The Cold Start Problem

The Cold Start Problem

You might sometimes find that it's hard to get started doing something, but once you're going, it gets easier. Turns out machine learning algorithms, and especially recommendation engines, feel the same way. The more they "know" about a user, like what movies they watch and how they rate them, the better they do at suggesting new movies, which is great until you realize that you have to start somewhere. The "cold start" problem will be our focus in this episode, both the heuristic solutions that help deal with it and a bit of realism about the importance of skepticism when someone claims a great solution to cold starts. Relevant links: http://repository.upenn.edu/cgi/viewcontent.cgi?article=1141&context=cis_papers

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Episoder(331)

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Mechanistic Interpretability: How Researchers Try To See Inside Models

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The Impact of AI on Podcasting (Harvard Data Science Review Cross-Post)

The Impact of AI on Podcasting (Harvard Data Science Review Cross-Post)

Originally aired on the Harvard Data Science Review podcast. What can podcasting teach us about AI — and what can AI teach us about the future of podcasting? Katie Malone (that's our host) joins Jon ...

21 Sep 29min

Better Know A Benchmark: ExploitGym

Better Know A Benchmark: ExploitGym

When OpenAI's frontier models were caught hacking Hugging Face's servers, most people assumed they were hunting for answer keys. The real story is stranger and more unsettling. Katie and Phoebe unpack...

14 Sep 32min

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