What is ChatGPT And How Can You Use It? （英译中）
This is what ChatGPT is and why it may be the most important tool since modern search engines
这篇文章关于ChatGPT是什么以及为什么它可能是从现代搜索软件产生以来最重要的工具。
OpenAI introduced a long-form question-answering AI called ChatGPT that answers complex questions conversationally.
OpenAI 引进了一种长篇解答问题的AI，叫做ChatGPT，它能够以一种对话的形式回答问题。
It’s a revolutionary technology because it’s trained to learn what humans mean when they ask a question
它是一种革命性的技术，因为它被训练去学习当人类问问题的时候是什么意思。
Many users are awed at its ability to provide human-quality responses, inspiring the feeling that it may eventually have the power to disrupt how humans interact with computers and change how information is retrieved.
许多用户惊叹于它能够提供人性的回答的能力，激发了一种它可能有中断人类如何和电脑互动的方式并且改变数据的检索方式。
What Is ChatGPT?
什么是ChatGPT？
ChatGPT is a large language model chatbot developed by OpenAI based on GPT-3.5.
ChatGPT是在GPT-3.5的基础上，由OpenAI研制的一个巨大的语言模型机器人。
It has a remarkable ability to interact in conversational dialogue form and provide responses that can appear surprisingly human.
它有一种能够在口语交谈形式下互动的非凡能力，而且能够提供一种惊人的像是人类的回应。
Large language models perform the task of predicting the next word in a series of words.
巨大的语言模型可以运转能够在一系列词中预测下一个词的任务。
Reinforcement Learning with Human Feedback (RLHF) is an additional layer of training that uses human feedback to help ChatGPT learn the ability to follow directions and generate responses that are satisfactory to humans.
用人类反馈以增强学习是通过用人类反馈来帮助ChatGPT学习去遵循方向以及生成使人类满意的答案的一种额外的训练。
Who Built ChatGPT?
谁制造了ChatGPT？
ChatGPT was created by San Francisco-based artificial intelligence company OpenAI.
ChatGPT由总部位于旧金山的人工智能公司OpenAI所创造。
OpenAI Inc. is the non-profit parent company of the for-profit OpenAI LP.
OpenAI股份有限公司是盈利公司OpenAI LP的非盈利母公司。
OpenAI is famous for its well-known DALL·E, a deep-learning model that generates images from text instructions called prompts.
OpenAI以DALL·E而著名，它是一种深度学习的模型，能够从被称为提示的文本指令中生成图像
The CEO is Sam Altman, who previously was president of Y CombinatorMicrosoft is a partner and investor in the amount of $1 billion dollars.
Sam Altman是它的CEO，他以前是Y CombinatorMicrosoft的总裁，现在是合伙人以及投资10亿的投资人。
They jointly developed the Azure AI Platform.
他们合作开发了 Azure AI Platform.
Large Language Models
大型语言模型
ChatGPT is a large language model (LLM).
ChatGPT是一个大型的语言模型。
Large Language Models (LLMs) are trained with massive amounts of data to accurately predict what word comes next in a sentence.
大型语言模型被大量的数据训练以准确预测一句话中下一个词。
It was discovered that increasing the amount of data increased the ability of the language models to do more.
它被发现增加数据总量会增强语言模型做更多事情的能力。
According to Stanford University:
根据斯坦福大学
“GPT-3 has 175 billion parameters and was trained on 570 gigabytes of text.
GPT-3有1750亿的参数，还被570千兆字节的文本训练。
For comparison, its predecessor, GPT-2, was over 100 times smaller at 1.5 billion parameters.
作为比较，它的前一代，GPT-2，只有15亿参数，别它小100倍。
This increase in scale drastically changes the behavior of the model — GPT-3 is able to perform tasks it was not explicitly trained on, like translating sentences from English to French, with few to no training examples.
这种规模上的增涨大大地改变了模型的性能——GPT-3能够执行它没有明确训练过的任务，比如在很少甚至没有训练模型的情况下，把英语句子翻译成法语。
This behavior was mostly absent in GPT-2.
GPT-2几乎没有这种性能。
Furthermore, for some tasks, GPT-3 outperforms models that were explicitly trained to solve those tasks, although in other tasks it falls short.”
与此同时，虽然GPT-3在别的任务上表现不好，但对于某些任务，则表现优于某些已经明确训练解决那些任务的模型。
LLMs predict the next word in a series of words in a sentence and the next sentences – kind of like autocomplete, but at a mind-bending scale.
LLMs在一句话的一系列词语中预测下一个词和下一个句子——有点像自动完成，却是一个令人兴奋（难以理解？）的规模。
This ability allows them to write paragraphs and entire pages of content.
这种能力是他们能够写文章和整面的内容。
But LLMs are limited in that they don’t always understand exactly what a human wants.
但是LLMs是有局限的他们不能一直明白人类到底想要什么。
And that’s where ChatGPT improves on state of the art, with the aforementioned Reinforcement Learning with Human Feedback (RLHF) training.
这就是ChatGPT利用上述的用人类反馈以增强学习的训练来提高技术的地方。
How Was ChatGPT Trained?
ChatGPT是如何训练的？
GPT-3.5 was trained on massive amounts of data about code and information from the internet, including sources like Reddit discussions, to help ChatGPT learn dialogue and attain a human style of responding.
GPT-3.5由大量包括密码和来自网络的信息的数据所训练，包括来自新闻网站的讨论，以帮助Chat GPT学习对话和得到一种人类风格的回应。
ChatGPT was also trained using human feedback (a technique called Reinforcement Learning with Human Feedback) so that the AI learned what humans expected when they asked a question.
Chat GPT也被训练使用人类反哭（一种叫做用人类反馈以增强学习的技术）所以当被问问题时，AI学习到什么是人类期待的。
Training the LLM this way is revolutionary because it goes beyond simply training the LLM to predict the next word.
这样训练LLM是革命性的，因为它超越了仅仅是训练LLM去预测下一个词。
A March 2022 research paper titled Training Language Models to Follow Instructions with Human Feedback explains why this is a breakthrough approach:
2022年3月，一个以训练语言模型利用人类反馈去遵循指令为题的研究报告解释了为何这是一个突破性的进步：
“This work is motivated by our aim to increase the positive impact of large language models by training them to do what a given set of humans want them to do.
这项工作是受我们通过训练它们去做一组特定人群想让它们做的来增加大型语言模型积极影响的目标的启发。
By default, language models optimize the next word prediction objective, which is only a proxy for what we want these models to do.
默认情况下，语言模型优化预测下一个单词的目标，这仅仅是我们希望这些模型去做的一种代替品。
Our results indicate that our techniques hold promise for making language models more helpful, truthful, and harmless.
我们的结果说明我们的技术有希望是语言模型跟有用，更真实，更无恶意的。
Making language models bigger does not inherently make them better at following a user’s intent.
使语言模型更大并没有内在地使它们更好地遵循用户意图。
For example, large language models can generate outputs that are untruthful, toxic, or simply not helpful to the user.
比如说，大型语言模型会生成不实的、有毒的、或者仅仅是对用户无用的信息。
In other words, these models are not aligned with their users.”
换句话说，这些模型与用户（的需求）并不一致。
The engineers who built ChatGPT hired contractors (called labelers) to rate the outputs of the two systems, GPT-3 and the new InstructGPT (a “sibling model” of ChatGPT).
构建ChatGPT的工程师们雇佣了承包商（所谓的标价机）去评估两个系统的输出，GPT-3和新的InstructGPT（一个ChatGPT的同胞模型）。
Based on the ratings, the researchers came to the following conclusions:
在评估的基础上，研究者们得出以下结论：
“Labelers significantly prefer InstructGPT outputs over outputs from GPT-3.
“标价机们相比于GPT-3的输出，明显地倾向于Instruct GPT的输出。
InstructGPT models show improvements in truthfulness over GPT-3.
InstructGPT模型在真实层面比GPT-3有所提高。
InstructGPT shows small improvements in toxicity over GPT-3, but not bias.”
InstructGPT相比于GPT-3在有毒？方面有小小的进步，但不是偏向性的。
The research paper concludes that the results for InstructGPT were positive.
这篇研究报道总结了InstructGPT的影响是有利的这一结论。
Still, it also noted that there was room for improvement.
尽管如此，研究还指出它们还有一些进步空间。
“Overall, our results indicate that fine-tuning large language models using human preferences significantly improves their behavior on a wide range of tasks, though much work remains to be done to improve their safety and reliability.”
总之，我们的结果说明那些显著地利用人类倾向微调的大型语言模型，在一系列的任务中提高了它们的性能，即使对于提升它们的安全性和可靠性还有许多工作要做。
What sets ChatGPT apart from a simple chatbot is that it was specifically trained to understand the human intent in a question and provide helpful, truthful, and harmless answers.
使ChatGPT有别于一个简单聊天机器人的是它是为明白人类在问题中的内容并且提供有用的、可信的和无恶意的回答而特别训练的。
Because of that training, ChatGPT may challenge certain questions and discard parts of the question that don’t make sense.
因为那种训练，ChatGPT可能挑战具体问题并且丢弃那些问题没用的部分。
Another research paper related to ChatGPT shows how they trained the AI to predict what humans preferred.
另一篇有关ChatGPT发研究报道指出他们如何训练AI去预测人类所倾向的东西。
The researchers noticed that the metrics used to rate the outputs of natural language processing AI resulted in machines that scored well on the metrics, but didn’t align with what humans expected.
研究者注意到，用来评估自然语言处理AI的指标导致机器在指标上评分很高，但与人类预期不一致。
The following is how the researchers explained the problem:
下面是研究人员如何解释这个问题：
“Many machine learning applications optimize simple metrics which are only rough proxies for what the designer intends.
许多机器学习应用程序充分利用简单的指标，它们只是设计者意图的粗略代理（？）
This can lead to problems, such as YouTube recommendations promoting click-bait.”
这会导致问题，比如YouTube建议推广“点击诱饵”。
So the solution they designed was to create an AI that could output answers optimized to what humans preferred.
所以他们设计的解决方法是去创造一个能够根据人类偏好优化答案的AI。
To do that, they trained the AI using datasets of human comparisons between different answers so that the machine became better at predicting what humans judged to be satisfactory answers.
为了做到它，他们训练用人类在不同答案比较的数据集训练AI，以至于机器变得被擅长于预测被人类评价为满意的答案。
The paper shares that training was done by summarizing Reddit posts and also tested on summarizing news.
论文呢分享道训练已经通过总结新闻网站的帖子完成，并且仍然在总结新闻反面测试。
The research paper from February 2022 is called Learning to Summarize from Human Feedback.
这篇2022年2月的研究论文题目为从人类反馈中学习总结。
The researchers write:
研究表示：
“In this work, we show that it is possible to significantly improve summary quality by training a model to optimize for human preferences.
在这项工作中，我们证明通过训练一个模型去根据人类的偏好优化是有可能显著提高总结的品质的。
We collect a large, high-quality dataset of human comparisons between summaries, train a model to predict the human-preferred summary, and use that model as a reward function to fine-tune a summarization policy using reinforcement learning.”
我们收集了一个巨大的、高品质的人类在总结中比较的数据集，训练一个可以去预测人类倾向总结的模型，并且把那个模型作为一个奖励功能通过加固设备学习去微调一个总结原则。
What are the Limitations of ChatGPT?
ChatGPT的局限是什么？
Limitations on Toxic Response
毒性反应的局限
ChatGPT is specifically programmed not to provide toxic or harmful responses.
ChatGPT被特别设计成不提供有毒或者有害的回应的。
So it will avoid answering those kinds of questions.
所以它会避免回答这类问题。
Quality of Answers Depends on Quality of Directions
回答的质量依赖于指向的质量
An important limitation of ChatGPT is that the quality of the output depends on the quality of the input.
ChatGPT一个很大的限制是输出的质量有赖于输入的质量。
In other words, expert directions (prompts) generate better answers.
换句话说，专业的指向（提示）生成更好的答案。
Answers Are Not Always Correct
回答不总是正确
Another limitation is that because it is trained to provide answers that feel right to humans, the answers can trick humans that the output is correct.
因为它被训练成提供人类感觉正确的答案，所以另一个局限是这些答案会引起人类输出是正确的错觉。
Many users discovered that ChatGPT can provide incorrect answers, including some that are wildly incorrect.
许多用户发现ChatGPT会提供不正确的答案，包括一些极其错误的回答。
The moderators at the coding Q&A website Stack Overflow may have discovered an unintended consequence of answers that feel right to humans.
编码网站的负责人可能已经发现非预期的人类感觉正确的结果
Stack Overflow was flooded with user responses generated from ChatGPT that appeared to be correct, but a great many were wrong answers.
编码网站已经充斥着用户反应的ChatGPT生成的看起来正确但是许多是错误的答案
The thousands of answers overwhelmed the volunteer moderator team, prompting the administrators to enact a ban against any users who post answers generated from ChatGPT.
数以千计的回答压倒了志愿仲裁团队，促使管理员通过禁令，禁止上传从ChatGPT上生成的回答。
The flood of ChatGPT answers resulted in a post entitled: Temporary policy: ChatGPT is banned:
许多的来自ChatGPT的回答导致（？）一个名为临时政策：ChatGPT被禁了。
“This is a temporary policy intended to slow down the influx of answers and other content created with ChatGPT.
为了减慢回答的大量涌入和其他由ChatGPT生成的内容，这是一个临时政策。
…The primary problem is that while the answers which ChatGPT produces have a high rate of being incorrect, they typically “look like” they “might” be good…”
最初的问题是当ChatGPT的回答有很高错误率时，它们通常看起来它们很好。
The experience of Stack Overflow moderators with wrong ChatGPT answers that look right is something that OpenAI, the makers of ChatGPT, are aware of and warned about in their announcement of the new technology.
Stack Overflow仲裁者吗和ChatGPT看起来正确的错误答案的经历是OpenAI，ChatGPT的开发者，正意识到并且在它们新技术的公布中所警惕的。
OpenAI Explains Limitations of ChatGPT
OPenAI解释ChatGPT的局限性
The OpenAI announcement offered this caveat:
OpenAI的公告提出了这份警告
ChatGPT sometimes writes plausible-sounding but incorrect or nonsensical answers.
Fixing this issue is challenging, as:
ChatGPT有时写出貌似合理的但是错误或者无意义的回答。
during RL training, there’s currently no source of truth;
在RL的训练中，目前没有真相来源
(2) training the model to be more cautious causes it to decline questions that it can answer correctly; and
训练模型更小心使得它减少了回答能回答正确的问题的数量。
(3) supervised training misleads the model because the ideal answer depends on what the model knows, rather than what the human demonstrator knows.”
监督训练误导这个模型，因为最理想的模型有赖于模型知道什么，而不是演示者知道的。
Is ChatGPT Free To Use?
ChatGPT能免费使用吗
The use of ChatGPT is currently free during the “research preview” time
在目前研究预览时间，ChatGPT是免费使用的。
The chatbot is currently open for users to try out and provide feedback on the responses so that the AI can become better at answering questions and to learn from its mistakes.
聊天机器人现在对用户开放以尝试并提供对答复的反馈，这样以后AI可以更擅长于回答问题并从错误中学习。
The official announcement states that OpenAI is eager to receive feedback about the mistakes:
官方声明称OpenAI正期待从错误中获得反馈：
“While we’ve made efforts to make the model refuse inappropriate requests, it will sometimes respond to harmful instructions or exhibit biased behavior.
当我们已经努力使模型拒绝不合适的请求时，它有时将会回应有害指令或表现出有偏见的行为。
We’re using the Moderation API to warn or block certain types of unsafe content, but we expect it to have some false negatives and positives for now.
我们正用Moderation API去警告或锁住某些类型的不安全内容，但是我们现在希望它能有一些误报漏报。
We’re eager to collect user feedback to aid our ongoing work to improve this system.”
我们希望收集用户的反馈去帮助我们继续接下来提升这个系统的工作。
There is currently a contest with a prize of $500 in ChatGPT credits to encourage the public to rate the responses.
目前有一个500美元的ChatGPA的比赛以鼓励公众去评估这些反应。
“Users are encouraged to provide feedback on problematic model outputs through the UI, as well as on false positives/negatives from the external content filter which is also part of the interface.
用户被鼓励去通过UI对有问题的模型输出提供人类反馈，同时关于来自漏报误报从是用户界面的一部分的外部过滤器
We are particularly interested in feedback regarding harmful outputs that could occur in real-world, non-adversarial conditions, as well as feedback that helps us uncover and understand novel risks and possible mitigations.
我们特别感兴趣的是关于发生在有害输出的现实世界，不敌对的条件下的反馈和能够帮助我们发现和理解新的风险和可能的解决办法的反馈。
You can choose to enter the ChatGPT Feedback Contest3 for a chance to win up to $500 in API credits.
你可以选择进入ChatGPT 反馈测试3，有机会赢得500美元在API的信用里。
Entries can be submitted via the feedback form that is linked in the ChatGPT interface.”
可以通过ChatGPT界面中链接的反馈表单提交条目
The currently ongoing contest ends at 11:59 p.m. PST on December 31, 2022.
目前持续的测试将在2020年12月31日11:59 p.m. PST 结束。.
Related: OpenAI May Introduce A Paid Pro Version Of ChatGPT
相关：OpenAI可能推出ChatGPT付费专业版
Will Language Models Replace Google Search?
语言模型会替代Google搜索吗？
Google itself has already created an AI chatbot that is called LaMDA.
Google已经开发了一个叫LaMDA的AI聊天机器人
The performance of Google’s chatbot was so close to a human conversation that a Google engineer claimed that LaMDA was sentient.
Google的聊天机器人的表现很接近一个人的对话，这使得一个Google工程师声称LaMDA是有知觉能力的。
Given how these large language models can answer so many questions, is it far-fetched that a company like OpenAI, Google, or Microsoft would one day replace traditional search with an AI chatbot?
鉴于这些大型语言模型如何回答如此多的问题，像OpenAI、谷歌或微软这样的公司有朝一日会用AI聊天机器人取代传统搜索，这是否牵强？
Some on Twitter are already declaring that ChatGPT will be the next Google.
有些人在推特上已经宣称ChatGPT将会是下一个Google
ChatGPT is the new Google.
ChatGPT是新的Google
— Angela Yu (@yu_angela) December 5, 2022
The scenario that a question-and-answer chatbot may one day replace Google is frightening to those who make a living as search marketing professionals.
有关于一个问答机器人将有一天取代Google发语言对那些以搜索营销专业人士为生的人来说是可怕的。
It has sparked discussions in online search marketing communities, like the popular Facebook SEOSignals Lab where someone asked if searches might move away from search engines and towards chatbots.
它已经在网络搜索市场界引发了讨论，例如有人在在流行的Facebook SEOSignals Lab上问道是否研究可能把搜索引擎改变为聊天机器人。
Having tested ChatGPT, I have to agree that the fear of search being replaced with a chatbot is not unfounded.
已经测试过ChatGPT之后，我不得不同意搜索被聊天机器人代替的恐惧是无根据。
The technology still has a long way to go, but it’s possible to envision a hybrid search and chatbot future for search.
这种技术还有很长的路要走，但是它有可能展望一种混合搜索和聊天机器人的未来去研究。
But the current implementation of ChatGPT seems to be a tool that, at some point, will require the purchase of credits to use.
但是目前ChatGPT非使用看起来像是一种工具，从某些时候，将要用信用卡支付使用。
How Can ChatGPT Be Used?
ChatGPT要如何使用？
ChatGPT can write code, poems, songs, and even short stories in the style of a specific author.
ChatGPT可以写代码、诗歌、歌曲，甚至以特定作者的风格写一个短故事。
The expertise in following directions elevates ChatGPT from an information source to a tool that can be asked to accomplish a task.
以下方面的专业知识把ChatGPT从一个信息资源提升为一个可以完成任务的工具。
This makes it useful for writing an essay on virtually any topic.
这使得它对于写几乎关于任何话题的小说都很有用。
ChatGPT can function as a tool for generating outlines for articles or even entire novels.
ChatGPT可以起到用于生成文章大纲甚至整篇小说的工具的作用。
It will provide a response for virtually any task that can be answered with written text.
实际上，它将给任何可以用书面文本回答的任务提供回应。
Conclusion
总结
As previously mentioned, ChatGPT is envisioned as a tool that the public will eventually have to pay to use.
正如之前所说，ChatGPT被设想为一种将让公众不得不购买使用的工具。
Over a million users have registered to use ChatGPT within the first five days since it was opened to the public.
从它公布以来，在最初的五天，已经有超过一百万用户注册了ChatGPT。
More resources:
ChatGPT Examples: 5 Ways SEOs and Digital Marketers Can Use ChatGPT
Microsoft Bing With ChatGPT Reportedly Launching In March
ChatGPT For Content and SEO?
The Future Of Chatbots: Use Cases & Opportunities You Need To Know
How Machine Learning in Search Works
