al换脸一键生成
On May 28th, 2020 OpenAI announced it’s a new monument to general AI: The GPT-3 system. It is an incredible piece of engineering: a deep neural network with 175 billion parameters. And it does play some incredible parlor tricks:
2020年5月28日,OpenAI宣布它是通用AI的新纪念碑: GPT-3系统。 这是一项令人难以置信的工程:具有1750亿个参数的深度神经网络。 它确实发挥了一些不可思议的客厅技巧:
- It can translate from English to other languages and back 它可以将英语翻译成其他语言并返回
- Given a prompt, can generate some pretty readable short stories给出提示,可以生成一些易于阅读的短篇小说
- Given a description of a user interface, it can generate the HTML web page layout给定用户界面的描述,它可以生成HTML网页布局
- Give a description of a database search, it can generate the program to query a database给出数据库搜索的描述,它可以生成查询数据库的程序
- Given some text, it can use common sense and knowledge of the real world to answer questions about the text给定一些文本,它可以使用常识和现实世界的知识来回答有关文本的问题
- It can summarize long documents可以汇总长文件
- It can emulate a real person in a chat (we are getting closer to passing the Turning Test)它可以在聊天中模拟真实的人(我们正在接近通过车削测试)
- When asked to write a short paragraph, a real person can guess if it was written by GPT-3 or a real person 52% of the time (50% is a random guess) 当要求撰写简短的段落时,真实的人可以猜出它是由GPT-3还是真实的人写的,有52%的时间(50%是随机猜测)
- It can answer simple math questions 它可以回答简单的数学问题
However, it is debatable how “smart” really is. It is very good at many language tasks that involve complex pattern matching, but it falls down on many common-sense tasks. In this blog, we will discuss if these new “generative” systems will be able to generate detailed lessons that can be customized to the need of a classroom of students or an individual student.
但是,究竟“智能”到底是什么,仍有待商bat。 它在涉及复杂模式匹配的许多语言任务中非常擅长,但在许多常识性任务中却有所下降。 在此博客中,我们将讨论这些新的“生成式”系统是否能够生成可根据学生教室或单个学生的需要进行定制的详细课程。
At the heart of GPT systems is the ability to “Generate” text. GPT stands for Generative Pre-trained Transformer. It means that the GPT-3 neural network was built using a Transformer model. The key difference between GPT-3 and its predecessor like