DeepLearning.AI

Multimodal AI Takes Off: Multimodal Models, such as CLIP and DALL·E, are taking over AI.

While models like GPT-3 and EfficientNet, which work on text and images respectively, are responsible for some of deep learning’s highest-profile successes, approaches that find relationships between text and images made impressive

UC San Diego

Multimodal AI Takes Off: Multimodal Models, such as CLIP and DALL·E, are taking over AI.

While models like GPT-3 and EfficientNet, which work on text and images respectively, are responsible for some of deep learning’s highest-profile successes, approaches that find relationships between text and images made impressive

UC San Diego

Facing Failure to Generalize: Why some AI models exhibit underspecification.

The same models trained on the same data may show the same performance in the lab, and yet respond very differently to data they haven’t seen before. New work finds this inconsistency to be pervasive.

UC San Diego

Facing Failure to Generalize: Why some AI models exhibit underspecification.

The same models trained on the same data may show the same performance in the lab, and yet respond very differently to data they haven’t seen before. New work finds this inconsistency to be pervasive.

UC San Diego

Multimodal AI Takes Off: Multimodal Models, such as CLIP and DALL·E, are taking over AI.

Multimodal AI Takes Off: Multimodal Models, such as CLIP and DALL·E, are taking over AI.

Facing Failure to Generalize: Why some AI models exhibit underspecification.

Facing Failure to Generalize: Why some AI models exhibit underspecification.

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