PA

Paradigm

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所以我们不是范式转换的科学家。那不是我们做的。我们是范式转换的系统工程师。
So we’re not the paradigm shifting scientists. That’s not what we do. We’re paradigm shifting system engineers.
—— Astro Teller · [18:30]

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它们挣扎是因为训练它们的范式没有优先考虑这一点。
They struggle because the paradigm for training them hasn’t prioritized that.
—— Zubin Gharemani · [21:10]

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所以所有这些都是真的不像是自然地从语言建模范式中产生出来的能力。
So all of these are capabilities that really don’t feel like they’re falling naturally out of the language modeling paradigm.
—— Justin Johnson · [01:54]

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并不清楚的是,用新架构和新范式预训练一个新模型的第一个周期,第一个周期就奏效是疯狂的。
It’s not clear that the first cycle of pre-training a new model with a new architecture and a new paradigm, the first cycle of that working is insane.
—— Justin Johnson · [22:32]

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我们想把算法范式的更替从每九年一次,缩短到每 12 个月一次。
We want to move from shifting the algorithmic paradigm every nine years to doing it every 12 months.
—— Alexander Whedon · [49:15]

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而如果你在现有范式中投入了大量沉没成本,从零开始创造一个新东西是非常痛苦的。
And if you have a lot of sunk cost in that existing paradigm, it’s very painful to create a new thing from scratch.
—— Roman Ugarte · [35:05]

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我们将迎来范式转变,从「我就在我笔记本上做点东西玩玩」到「嘿,这会如何影响整个组织?」
We’re going to have the paradigm shift from, I’m just going to do things on my laptop and have fun, to, hey, how does this affect the entire organization?
—— Jason · [18:31]

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所以我认为我们会进入这样一个世界:你不会对所有事情都用最聪明的模型——而这正是过去几年的范式。
So I think we’re going to get to a world where you’re not always using the smartest model for everything, which has kind of been the paradigm for the last couple of years.
—— 嘉宾 · [58:35]

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用大模型来调度一支更小模型的舰队,我认为这是我们相当坚定信奉的范式,它已经被证明是编码系统的运作方式,而且我认为在未来一年它会进入几乎每一个智能体。
Over a fleet of smaller models is I think a paradigm that we believe in quite firmly and it’s already proving out to be how coding systems will work and I think it’ll find its way into almost every agent over the coming year.
—— Hamad Bashir · [24:29]

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这个范式令人兴奋的地方在于,大部分算力是在所有产品表面之间共享的,所以你不必为每个产品表面从头训练单独的模型。
The exciting thing about this paradigm is most of the compute is shared across all of the product surfaces, so you don’t have to train individual models from scratch for every product surface.
—— Devansh Tandon · [09:24]

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每一个深科技范式转变都需要一个杀手级应用,光子学在科学方面已经酝酿了 20 多年、30 多年。数据中心和 AI 的能耗就是那个杀手级应用。
Every deep deck paradigm shift needs a killer app for photonics more than 20 years in the making, 30 years in the making on the science side. Energy consumption by data centers and AI is that killer app.
—— Rorik · [08:09]

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所以对数据公司来说,我认为最需要擅长的事情就是转型,也就是从一个范式转到下一个范式。
So for data companies, I think the main thing to get really good at is pivoting, like going from each paradigm to the next.
—— Yash · [51:43]

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如果我只是让 AI 放任自流,不加任何控制或担忧。我认为我们几乎必死无疑。当前的范式不可能通向一个好结局。
If I just let the AI rip without any control or worry about it. I think we’re almost certainly dead. There is no way that the current paradigm is going to lead to a good place.
—— Jaan Tallinn · [06:37]

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而当前的抢跑——我认为当前这种培育 AI 的范式中真正大的问题在于,我们不是在建造它们,我们是在培育它们——是我们某种程度上在重演进化。
And the current rushing ahead, I think the really big kind of problem with the current paradigm of how we grow AIs and we’re not building them, we’re growing them. is that we are in some ways kind of recapitulating the evolution.
—— Jaan Tallinn · [11:18]

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