# 弗朗索瓦·乔莱：未来AI将走向符号学习

- 来源：François Chollet (@fchollet)
- 发布时间：2026-08-08 02:34
- AIHOT 分数：45
- AIHOT 链接：https://aihot.virxact.com/items/cmsjakezl04cdroo5bbjhl7nw
- 原文链接：https://x.com/fchollet/status/2085796727362052241

## AI 摘要

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。

## 正文

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.

### 引用推文

> François Chollet：The limitations of specific techniques are predictable and correspondingly lead to plateaus for those techniques. But there is always the next technique, buildi...