Ming-Lite-Uni: An Open-Source AI Framework Designed to Unify Text and Vision through an Autoregressive Multimodal Structure

Ming-Lite-Uni: An Open-Source AI Framework Designed to Unify Text and Vision through an Autoregressive Multimodal Structure

Multimodal AI rapidly evolves to create systems that can understand, generate, and respond using multiple data types within a single conversation or task, such as text, images, and even video or audio. These systems are expected to function across diverse interaction formats, enabling more seamless human-AI communication. With users increasingly engaging AI for tasks like…

Read More
OpenAI’s new voice AI model gpt-4o-transcribe lets you add speech to your existing text apps in seconds

OpenAI’s new voice AI model gpt-4o-transcribe lets you add speech to your existing text apps in seconds

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More OpenAI’s voice AI models have gotten it into trouble before with actor Scarlett Johansson, but that isn’t stopping the company from continuing to advance its offerings in this category. Today, the ChatGPT maker has unveiled three,…

Read More
Ghostbuster: Detecting Text Ghostwritten by Large Language Models

Ghostbuster: Detecting Text Ghostwritten by Large Language Models

The structure of Ghostbuster, our new state-of-the-art method for detecting AI-generated text. Large language models like ChatGPT write impressively well—so well, in fact, that they’ve become a problem. Students have begun using these models to ghostwrite assignments, leading some schools to ban ChatGPT. In addition, these models are also prone to producing text with factual…

Read More
This AI Paper from Tel Aviv University Introduces GASLITE: A Gradient-Based Method to Expose Vulnerabilities in Dense Embedding-Based Text Retrieval Systems

This AI Paper from Tel Aviv University Introduces GASLITE: A Gradient-Based Method to Expose Vulnerabilities in Dense Embedding-Based Text Retrieval Systems

Dense embedding-based text retrieval has become the cornerstone for ranking text passages in response to queries. The systems use deep learning models for embedding text into vector spaces that enable semantic similarity measurements. This method has been adopted widely in applications such as search engines and retrieval-augmented generation (RAG), where retrieving accurate and contextually relevant…

Read More