Scaling LLMs and Accelerating Adoption with Aidan Gomez at Cohere
Gradient Dissent: Conversations on AI
The Future of State-Space Models
The idea is that we're trying to cut some middle ground in between transformers, which are like fully auto-aggressive. And then on the other end of the spectrum are LS Tamps or RNNs, which they have a state and they just- they need to memorize in order to remember the past. So SSMs are trying to find this middle ground where, yeah, you have some window within which you can do look up. But for everything that's outside of that, you can rely on an internal memory that you can read and write from. The general principle is- Well, what makes you think that they're promising if you don't understand? I'm kidding.
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