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New study reveals teens are building deep bonds with AI—but at what cost?

By Daniel Reed
November 2, 2025
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Companies / Meta / Meta launches AI models for text and image generation
AI assistantsCompaniesMeta

Meta launches AI models for text and image generation

The Chameleon model family stands out for its ability to process and generate both images and text simultaneously

Alex Carter
ByAlex Carter
AI News & Big Tech Correspondent
Alex Carter writes for Aiholics, keeping readers updated on the fast-paced world of AI and Big Tech. He breaks down important news and developments from the...
- AI News & Big Tech Correspondent
Published: June 19, 2024
3 Min Read
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Meta, through its Fundamental AI Research (FAIR) team, continues to drive innovation in artificial intelligence with a significant release of research models aimed at advancing the field in collaboration with the global AI community.

In a commitment to open research, Meta has unveiled five new FAIR models, each designed to push the boundaries of AI capabilities. These include groundbreaking advancements in image-to-text and text-to-music generation, multi-token prediction for faster language model training, and AudioSeal, a pioneering technique for detecting AI-generated speech.

Central to this release is the Chameleon model family, which stands out for its ability to process and generate both images and text simultaneously. Unlike traditional models limited to single modalities, Chameleon can seamlessly integrate various combinations of text and images, offering limitless possibilities from creative captioning to entirely new scene creation.

Meta also introduces JASCO, an innovative AI model enhancing control over music generation by incorporating inputs beyond text, such as chords and beats. This advancement not only improves the quality of generated music but also provides greater versatility in creative outputs.

Another significant contribution is the AudioSeal technology, addressing the need for detecting AI-generated speech within audio recordings. This groundbreaking approach enables fast and efficient localization of AI-generated segments, crucial for real-time applications and large-scale deployments.

Additionally, Meta is tackling geographical and cultural biases in text-to-image generation systems. Through extensive annotation studies and the development of automatic evaluation indicators, Meta aims to ensure that AI-generated images are inclusive and reflective of global diversity.

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These releases underscore Meta’s commitment to responsible AI research and open collaboration, providing the global AI community with essential tools and frameworks to advance AI technologies while addressing ethical considerations.

For researchers and developers interested in exploring these advancements, Meta has made these models and tools available under various licensing agreements, fostering a community-driven approach to AI innovation.

As Meta continues to pioneer AI research through FAIR, these advancements mark a significant leap forward in the capabilities of AI systems, promising new opportunities across industries and applications worldwide.

TAGGED:AI assistantsAI ModelsChameleonMeta

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ByAlex Carter
AI News & Big Tech Correspondent
Alex Carter writes for Aiholics, keeping readers updated on the fast-paced world of AI and Big Tech. He breaks down important news and developments from the industry's top players, including OpenAI, Google, Meta, Microsoft, and NVIDIA. His goal is to present these updates in a straightforward way that’s easy to understand and genuinely helpful. What makes Alex different is that he's focused on technology that matters to real people's lives, not just for flashy headlines. He demonstrates why each news is important, answering the most important questions for readers: "Why should I care?" From major AI models to big acquisitions and new tools, Alex examines what it means for businesses, society, and end-users. When not reporting, Alex enjoys covering the trends in AI competition, tech ethics, and what's next in digital.
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Project Suncatcher explores a radical idea - scaling machine learning compute into space. By using solar-powered satellites equipped with TPUs and optical links, this moonshot aims to harness the sun's…

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