A Google study finds that the standard three to five human raters per test example often aren't enough for reliable AI benchmarks, and that splitting your annotation budget the right way matters just as much as the budget itself.<br /> The article AI benchmarks systematically ignore how humans disagree, Google study finds appeared first on The Decoder. [...]
Artificial intelligence agents powered by the world's most advanced language models routinely fail to complete even straightforward professional tasks on their own, according to groundbreaking re [...]
For three decades, the web has been designed with one audience in mind: People. Pages are optimized for human eyes, clicks and intuition. But as AI-driven agents begin to browse on our behalf, the hum [...]
Elon Musk says his AI chatbot Grok is committed to "political neutrality" and being "maximally truth-seeking," according to xAI. But a recent New York Times analysis shows that Gro [...]
A new international study highlights major problems with large language model (LLM) benchmarks, showing that most current evaluation methods have serious flaws.<br /> The article Most LLM benchm [...]
Benchmarks are supposed to measure AI model performance objectively. But according to an analysis by Epoch AI, results depend heavily on how the test is run. The research organization identifies numer [...]
Google on Monday unveiled the most significant upgrade to its autonomous research agent capabilities since the product's debut, launching two new agents — Deep Research and Deep Research Max †[...]
A large-scale study shows that AI agent development focuses almost entirely on programming tasks, ignoring the vast majority of the labor market.<br /> The article AI agent benchmarks obsess ove [...]