96 summarised stories about Multimodal Models, each linking back to the original source. Browse all topics →
SiliconANGLE 1 week ago 24 ● 7 sources
Google released EmbeddingGemma 2, expanding its open embedding model from text to shared multimodal embeddings for images, audio, and video. The model is 740 million parameters and is designed to run on a smartphone. It enables on-device retrieval like matching a voice memo to a video moment without sending data off the phone, with options to shrink embedding vectors down to 128 numbers.
MarkTechPost 1 week ago 49 ● 7 sources
Google DeepMind released EmbeddingGemma 2, an open multimodal embedding model that maps text, code, images, video, and audio into a shared vector space. It has 740M parameters and uses an 8,192-token context window. The release adds deployable weights under an Apache 2.0 license (including Edge/phone-friendly builds) and enables privacy-first, on-device search and retrieval for RAG pipelines across modalities.
Tech.eu 1 week ago 29 ● 12 sources
Mistral unveiled Mistral Large 4 (le Chonk), its largest multimodal AI model to date, and said it is aiming to match or beat top open models. The model is a one trillion-parameter system, and Mistral says it will be fully available later this month after a preview launch. Availability shifts from preview for developers to full release and wider access via Mistral’s API.
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14 October 2026
Sakana AI added a new transparency layer to Sakana Chat with “My Fish,” letting users see what the chatbot remembers, when those memories get used, and how related facts connect. The interface surfaces memory activity as small “chips” during responses and backs it up with a heatmap of memory use plus read/write history and a connection map. It’s a practical answer to a familiar problem: people don’t just want outputs from AI systems, they want a way to audit what the system is carrying forward.
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