评论
打印
All the same, the idea of LLMs as repositories of meaning that are then recombined does align with some assertions from 20th-century philosophy about the way humans think, experience the world, and create art.
尽管如此，将大型语言模型视为意义的储存库，然后再进行重组的观点，确实与20世纪哲学中关于人类思考、体验世界和创造艺术的方式的一些断言相一致。
The French philosopher Jacques Derrida, building on the work of linguist Ferdinand de Saussure, suggested that meaning was differential – the meaning of each word depends on that of other words.
法国哲学家雅克·德里达在语言学家费迪南·德·索绪尔的研究基础上提出，意义是有差异的——每个词的意义取决于其他词的意义。
Think of a dictionary: the meaning of words can only ever be explained by other words, which in turn can only ever be explained by other words.
想想字典：词语的含义只能用其他词语来解释，而这些词语又只能用其他词语来解释。
What is always missing is some sort of “objective” meaning outside this neverending chain of signification that brings it to a halt.
总是缺少的是某种“客观”意义，这种意义存在于这个永无止境的符号链之外，使其停止。
We are instead forever stuck in this loop of difference.
我们反而永远被困在这种差异的循环中。
Some, like Russian literary scholar Vladimir Propp, theorised that you could break down folklore narratives into constituent structural elements, as per his seminal work, Morphology of the Folktale.
一些人，如俄罗斯文学学者弗拉基米尔·普罗普，根据他的开创性著作《民间故事形态学》，提出理论认为你可以将民间故事叙述分解为组成结构元素。
Of course, this doesn't apply to all narratives, but you can see how you might combine units of a story – a starting action, a crisis, a resolution and so on – to then create a story about a sentient cloud.
当然，这并不适用于所有的叙述，但你可以看到你如何将故事的单元——一个起始动作、一个危机、一个解决方案等等——组合起来，然后创造一个关于一朵有感知的云的故事。
Today, AI can take previously unconnected, even random things, such as the skyline of Toronto and the style of the impressionists, and join them to create what hasn't existed before.
今天，人工智能可以将以前不相关的，甚至是随机的事物，比如多伦多的天际线和印象派的风格，结合起来创造出以前不存在的东西。
But there is a sort of discomforting or unnerving implication here.
但是这里有一种令人不安或令人不安的暗示。
Isn't that also, in a way, how we think?
难道那在某种程度上不也是我们思考的方式吗？
Rapha?l Millière, an assistant professor at Macquarie University in Sydney, says that, for example, we know what a pet is (a creature we keep with us at home) and we also know what a fish is (an animal that swims in large water bodies); we combine those two in a way that keeps some characteristics and discards others to form a novel concept: a pet fish.
拉斐尔·米利埃是悉尼麦考瑞大学的助理教授，他说，例如，我们知道什么是宠物（一种我们养在家里的生物），我们也知道什么是鱼（一种在大水体中游泳的动物）；我们以一种保留某些特征并丢弃其他特征的方式将这两者结合起来，形成一个新颖的概念：宠物鱼。
Newer AI models boast this capacity to amalgamate into the ostensibly new – and it is precisely why they are called “generative.”
较新的人工智能模型拥有这种融合到表面上全新的能力——这正是它们被称为“生成式”的原因。
Even comparatively sophisticated arguments can be seen to work this way.
即使是相对复杂的论点也可以被视为以这种方式起作用。
The problem of theodicy has been a topic of debate among theologians for centuries.
神正论的问题几个世纪以来一直是神学家们争论的话题。
It asks: if an absolutely good God is omniscient, omnipotent and omnipresent, how can evil exist when God knows it will happen and can stop it?
它问道：如果一个绝对善良的上帝是无所不知、无所不能和无所不在的，那么当上帝知道邪恶会发生并且可以阻止它时，邪恶怎么可能存在呢？
It radically oversimplifies the theological issue, but theodicy, too, is in some ways a kind of logical puzzle, a pattern of ideas that can be recombined in particular ways.
它从根本上简化了神学问题，但神正论在某些方面也是一种逻辑谜题，是一种可以以特定方式重新组合的思想模式。
I don't mean to say that AI can solve our deepest epistemological or philosophical questions, but it does suggest that the line between thinking beings and pattern recognition machines is not quite as hard and bright as we may have hoped.
我并不是说人工智能可以解决我们最深层次的认识论或哲学问题，但它确实表明，有思想的生物和模式识别机器之间的界限并不像我们所希望的那样清晰和明确。
