AI

As Washington pushes for a TikTok deal, Beijing is countering with probes into American chipmakers.

A wave of AI users presenting in states of psychological distress gave birth to an unofficial diagnostic label. Experts say it’s neither accurate nor needed, but concede that it’s likely to stay.

Generative AI solutions like Amazon Q Business are transforming the way employees work. Organizations in every industry are embracing these tools to help their workforce extract valuable insights from increasingly fragmented data to accelerate decision-making processes. However, the adoption of generative AI tools hasn’t been without its challenges. Two hurdles have emerged in the implementation

OpenAI has released two open-weight models, gpt-oss-120b (117 billion parameters) and gpt-oss-20b (21 billion parameters), both built with a Mixture of Experts (MoE) design and a 128K context window. These models are the leading open source models, according to Artificial Analysis benchmarks, and excel at reasoning and agentic workflows. With Amazon SageMaker AI, you can

The Nvidia CEO reveals his consuming love for Google’s image generator, the artsy side of Grok, and what exactly he uses Perplexity, Gemini, and ChatGPT for right now.

Microsoft and Nvidia unveiled plans to invest up to $45 billion in the UK during US President Donald Trump’s state visit.

CEO Sam Altman announced an age-prediction system and new parental controls in a blog post on Tuesday.

This post is co-written with Samit Verma, Eusha Rizvi, Manmeet Singh, Troy Smith, and Corey Finley from Verisk. Verisk Rating Insights as a feature of ISO Electronic Rating Content (ERC) is a powerful tool designed to provide summaries of ISO Rating changes between two releases. Traditionally, extracting specific filing information or identifying differences across multiple

Organizations gain competitive advantage by deploying and integrating new generative AI models quickly through Generative AI Gateway architectures. This unified interface approach simplifies access to multiple foundation models (FMs), addressing a critical challenge: the proliferation of specialized AI models, each with unique capabilities, API specifications, and operational requirements. Rather than building and maintaining separate integration

When researchers are building large language models (LLMs), they aim to maximize performance under a particular computational and financial budget. Since training a model can amount to millions of dollars, developers need to be judicious with cost-impacting decisions about, for instance, the model architecture, optimizers, and training datasets before committing to a model. To anticipate