AutoML is dead an LLMs have killed it? MLGym is a benchmark and framework testing this theory. Roberta Raileanu and Deepak Nathani discuss how well current LLMs are doing at solving ML tasks, what the biggest roadblocks are, and what that means for AutoML generally. Check out the paper: https://arxiv.org/pdf/2502.14499 More on Roberta: https://rraileanu.github.io/ More on Deepak: https://dnathani.net/
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AutoML is dead an LLMs have killed it? MLGym is a benchmark and framework testing this theory. Roberta Raileanu and Deepak Nathani discuss how well current LLMs are doing at solving ML tasks, what the biggest roadblocks are, and what that means for AutoML generally. Check out the paper: https://arxiv.org/pdf/2502.14499 More on Roberta: https://rraileanu.github.io/ More on Deepak: https://dnathani.net/
AutoML can be a tool for good, but there are pitfalls along the way. Rahul Sharma and David Selby tell us about how AutoML systems can be used to give us false impressions about explainability metrics of ML systems - maliciously, but also on accident. While this episode isn't talking about a new exciting AutoML method, it can tell us a lot about what can go wrong in applying AutoML and what we should think about when we build tools for ML novices to use.
The AutoML Podcast
AutoML is dead an LLMs have killed it? MLGym is a benchmark and framework testing this theory. Roberta Raileanu and Deepak Nathani discuss how well current LLMs are doing at solving ML tasks, what the biggest roadblocks are, and what that means for AutoML generally. Check out the paper: https://arxiv.org/pdf/2502.14499 More on Roberta: https://rraileanu.github.io/ More on Deepak: https://dnathani.net/
