Multi-modal and Multi-task Models (S03 E04)

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Multimodal and multitask models are machine learning models that can generalize. Multimodal models can generalize to understand different types of input, for example images and text. Multitask models can generalize their knowledge by applying what they’ve learned about one task to solve another task.

Links/Resources:
• MUM: https://blog.google/products/search/introducing-mum/
• Gato: https://www.youtube.com/watch?v=wSQJZHfAg18
• MIA: https://www.youtube.com/watch?v=L9kA8nSJdYw
• Flamingo: https://www.deepmind.com/blog/tackling-multiple-tasks-with-a-single-visual-language-model
• Flamingo explaining a funny photo: https://twitter.com/MelMitchell1/status/1522642194741538817
• Is LaMDA Sentient?: https://cajundiscordian.medium.com/is-lamda-sentient-an-interview-ea64d916d917

Chapters:
0:00 Intros
2:33 Multimodal and Multitasks Models
6:50 Deepmind's Gato: The All-Rounder Athlete
14:43 Google's MUM: The Search Assistant
18:12 Deepmind's Multimodal Interactive Agent: The Domestic Helper
22:31 Deepmind's Flamingo: Reasoning about Pictures
26:45 Why are these mind-blowing?
31:20 Machine Learning has come a looooong way
35:21 Could Flamingo be the real JARVIS?
38:56 Could MIA assist the elderly?
43:05 Multimodal AI for self driving cars
51:15 Multitask = A Shared Brain That Learns Everything
1:00:19 Could these models transcend human knowledge?
1:08:50 Breaking news: AI models are sentient
1:10:37 Is this just a local maximum or a path to AGI?
1:11:50 Outros

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