从这个角度看, chatGPT就是一个 an intelligent world model, 这是人类历史上第一次做出来。 未来chatGPT的构架应该是这样的: 底层是,各种数据库+ search engine ,为Chat提供具体的数据,实时的数据。 中间层, 以world model为核心提供intelligent服务 , 就是ChatGPT (主要价值就是,逻辑, 推理)。 各种plugin,把...
Machine Learning, and the Training of Neural Nets · The Practice and Lore of Neural Net Training · “Surely a Network That’s Big Enough Can Do Anything!”· The Concept of Embeddings · Inside ChatGPT · The Training of ChatGPT · Beyond Basic Training · What Really Lets ChatGPT Work...
If writing is a core part of your job, don’t use tools like ChatGPT, particularly if you submit your work to clients. As a professional writer with over 15 years of experience, my suggestion is simple: avoid using generative AI tools. Whether you’re a journalist, freelancer, business o...
从根本上讲,ChatGPT就是一个庞大的神经网络(GPT3拥有1750亿个权重),是一个专门为处理语言而设置的神经网络。它最显著的特点是Transfomer神经网络。 (查看原文) Aaron2赞2023-09-02 01:56:34 —— 引自章节:ChatGPT 的内部原理 / 64 构建ChatGPT的一个关键思路是,在“被动阅读”互联网内容之后添加一步:让人...
而现在我们看到像ChatGPT这样的程序能够完成这些任务,我们往往会突然认为计算机一定变得非常强大——特别是在超过它们已经基本能够完成的事情(如逐步计算像元胞自动机这样的计算系统的行为)。 But this isn’t the right conclusion to draw. Computationally irreducible processes are still computationally irreducible, ...
而在处理类似于人类语言的事物时,像ChatGPT这样的神经网络在“回顾序列”上的关注似乎也很有用。 But an important feature of neural nets is that—like computers in general—they’re ultimately just dealing with data. And current neural nets—with current approaches to neural net training—specifically ...
根据 “ChatGPT uses a much bigger database(数据库) for training. It uses stronger software(软件) and hardware(硬件) to learn things by itself."可知,ChatGPT不仅有一个训练数据库,而且还可以自学,对应 bc两个点。故选C。 【55题详解】 细节理解题。根据 “Since the robot is trained using words...
Artificial intelligence (AI) is transforming the way we interact with technology, and among the various AI applications, ChatGPT stands out as a versatile
也发布在:https://blog.laisky.com/p/what-is-gpt/GPT 的横空出世引起了人类的普遍关注,Stephen Wolfram 的这篇文章深入浅出地讲解了人类语言模型和神经网络的历史进展,深度剖析了 ChatGPT 的底层原理,讲述 GPT 的能力和局限。本文不仅仅是笔记,也有一些我自己的思考和补充材料。notes:https://laisky.notion.sit...
ChatGPT effectively does something like this, except that (as I’ll explain) it doesn’t look at literal text; it looks for things that in a certain sense “match in meaning”. But the end result is that it produces a ranked list of words that might follow, together with ...