GPT models are usually benchmarked against certain tasks. Now that you put a code interpreter in it, all of a sudden it's not a math in the tokenizer in the latent space question,. It's like, can you write code that answers the math question? So that kind of enables a lot more use cases that are just not possible with the transformer architecture of the underlying model. And then the other thing is that when it first came out, people are like, oh, this is great for developers. But there's this whole other side of the world, which is, hey, I have this very basic thing to do.
Today NLW is joined by Swyx and Alessio, the hosts of the Latent Space podcast to discuss the key technical developments from the last month of AI, including code interpreter; llama 2; the latest in AI agents; growing interest in AI companions, and more.
Latent Space podcast -https://www.latent.space/podcast / https://twitter.com/latentspacepod
Swyx - https://twitter.com/swyx
Alessio Fanelli - https://twitter.com/FanaHOVA
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