Missing code and data make it tough to compare AI work. That may hurt progress. The problem: Science reports that of a sample of 400 papers from AI conferences in recent years, only 6 percent of presenters shared code. Just a third shared data. Why it matters: Without access to that information it’s hard to reproduce a study’s findings, making it impossible to benchmark tools that could push the field forward. How to solve it: There’s a clear culture of keeping such details under wraps. Some meetings and journals are now encouraging sharing. Perhaps more ought follow.
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