Jeff Dean: belief

Transcript context

…Well, also, I think that the classic example is you start going beyond language into like proteins and whatever else is extremely information dense. Yeah. Yeah. I mean, I think one of the things about Gemini’s multimodal aspects is we’ve always wanted it to be multimodal from the start. And so, you know, that sometimes to people means text and images and video sort of human-like and audio, audio, human-like modalities. But I think it’s also really useful to have Gemini know about non-human modalities. Yeah. Like LIDAR sensor data from. Yes. Say, Waymo vehicles or. Like robots or, you know, various kinds of health modalities, x-rays and MRIs and imaging and genomics information. And I think there’s probably hundreds of modalities of data where you’d like the model to be able to at least be exposed to the fact that this is an interesting modality and has certain meaning in the world. Where even if you haven’t trained on all the LIDAR data or MRI data, you could have, because maybe that’s not, you know, it doesn’t make sense in terms of trade-offs of. You know, what you include in your main pre-training data mix, at least including a little bit of it is actually quite useful. Yeah. Because it sort of tempts the model that this is a thing. Yeah. Do you believe, I mean, since we’re on this topic and something I just get to ask you all the questions I always wanted to ask, which is fantastic. Like, are there some king modalities, like modalities that supersede all the other modalities? So a simple example was Vision can, on a pixel level, encode text. And DeepSeq had this DeepSeq CR paper that did that. Vision. And Vision has also been shown to maybe incorporate audio because you can do audio spectrograms and that’s, that’s also like a Vision capable thing. Like, so, so maybe Vision is just the king modality and like. Yeah.…

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