multimodal
SEA-LION v4: Multimodal Language Modeling for Southeast Asia
[ad_1] AI Singapore (AISG) has released SEA-LION v4, an open-source multimodal language model developed in collaboration with Google and based on the Gemma 3 (27B) architecture. The model is designed to support Southeast Asian languages, including those with limited digital resources, and provides both text and image understanding capabilities. SEA-LION v4 uses a commercially permissive…
Zhipu AI Releases GLM-4.5V: Versatile Multimodal Reasoning with Scalable Reinforcement Learning
[ad_1] Zhipu AI has officially released and open-sourced GLM-4.5V, a next-generation vision-language model (VLM) that significantly advances the state of open multimodal AI. Based on Zhipu’s 106-billion parameter GLM-4.5-Air architecture—with 12 billion active parameters via a Mixture-of-Experts (MoE) design—GLM-4.5V delivers strong real-world performance and unmatched versatility across visual and textual content. Key…
Mastering Multimodal UX: Best Practices for Seamless User Interactions
[ad_1] We all know the feeling: you’re in the middle of an interaction, your app is humming along beautifully, and then—bam!—you hit a wall. Maybe it’s because your user is trying to control a device with a voice command while also swiping through a page, or they’re moving between devices and losing all context. Multimodal…
Ming-Lite-Uni: An Open-Source AI Framework Designed to Unify Text and Vision through an Autoregressive Multimodal Structure
[ad_1] 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…
How Patronus AI’s Judge-Image is Shaping the Future of Multimodal AI Evaluation
[ad_1] Multimodal AI is transforming the field of artificial intelligence by combining different types of data, such as text, images, video, and audio, to provide a deeper understanding of information. This approach is similar to how humans process the world around them using multiple senses. For example, AI can examine medical images in healthcare while…
This AI Paper Introduces R1-Onevision: A Cross-Modal Formalization Model for Advancing Multimodal Reasoning and Structured Visual Interpretation
[ad_1] Multimodal reasoning is an evolving field that integrates visual and textual data to enhance machine intelligence. Traditional artificial intelligence models excel at processing either text or images but often struggle when required to reason across both formats. Analyzing charts, graphs, mathematical symbols, and complex visual patterns alongside textual descriptions is crucial for applications in…
Microsoft AI Research Introduces MVoT: A Multimodal Framework for Integrating Visual and Verbal Reasoning in Complex Tasks
[ad_1] The study of artificial intelligence has witnessed transformative developments in reasoning and understanding complex tasks. The most innovative developments are large language models (LLMs) and multimodal large language models (MLLMs). These systems can process textual and visual data, allowing them to analyze intricate tasks. Unlike traditional approaches that base their reasoning skills on verbal…
Breaking the data bottleneck: Salesforce’s ProVision speeds multimodal AI training with image scene graphs
[ad_1] Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More As enterprises around the world double down on their AI projects, the availability of high-quality training data has become a major bottleneck. While the public web has largely been exhausted as a data source, major…