TED: AI progress is driven by scaling, with a new focus on system two thinking enhancing performance.
TED - AI Won’t Plateau — if We Give It Time To Think | Noam Brown | TED
The speaker discusses the significant advancements in AI over the past five years, primarily driven by scaling data and computational power. Despite concerns about the limits of scaling, the speaker argues that AI will continue to progress by incorporating system two thinking, which involves more deliberate and methodical processing. This approach was demonstrated in a poker AI project where allowing the AI to think longer significantly improved its performance, akin to scaling the model size and training duration by 100,000 times. This concept is supported by similar findings in other games like chess and Go, where AI performance improved with increased thinking time. The speaker highlights a new AI model, O1, which incorporates system two thinking, allowing it to think longer for complex queries, thus opening a new dimension for AI scaling. This approach is seen as a valuable investment for solving complex problems, despite the increased cost and time per query.
Key Points:
- AI advancements are largely due to scaling data and computational power.
- System two thinking, involving deliberate processing, can significantly enhance AI performance.
- In poker AI, thinking longer improved performance as much as massive scaling of model size and training.
- Similar improvements were observed in chess and Go AI with increased thinking time.
- New AI models like O1 use system two thinking, offering a new scaling dimension for complex problem-solving.
Details:
1. 🔍 AI Progress Through Scaling
- The primary driver of AI progress over the past 5 years has been the scaling of data and computational power. This has overshadowed algorithmic advances, even though the core Transformer architecture introduced in 2017 remains foundational to current models.
- Although methodologies for training frontier AI models have remained largely unchanged since 2019, the significant factor has been the scale of data and compute resources. This scaling is crucial for achieving breakthroughs in AI capabilities.
- As an example, scaling has enabled models to process and learn from exponentially larger datasets, leading to improved performance across various tasks. This demonstrates the direct impact of scaling on AI model efficacy.
- The implications of scaling are profound, as it suggests that further advancements in AI may rely heavily on continued increases in computational resources and data availability. This trend underscores the strategic importance of investing in infrastructure to support large-scale AI research and development.
2. 💸 Cost and Concerns of Scaling AI
- Training the GPT-2 model in 2019 cost around $5,000, highlighting the significant financial resources required even at early stages of large-scale AI development.
- Over the past 5 years, AI models have dramatically increased in size and complexity, necessitating training on larger datasets, which in turn drives up costs.
- The continuous improvement in the quality and capabilities of AI models demonstrates an annual trend of increasing effectiveness, suggesting that the rising costs are justified by enhanced performance.
- Understanding the reasons behind cost increases, such as the need for more data and computational power, is crucial for strategic planning in AI development.
- Future cost projections indicate that while expenses will continue to rise, the advancements in AI capabilities may offset these costs, making strategic investment in AI development essential.
3. 🚀 Confidence in AI's Future and Personal Journey
- AI scaling concerns exist regarding the cost of training models, potentially reaching hundreds of billions to trillions of dollars, which could impact future developments.
- Despite these financial concerns, there is a strong belief that AI will continue to advance rapidly, with expectations of significant progress in the near term.
- The discussion highlights optimism that AI will not hit a plateau, suggesting ongoing innovation and breakthroughs in the field.
4. 🎲 The Poker AI Challenge
4.1. Development of Poker AI
4.2. The Poker AI Challenge Event
5. 🧠 System Thinking in AI vs Human Decision Making
5.1. AI Performance in Poker
5.2. System Thinking in AI
6. ♟️ AI Advancements in Games
6.1. AI Thinking Time
6.2. Impact of Thinking Time
6.3. Quantifying Thinking Time
6.4. Cost and Strategy Implications
7. 🔄 Introducing System Two Thinking to Language Models
- OpenAI's 'O1' models introduce system two thinking, improving decision-making for complex queries by allowing the model to take time before responding, akin to AI in strategic games like chess and poker.
- The model's slower, thoughtful processing can increase costs and time per query but offers enhanced capabilities for tackling significant problems, such as cancer treatment optimization or solar panel efficiency improvements.
- The practical impact is already evident, with researchers at leading universities leveraging O1 to save time on complex tasks, demonstrating its value beyond standard chatbot functions.
- Concrete examples include O1's use in optimizing resource allocation in renewable energy projects, showcasing real-world applications of system two thinking.
8. 📈 The Future of AI Scaling
- Despite prevailing skepticism about AI hitting a growth plateau, the speaker confidently argues for the continued potential and scalability of AI technologies. They highlight ongoing advancements that defy the notion of stagnation and foresee sustained growth in AI capabilities.
