Google DeepMind: The Podcast Gemini 2.0 and the evolution of agentic AI with Oriol Vinyals
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Dec 12, 2024 Oriol Vinyals, VP of Drastic Research and co-lead of Gemini at Google DeepMind, shares insights on the evolution of AI agents from narrow tasks to complex problem-solving. He explains the two-step training process of multimodal models, highlighting the advancements in reinforcement learning. Vinyals delves into the challenges of scaling AI capabilities, its reasoning mechanisms, and future functionalities like independent research. The conversation also touches on the implications of AI in travel planning and the exciting journey toward achieving artificial general intelligence.
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Evolution of AI Agents
- AI agents have evolved from single-task specialists to more general-purpose models.
- These new models can handle broader applications, like chatbots and multimodal interactions.
Two-Step Training Process
- Training AI models involves two steps: pre-training (imitation learning) and post-training (reinforcement learning).
- Pre-training involves imitating human-created data, while post-training refines the model's behavior based on rewards.
Frozen Weights
- After training, the AI model's weights are frozen, creating a snapshot that users interact with.
- This frozen set of weights ensures consistency and avoids further changes during user interaction.