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Google DeepMind: The Podcast
Gemini 2.0 and the evolution of agentic AI with Oriol Vinyals
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.
49:25
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Quick takeaways
- The evolution of AI agents has transitioned from specialized models to more versatile, agentic systems capable of performing a variety of human-like tasks.
- The training of modern AI models involves a two-step process of imitation learning and reinforcement learning, enhancing performance and autonomy in task execution.
Deep dives
Evolution of AI Agents
AI agents have made significant advancements since their inception, evolving from specialized models focused on singular tasks like playing StarCraft to more broad and capable systems. Initially, the development relied heavily on reinforcement learning techniques that addressed very complex games, while now the focus has shifted to large language models and multimodal approaches that can engage in various tasks from chatting to creating content. For instance, modern agents can utilize a general training curriculum that allows them to perform across multiple environments, enhancing their applicability beyond strict game environments. This shift marks a notable transition in the capabilities of AI agents to make autonomous decisions and perform tasks that align more closely with human-like cognition.
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