Ali Behrouz: prediction

Transcript context

…From. So for the second question, I think I in my opinion the current models are very efficient from one specific point of view. Why they are efficient? Because because you can see that from what we can get from LLMS, they are very cheap. And for example, you can, you know, if you want to match their power in some of the tasks with human, then potentially the cost would be very, very different. So from that perspective we can add computation and generally like the current LLM paradigms are is is very like efficient and we can perform more computation per each parameters or artificial neuron that we have into our model and it can help us for different things. One is that it can help us to have more, we can have a smarter model. It's it's, it's a very like subjective term to describe this. But you know, when we have more computation, it seems that we are performing some internal thinking. So a simple LM vendor is that, let's say that I have a simple LM structure based on Transformers. When the token comes, I will like do some computation on the token, pass it through all the layers and then predict the next token. And that's how how it works. But now let's assume that for this specific token, instead of just a simple compute, the simple pass of computation, I also perform more internal computation with respect to the past data or generally like. You know.…

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