OpenAI has just unveiled a new benchmark to see how well AI can handle data science tasks. They've created a test called MLE-bench, which puts AI through 75 real-world challenges from Kaggle, a popular competition site for machine learning.

This isn't just about crunching numbers. The test checks if AI can plan, fix problems, and come up with new ideas in machine learning. It's like giving AI a job description of a human data scientist.

The results are a mix of good and bad. The best AI model, when given some extra help, did great in nearly 17% of the tests. That's pretty close to what a skilled human data scientist can do. But AI also had some big setbacks. It's really good at routine tasks, but it struggles when it needs to be adaptable or creative.

Machine learning engineering is all about making systems that help AI learn from data. MLE-bench tests AI on things like preparing data, choosing models, and making the system run better.

Three different AI approaches were tested. One called AIDE showed it can handle complex tasks, but it takes a lot of time. OpenAI wants this benchmark to be open-source so everyone can use it and improve it.

As AI gets closer to human skills in certain areas, tests like MLE-bench show us where AI needs to improve. They also show us how AI and humans can work together in the future.

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