弗朗索瓦·乔莱（François Chollet）表示，他在2024年底o3测试时计算演示后改变了看法，认为LLM研究路线可实现无上限的能力扩展，“不会有墙”。但他仍预测15年后的AI不会基于LLM技术栈，而将转向其最优最终形态——符号学习。他认为当前技术在数据效率和测试时计算效率上距最优差4-6个数量级，未来AI将接近最优。
In the era of base LLM scaling (2022-2024), I believed the LLM line of research would reach a capability plateau (as later seen with base LLMs).
In late 2024, after the o3 test-time compute demo, I changed my views: the new models were showing genuine fluid intelligence, and with this new line of work, the LLM line of research could achieve unbounded capability scaling. "There will be no wall." I talked about it at length on Twitter and in a blog post.
However, looking ahead, I still do not believe that future AI (say, in 15 years) will be based on the LLM stack. I believe it will necessarily have to move closer to its optimal, final form -- symbolic learning. Obviously this is a risky and contrarian belief -- the safe bet would be LRMs. But let's see.
The only meaningful difference is efficiency, not task-specific skill. I believe current techniques are 4-6 orders of magnitude away from optimality in terms of data efficiency and test-time compute efficiency. But far future AI will be near-optimal.
The limitations of specific techniques are predictable and correspondingly lead to plateaus for those techniques. But there is always the next technique, building on top of the pile that's already available. There is enough research investment that there will be no wall.在 X 查看被引用的帖子
来源：François Chollet · x.com