Google DeepMind Explores AI Advancements in Multimodal Models and Compute Efficiency
CEO Demis Hassabis discusses the development of Gemini, custom AI chips, and the long-term path toward Artificial General Intelligence.
- Google DeepMind's Gemini model emphasizes a multimodal approach, integrating text, spatial, and real-world context for broader AI applications like universal assistants and robotics.
- The company is investing in custom 'light chips' to enhance cost-efficiency and performance during AI inference, addressing growing computational demands.
- DeepMind is researching memory improvements for AI, including extended context windows and episodic memory, to enable more complex reasoning and tasks.
- Hassabis highlighted ongoing projects in material science, drug discovery, and robotics, with the potential for breakthroughs like AI-designed drugs and room-temperature superconductors.
- Google's full-stack approach, encompassing hardware, data centers, and algorithms, aims to maintain a competitive edge in the evolving AI landscape.