional state analysis capability
..by not just inferring emotion by text (an "emerged" capability) like the text-trained LLM like chatGPT, but by clearly, certainly defined emotions attached to a precise scene and a precise text describing the scene.
Then you input a human conversation into the LLM, and make it infer what's going on in the scene, the situation behind the conversation, by knowing the emotional state of the different persons in the conversation.
And by now, there are billions of public conversations available to scrap, many of them fully attributable to fully identificable people (think Instagram).
There will be even more billions in the coming years, and the "emotional capable" LLM would just get better at its game.
No surprise the american government is - fastly, by all means necessary - looking to seriously slow down the chinese AI research complex.
best bet, it's feeding, right now, a giant dataset.
What someone could do with the known tecnology - not including hyphotetical AGI level tech, close to scifi - and that amount of information, probably automatically - even quite easily give how the input gets into the system - tagged and filtered data, could be in realms of several billions of value, maybe in months from now (just take a look at the value of OpenAI by now, 29 billions).
My 2nd bet is they are obviously using some of that data to improve their game, whatever systems they have behind the app, maximizing whatever thing they're maximizing (you'd think "money", but that's too linear, maybe they have 50 parallel targets).
..the dataset could include the "emotional state" of the situation, the scene showed in the video. Then you'd have a dataset which would include "emotions" precisely described and associated to even a precise geographical location (i.e. the humor in Thailand would be different than the humor in Manhattan, NY),
Then you could train a LLM with a human emotional state analysis capability
..by not just inferring emotion by text (an "emerged" capability) like the text-trained LLM like chatGPT, but by clearly, certainly defined emotions attached to a precise scene and a precise text describing the scene.
Then you input a human conversation into the LLM, and make it infer what's going on in the scene, the situation behind the conversation, by knowing the emotional state of the different persons in the conversation.
And by now, there are billions of public conversations available to scrap, many of them fully attributable to fully identificable people (think Instagram).
There will be even more billions in the coming years, and the "emotional capable" LLM would just get better at its game.
No surprise the american government is - fastly, by all means necessary - looking to seriously slow down the chinese AI research complex.