跨国深谈← 返回←Jerry Tworek

① 他说过的话

7 条

如果 Transformer 是有利可图的,如果你可以花费更多的努力和资源来扩大 Transformer 以在下一个季度获胜,那么很难把大量的注意力和精力投入到可能在一两年内更好或重新定义该领域的事情上。
If Transformer is profitable and if you can spend more efforts and more resources scaling Transformer to win in the next quarter, it’s very hard to put at least a lot of attention and a lot of energy to work on something that will maybe better or maybe will redefine the field in a year or two.
—— Jerry Tworek · [13:28]

指向原始笔记的链接

我们可能比以往任何时候都在从经验学习上花费最多的算力,但强化学习并不是从经验学习的终点。
We probably are spending the most compute than ever on learning from experience, but reinforcement learning is not the end of learning from experience.
—— Jerry Tworek · [26:22]

指向原始笔记的链接

我认为核心自动化所设想的自动化版本,在某种程度上是赋予每个人最大水平的主观能动性。
I think the version of automation by Core Automation is about giving each human maximum level of agency in some way.
—— Jerry Tworek · [35:03]

指向原始笔记的链接

对我来说,AGI 是一个可以在没有任何人在循环中的情况下自我改进的模型。
For me, AGI is a model that can improve itself without human in the loop in any way.
—— Jerry Tworek · [38:21]

指向原始笔记的链接

人类与大语言模型的混合模式现在真的非常成功。但是没有人类的大语言模型,就不怎么行,根本不行。
The human-LLM hybrid is really, really successful right now. But LLMs without humans, not so much, not at all.
—— Jerry Tworek · [39:00]

指向原始笔记的链接

正如我提到的,我们相信 Transformer 无法进行持续学习。没有办法在 Transformer 上进行持续学习。
We believe, as I mentioned, that transformers are incapable of continual learning. There’s no way how to put continual learning on transformers.
—— Jerry Tworek · [45:21]

指向原始笔记的链接

而我在 2024 年和 2025 年初看到的是,当前的路径无法让我们到达那里。
And what I have seen there in 2024 and in early 2025 is that the current path doesn’t get us there.
—— Jerry Tworek · [39:09]

指向原始笔记的链接

② 出现在这些集

1 集

• 《Transformer 已见顶?OpenAI 与 Google 双雄离职造新架构》 — 作为联合主持

③ 他谈到的

点进去有真内容 —— 本页主要出口

Sonja · Rohan Anil · Core Automation · OpenAI · Google · Anthropic · Transformer · 强化学习 · 预训练 · 测试时学习

④ 也在聊「创业与行业」的人