M_C
Founding Member
Yan Lacunn seems to be singing a different tune these days........ thoughts??
YC: No, it does, for this model for autonomous AI that I've proposed, which has a single configurable world model simulation engine for the purpose of planning and imagining the future and filling in the blanks of things that you cannot completely observe. There is a computational advantage to having a single model that is configurable. Having a single-engine that you configure may allow the system to share that knowledge across tasks, things that are common to everything in the world that you've learned by observation or things like basic logic. It's much more efficient to have that big model that you configure than to have a completely separate model for different tasks which may have to be trained separately. But we see this already, right?
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It used to be, back in the old days at Facebook -- when it was still called Facebook, the vision we were using for analyzing images, to do ranking and filtering, we had specialized neural nets, specialized convolutional nets, basically, for different tasks. And now we have one gigantic one that does everything. We used to have half a dozen ConvNets; now, we have only one.
So, we see that convergence. We even have architectures now that do everything: they do vision, they do text, they do speech, with a single architecture. They have to be trained separately for the three tasks, but this work, data2vec, it's a self-supervised approach.
ZDNet: Most intriguing! Thank you for your time.
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www-zdnet-com.cdn.ampproject.org
YC: No, it does, for this model for autonomous AI that I've proposed, which has a single configurable world model simulation engine for the purpose of planning and imagining the future and filling in the blanks of things that you cannot completely observe. There is a computational advantage to having a single model that is configurable. Having a single-engine that you configure may allow the system to share that knowledge across tasks, things that are common to everything in the world that you've learned by observation or things like basic logic. It's much more efficient to have that big model that you configure than to have a completely separate model for different tasks which may have to be trained separately. But we see this already, right?
.
It used to be, back in the old days at Facebook -- when it was still called Facebook, the vision we were using for analyzing images, to do ranking and filtering, we had specialized neural nets, specialized convolutional nets, basically, for different tasks. And now we have one gigantic one that does everything. We used to have half a dozen ConvNets; now, we have only one.
So, we see that convergence. We even have architectures now that do everything: they do vision, they do text, they do speech, with a single architecture. They have to be trained separately for the three tasks, but this work, data2vec, it's a self-supervised approach.
ZDNet: Most intriguing! Thank you for your time.
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