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Unifying Vision and Language Models with Mohit Bansal - #636

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Manage episode 367779038 series 2355587
Content provided by TWIML and Sam Charrington. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by TWIML and Sam Charrington or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.

Today we're joined by Mohit Bansal, Parker Professor, and Director of the MURGe-Lab at UNC, Chapel Hill. In our conversation with Mohit, we explore the concept of unification in AI models, highlighting the advantages of shared knowledge and efficiency. He addresses the challenges of evaluation in generative AI, including biases and spurious correlations. Mohit introduces groundbreaking models such as UDOP and VL-T5, which achieved state-of-the-art results in various vision and language tasks while using fewer parameters. Finally, we discuss the importance of data efficiency, evaluating bias in models, and the future of multimodal models and explainability.

The complete show notes for this episode can be found at twimlai.com/go/636.

  continue reading

700 episodes

Artwork
iconShare
 
Manage episode 367779038 series 2355587
Content provided by TWIML and Sam Charrington. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by TWIML and Sam Charrington or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.

Today we're joined by Mohit Bansal, Parker Professor, and Director of the MURGe-Lab at UNC, Chapel Hill. In our conversation with Mohit, we explore the concept of unification in AI models, highlighting the advantages of shared knowledge and efficiency. He addresses the challenges of evaluation in generative AI, including biases and spurious correlations. Mohit introduces groundbreaking models such as UDOP and VL-T5, which achieved state-of-the-art results in various vision and language tasks while using fewer parameters. Finally, we discuss the importance of data efficiency, evaluating bias in models, and the future of multimodal models and explainability.

The complete show notes for this episode can be found at twimlai.com/go/636.

  continue reading

700 episodes

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