Welcome back to our series on scaling Large Language Models (LLMs)! Following our exploration of multi-modality's impact on enhancing LLM learning, let's dive into another pivotal aspect: the role of compute.
- The Dominance of Generic Training Methods:
Picture a scenario where you're trying to optimize a complex system. Initially, you might apply specialized rules based on human understanding, akin to using a detailed map for navigation.
However, as computational power grows, a more effective approach emerges: generic training methods that leverage this compute power. It's like switching from using a map to a GPS that continuously learns and updates the best routes.
In AI, this principle holds true: Generic training methods with more compute always trump human-crafted heuristics.
- Embracing Complexity in AI Development:
A key insight from our exploration of compute is recognizing the immense complexity of human cognition. Unlike simple models of space, objects, or agents, human thought processes are deeply intricate.
In scaling LLMs, we aim not to encode these complexities directly but to develop meta-methods that enable AI to discover and navigate this complexity on its own. It’s about equipping AI to find patterns and approximations in data as humans do, rather than pre-loading it with our existing knowledge.
This approach allows AI to evolve and adapt in ways that mimic human discovery and learning.
- Real-World Example of AlphaGo to AlphaZero by Google DeepMind:
Initially, AlphaGo learned from human players, much like a student learning from a textbook. But AlphaZero changed the game. It learned by playing against itself, akin to a student who learns not from books, but by experimenting and discovering new knowledge independently.
This shift from human-guided learning to self-exploration and self-improvement showcases the power of computation in AI development.
- The Future of AI:
Envision a world where AI can not only learn from what it's been taught but can also innovate and discover new ideas, much like an artist who evolves from imitating others to creating their unique style.
This future of AI, where originality and creativity flourish, is powered by the relentless growth of computational capabilities.