Definitely check again. That was how it worked with gpt-4, handing off to Dall-E.
4o (the ‘o’ stands for ‘omnimodel’) and Gemini Flash are native multimodal outputs. Completely just transformers.
It’s why those models can do things like complex analysis in the process of generating things.
For example, just today in a group chat where earlier on one model had “turned into” a unicorn and then the other models were pretending to be unicorns to fit in, dozens messages later the only direct prompt to an instance of 4o imagegen was “create a photorealistic picture of the room and everyone in it.”
The end result had exactly one actual unicorn and everyone else had horns taped on their head. That kind of situational awareness and nuanced tracking across a 100+ long message context isn’t possible in a CNN.
Also, if you really want your mind blown, check out Genie 3 and the several minute state change persistence. That one is really nuts and the kind of thing that should really have everyone seeing it questioning the empirical findings of our universe fundamentally being superimposed probabilities only collapsing based on attention. Eerily similar to what we’re just starting to be independently building.
As for the consumption — eating a single hamburger has a larger water/energy impact than a year of using these tools in average use. And even those inference costs are probably going to drop to effective insignificance within the decade. There’s been very promising advancements in light based neural networks, and those run at like 1,000-10,000x lower energy costs paramater to parameter.
Definitely check again. That was how it worked with gpt-4, handing off to Dall-E.
4o (the ‘o’ stands for ‘omnimodel’) and Gemini Flash are native multimodal outputs. Completely just transformers.
It’s why those models can do things like complex analysis in the process of generating things.
For example, just today in a group chat where earlier on one model had “turned into” a unicorn and then the other models were pretending to be unicorns to fit in, dozens messages later the only direct prompt to an instance of 4o imagegen was “create a photorealistic picture of the room and everyone in it.”
The end result had exactly one actual unicorn and everyone else had horns taped on their head. That kind of situational awareness and nuanced tracking across a 100+ long message context isn’t possible in a CNN.
Also, if you really want your mind blown, check out Genie 3 and the several minute state change persistence. That one is really nuts and the kind of thing that should really have everyone seeing it questioning the empirical findings of our universe fundamentally being superimposed probabilities only collapsing based on attention. Eerily similar to what we’re just starting to be independently building.
As for the consumption — eating a single hamburger has a larger water/energy impact than a year of using these tools in average use. And even those inference costs are probably going to drop to effective insignificance within the decade. There’s been very promising advancements in light based neural networks, and those run at like 1,000-10,000x lower energy costs paramater to parameter.
Thanks I’ll give it a new look!