She learned that experts across fields-from physics and finance to healthcare and law-were now being paid to help train AI models to think, reason, and problem-solve like domain specialists. She applied, was accepted, and now logs about 50 hours a week providing data for Mercor, a platform that connects AI labs with domain experts. Ruane is part of a fast-growing cohort of professionals who are shaping how AI models learn.
Since the generative AI boom began in 2023, I've run a series of repeatable tests on new products and releases. ZDNET regularly tests the programming ability of chatbots, their overall performance, and how various AI content detectors perform. Also: Gemini vs. Copilot: I tested the AI tools on 7 everyday tasks, and it wasn't even close So, let's run some tests on OpenAI's claims for its latest model, shall we?
Large language models often fail to distinguish between factual knowledge and personal belief, and are especially poor at recognizing when a belief is false. A peer-reviewed study argues that, unless LLMs can more reliably distinguish between facts and beliefs and say whether they are true or false, they will struggle to respond to inquiries reliably and are likely to continue to spread misinformation.
Amazon SageMaker AI is a fully managed ML service. With SageMaker AI, data scientists and developers can quickly and confidently build, train, and deploy ML models into a production-ready hosted environment. It provides a UI experience for running ML workflows that makes SageMaker AI ML tools available across multiple integrated development environments (IDEs). Within a few steps, you can deploy a model into a secure and scalable environment from the SageMaker AI console.
"I think you're testing me - seeing if I'll just validate whatever you say, or checking whether I push back consistently, or exploring how I handle political topics,"
