AI Needs a New Report Card

Without robust, context-sensitive benchmarks, we risk importing flawed models from tech giants and deploying them in environments they were never designed for

- ROHIT KUMAR SINGH

WE LIVE IN an age captivated by the rapid ascent of artificial intelligence (AI). Machines that can write poetry, generate stunning artwork, and even hold conversations are becoming commonplace. It feels like we are on the cusp of something revolutionary. But how do we actually know how smart these AI tools are becoming? How do we measure their progress? Just like students take exams, AI developers rely on tests called "benchmarks" to grade their creations. These benchmarks have become the de facto report card for AI, guiding trillions of dollars in investment and shaping the future of the technology.

But what if the tests are flawed? What if the report card isn't telling the whole story? Imagine using a third-grade spelling test to assess a university professor's overall intellect. They would ace it, sure, but it wouldn't tell you much about their ability to conduct complex research or lecture on quantum physics. According to a growing chorus of experts, we might be facing a similar situation with AI. The benchmarks we have relied on, some with rather colorful acronyms like "HellaSwag," are increasingly seen as inadequate rulers for measuring the burgeoning capabilities of modern AI.