Why The Next AI Breakthroughs Will Be In Reasoning, Not Scaling

Artificial intelligence has made remarkable strides in recent years, with large language models like GPT-3 and GPT-4 capturing headlines and imaginations. These models have achieved their impressive capabilities largely through scaling - increasing model size, training data, and computational resources.

But is this really how the next wave of AI breakthroughs is likely to come from?

The Limits of Scaling

While scaling up AI models has produced amazing results, there are signs that we may be approaching diminishing returns from this approach alone. Simply making models bigger and training them on more data can only go so far.

What's needed now is for AI systems to develop more human-like reasoning abilities - to break down complex problems, show their work, and engage in step-by-step logical thinking.

"Chain of Thought" AI

OpenAI's new "01" model represents a major step in this direction. Unlike previous models that focused primarily on pattern recognition and next-token prediction, 01 is designed to engage in multi-step reasoning. It can break down complex tasks into a series of steps, showing its work along the way.

This "chain of thought" approach allows 01 to tackle problems that were previously out of reach for AI systems. For example, it has shown impressive capabilities in areas like:

• Chip and circuit design automation

• Airfoil design optimization for aerospace

• Advanced customer support automation

• Scientific research and problem-solving

• Engineering and CAD design

In each of these domains, 01 isn't just pattern matching - it's reasoning through multi-step processes in ways that more closely resemble human expert thinking.

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From 80% to 100%

One of the key advantages of this reasoning-focused approach is that it allows AI to push beyond the "80% solution" and tackle the truly difficult edge cases. As one expert noted about 01: "It makes it quite easy or easier now to get to like the prototype like 80% of the way there." But more importantly, it's also making progress on that final, crucial 20%.

For example, in customer support automation, previous AI models could handle simple, common queries but struggled with complex edge cases. 01, in contrast, has shown the ability to reason through difficult support tickets that previously required human intervention. This is pushing automated support from 70-80% effectiveness towards 95%+ in some cases.

Implications for AGI