The transition out of API is a very discreet one in some sense. I don't think the last thing you might should be like, let's go pre-train a 500 billion parameter model when you don't know what application you're building. And then in some cases, maybe Fuchsia learning is actually for some things actually not that strong if you have, for example, data and maybe like a fine-tune T5 model or something much smaller can actually be effective. David: How do you work around some of these limitations in the actual interface to these models such that it doesn't become a problem for your users to use that? We'll talk more about HCI next week
A re-broadcast of Greylock general partner Saam Motamedi's interview with Adept CEO and co-founder David Luan and Stanford computer science and statistics professor Percy Liang. In this conversation (recorded in mid-2022), David and Percy discuss how large language models are paving the way for the next wave of AI. Adept is developing an AI "teammate" tool that is trained to use every software tool and API for knowledge workers. The company just raised $350 million in Series B funding to further its mission. Greylock has been partnered with Adept since co-leading the Series A in 2022.
You can watch the video from this interview on our YouTube channel here:
https://youtu.be/_ydBm3tADvA
You can read a transcript of the conversation on our website here: https://greylock.com/greymatter/ai-language-words-into-action/
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