When models are trained on unverified AI slop, results drift from reality fast. Here's how to stop the spread, according to Gartner.

The company is positioning this approach as a turning point for robotics, comparable to what large generative models have done for text and images.

Google, Microsoft and Nvidia are among the names vying to make forecasts more accurate for longer.

New “AI GYM for Science” dramatically boosts the biological and chemical intelligence of any causal or frontier LLM, ...

For a project in Bangladesh, Prof. Mushfiq Mobarak and his team used machine-learning models applied to mobile phone records ...

Brex reports that recurring revenue, a predictable income stream, is key for modern business growth and stability, ...

Learn to apply Bayes' theorem in financial forecasting for insightful, updated predictions. Enhance decision-making with ...

Modern vision-language models allow documents to be transformed into structured, computable representations rather than lossy text blobs.

Organizations have a wealth of unstructured data that most AI models can’t yet read. Preparing and contextualizing this data is essential for moving from AI experiments to measurable results.

Abstract: The data-driven techniques have been developed to deal with the output regulation problem of unknown linear systems by various approaches. In this article, we first extend an existing result ...

Statistical models predict stock trends using historical data and mathematical equations. Common statistical models include regression, time series, and risk assessment tools. Effective use depends on ...

Chances are, you’ve seen clicks to your website from organic search results decline since about May 2024—when AI Overviews launched. Large language model optimization (LLMO), a set of tactics for ...