gpt 3的简要说明

自然语言处理 (NATURAL LANGUAGE PROCESSING)

GPT-3 is a new tool, based on a machine-learning algorithm, which is used to predict text. It is released by OpenAI, where Elon Musk and Sam Altman are two of the founders, and both of them have been outspoken about the possibility of artificial general intelligence (AGI), as well as the opportunities and dangers that it may present our society with. Therefore, they wanted to create a company that is developing artificial intelligence in a responsible manner. OpenAI is set out to advance AI, without letting it be able to control us, and they are doing that by creating a friendly artificial intelligence.

GPT-3是一种基于机器学习算法的新工具，可用于预测文本。 它由OpenAI发布，其中Elon Musk和Sam Altman是两位创始人，并且他们都直言不讳地提出了人工智能(AGI)的可能性，以及它可能给我们的社会带来的机遇和危险。 。 因此，他们希望创建一家 以负责任的方式开发人工智能 的公司 。 OpenAI致力于在不让AI能够控制我们的情况下推进AI，他们通过创建友好的人工智能来做到这一点。

GPT-3 is a tool that is used to predict text

GPT-3是用于预测文本的工具

Saying that GPT-3 is a text predictor might not sound that impressive. However, the tool is considered a huge step towards AGI and can be used to create believable press releases, articles, books, etc.

说GPT-3是文本预测器，听起来可能并不那么令人印象深刻。 但是，该工具被认为是迈向AGI的重要一步，可用于创建令人信服的新闻稿，文章，书籍等。

Further, it is possible to interact with it, and many people have been creating software where you interact with different personas and ask them questions. GPT-3 has also been used to create strategy documents, design websites, and do arithmetic, and this is only the tip of the iceberg.

此外，还可以与之交互，并且许多人都在创建软件，您可以在其中与不同的角色交互并向他们提问。 GPT-3也已用于创建策略文档，设计网站和进行算术运算，这只是冰山一角。

The algorithm, which is based on a deep neural network, is explained in great detail in an article that OpenAI published in July. The paper compares GPT-3 towards the state of the art algorithms for specific natural language processing (NLP) tasks. The algorithm is trained only once before it is tested on the NLP tasks, which means that the weights are not being adjusted.

OpenAI在7月发表的一篇文章中对基于深度神经网络的算法进行了详细说明。 本文将GPT-3与针对特定自然语言处理(NLP)任务的最新算法进行了比较。 在对NLP任务进行测试之前，仅对该算法进行过一次训练，这意味着权重没有得到调整。

Instead of learning like a traditional machine learning algorithm through thousands of epochs, GPT-3 learns from the input and is able to adapt to other problems, not unlike the way humans learn. Due to the nature of the algorithm and the amount of data (approximately 500 billion tokens) used during training, it has been performing extremely well. The model itself has not been made accessible for developers, who rather can get access to an API.

GPT-3没有像传统的机器学习算法那样经过数千个纪元学习，而是从输入中学习并能够适应其他问题，与人类的学习方式不同。 由于算法的性质以及训练过程中使用的数据量(大约5000亿个令牌)，它一直表现非常出色。 开发人员无法访问模型本身，而开发人员可以访问API。

Developers will, with the API request, provide a couple of examples of what it would like GPT-3 to do, and then this will be used to prime the model and provide the developer with his or her requested text. Priming the model with the best possible examples is important in order to achieve desired results.

开发人员将根据API请求提供一些示例，说明其希望GPT-3执行的操作，然后将这些示例用于启动模型并向开发人员提供其所请求的文本。 为了获得所需的结果，使用尽可能好的示例对模型进行初始化很重要。

Priming the model with good examples is important in order to achieve desired results

为了获得理想的结果，为模型提供良好的示例很重要

GPT-3 has generated a large amount of hype with good reason. However, there are still challenges that have to be solved to make sure it is not being misused. The algorithm is guessing the next word or words by looking at a massive amount of documents. OpenAI has to figure out a way to teach it the social norms we want it to inherit versus what they read in the documents. Hence, a safety net should be included to e.g. filter out racism, sexism, etc.

GPT-3有充分的理由引起了大肆宣传。 但是，仍然需要解决一些挑战，以确保它不会被滥用。 该算法通过查看大量文档来猜测下一个或多个单词。 OpenAI必须找出一种方法来教导我们要继承的社会规范以及他们在文档中阅读的内容。 因此，应包括一个安全网，例如过滤掉种族主义，性别歧视等。

[1] Frank Chen and Sonal Chokshi. 16 Minutes on News #37: GPT-3, Beyond the Hype (Jul. 2020).

[1] Frank Chen和Sonal Chokshi。 新闻＃16上的16分钟： GPT-3，超越炒作(2020年7月) 。

[2] Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Amanda Askell, Rewon Child, Prafulla Dhariwal, Sandhini Agarwal, Aditya Ramesh, Arvind Neelakantan, Ariel Herbert-Voss, Daniel M. Ziegler, Pranav Shyam, Gretchen Krueger, Jeffrey Wu, Mateusz Litwin, Girish Sastry, Tom Henighan, Clemens Winter, Scott Gray, Christopher Hesse, Benjamin Chess, Sam McCandlish, Mark Chen, Eric Sigler, Jack Clark, Christopher Berner, Alec Radford, Ilya Sutskever, and Dario Amodei. Language Models are Few-Shot Learners (Jul. 2020).

[2]汤姆·B·布朗，本杰明·曼恩，尼克·赖德，梅兰妮·斯比亚比亚，贾里德·卡普兰，阿曼达·阿斯凯尔，瑞云·儿童，普拉弗拉·达里瓦尔，桑迪尼·阿加瓦尔，阿迪亚·拉梅什，阿文德·内拉坎丹，阿里尔·赫伯特·沃斯，丹尼尔·齐格勒，普兰纳夫·夏雅姆，格蕾琴·克鲁格(Gretchen Krueger)，杰弗里·吴(Jeffrey Wu)，马特乌斯·里特温(Mateusz Litwin)，吉里斯·赛斯特里(Girish Sastry)，汤姆·海尼根(Tom Henighan)，克莱门斯·温特(Clemens Winter)，斯科特·格雷(Christopher Hesse)，本杰明·切斯(Benjamin Chess)，萨姆·麦坎德利什(Sam McCandlish)，马克·陈(Mark Chen)，埃里克·西格勒(Eric Sigler)，杰克·克拉克(Jack Clark)，克里斯托弗·伯纳尔(Christopher Berner)，亚历克·拉德福德(Alec Radford)，伊利亚·萨茨克沃(Ilya Sutskever)达里奥·阿莫迪(Dario Amodei)。 语言模型是少数学习者(2020年7月) 。