Multimodal Data
topic on 3 shows · 3 statements across 3 episodes
3 statements about Multimodal Data, every show
Rumbelow: Multimodal analysis is impossible without fine labeling or interpretability
“It's largely impossible to do good data analysis on multimodal data of this kind, unless you have really fine grained labeling of your images. For example, which is just very, very burdensome, but obviously deep learning, we can let the model figure out its ow…”
Kolter: Multimodal AI data is abundant but constrained by compute bottlenecks
“There are massive amounts of data available, And I think we have not yet figured out how to properly leverage those due to either limitations of compute. I mean, you have to process all that data and it does take, we don't have current models to do this very w…”
Alexandr Wang: Multimodality faces a scarcity of quality data for personal agents.
“So multimodality as an entire space is one where for the same reasons that we've like exhaust a lot of the internet data, there's a lot of scarcity for good multimodal data that can empower these personal agents and these personal
Use cases.”