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Unifying Vision and Language Models with Mohit Bansal - #636

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Manage episode 367779038 series 2355587
Indhold leveret af TWIML and Sam Charrington. Alt podcastindhold inklusive episoder, grafik og podcastbeskrivelser uploades og leveres direkte af TWIML and Sam Charrington eller deres podcastplatformspartner. Hvis du mener, at nogen bruger dit ophavsretligt beskyttede værk uden din tilladelse, kan du følge processen beskrevet her https://da.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.

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778 episoder

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Manage episode 367779038 series 2355587
Indhold leveret af TWIML and Sam Charrington. Alt podcastindhold inklusive episoder, grafik og podcastbeskrivelser uploades og leveres direkte af TWIML and Sam Charrington eller deres podcastplatformspartner. Hvis du mener, at nogen bruger dit ophavsretligt beskyttede værk uden din tilladelse, kan du følge processen beskrevet her https://da.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

778 episoder

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