World's Fair 2025
Thinking Deeper in Gemini — Jack Rae, Google DeepMind
Overview
This talk explores the concept of "thinking" within Gemini, a new paradigm for AI models that allows for iterative computation and deeper reasoning before generating a final response. The core thesis is that by enabling models to spend more test-time compute on complex problems, we can unlock significant advancements in AI capabilities, moving beyond immediate response generation towards more profound problem-solving. This approach addresses bottlenecks in current AI by allowing for dynamic allocation of computational resources based on task difficulty.
Who should watch
- AI Engineers
- Product Managers
- Builders working on complex AI applications
- Those interested in the evolution of large language models
- Developers seeking more capable and efficient AI tools
Key takeaways
- Progress in AI has historically been driven by identifying and solving key bottlenecks, from early language models to transformers.
- A current bottleneck is the fixed test-time compute in LLMs, which limits their ability to deeply reason.
- Gemini's "thinking" stage introduces an iterative loop, allowing models to perform significantly more computation dynamically before outputting a response.
- This thinking capability is trained using reinforcement learning, enabling emergent behaviors like hypothesis testing and self-correction.
- The "thinking" paradigm allows for a continuous budget of compute, offering granular control over cost and performance, unlike discrete model size choices.
- Future developments include improving the efficiency of the thinking process, enabling deeper reasoning for extremely complex tasks (e.g., "deep think" mode), and enhancing data efficiency for models to deeply contemplate limited information.
- Increased test-time compute directly correlates with improved reasoning performance across various domains like math and coding.
- The goal is to create models that can deeply contemplate and build knowledge from limited data, akin to human mathematicians like Ramanujan.
Notable quotes
*Progress has really been marked by identifying key bottlenecks towards intelligence and then solving them.*
*The bottleneck of test time compute, this is relevant to thinking.*
*We want a model to be able to be incredibly data efficient and actually go to millions or beyond of inference tokens where the model is really building up knowledge and artifacts.*
Unofficial community note. Prefer the recording for nuance.