Why Tejal Patwardhan stopped underestimating the models - Episode 21
Episode publishedPodcast: OpenAI PodcastOpenAI Podcast
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Hello. I'm Andrew Maine, and welcome to the OpenAI podcast. On today's episode, we're talking to the research lead Tejal Patwarden about the need to build frontier evals as old benchmarks get saturated.
Generally bad. French maxing is bad. How can we make these models useful for people in their real work? We were really nervous because we were like, this human baseline is kind of hard. We don't know if the model is going to beat it, but we should never underestimate the model.
Tangela, I have a question. How did you end up where you were? What brought you into OpenAI?
Oh, I thought we weren't gonna start with this one.
Tejal, I have a question for you. What would you like to start with?
Can we start with like, tell us, like, what you did when you started OpenAI, and then you can, like, work work backwards.
Don't you wanna talk about your early days?
No. I I grew up OpenAI. This is, like, not Okay.
Tell me a bit about your journey here working inside artificial intelligence inside OpenAI.
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