chatGPT를 이용한 chatGPT 강의자료 만들기!
강사의 역할에 대한 고민이 필요해 보인다
며칠 전까지만 해도 영하 10도 이하로 떨어지는 날들이 계속되었는데, 입춘이 지나며 낮 기온은 영상 10도를 넘어설 정도로 날이 풀리는 것 같습니다. 아마도 생성형 인공지능인 챗GPT(ChatGPT)에 대한 열풍 때문이 아닐까 합니다. 신문 기사는 읽을 수도 없고 페이스북에 올라오는 업데이트만 따라가기에도 바쁠 정도입니다.
저도 아래 보이는 <앰비언트 - 포스트 스마트폰 시대에 대비하라>라는 책의 표지를 미드저니(Midjourney)를 이용해서 생성한 적이 있어서 누구보다도 chatGPT 같은 생성형 인공지능에 관심이 많은 데다가, 벌써부터 모 기업에서 강연 요청이 들어와서 관련 자료를 준비하고 있습니다.
http://www.yes24.com/Product/Goods/116475354
그동안 발표자료는 제가 틈틈이 모은 자료들을 이용해서 만들곤 했는데요, 이번에는 chatGPT를 이용해서 만들어 보기로 했습니다. 말 그대로 "chatGPT에 의한 chatGPT 강의"가 되겠습니다 ㅋㅋ
강연 목차 생성
강의를 할 때면 저는 대상이 누구고 몇 명이며 어떤 이유로 강연 요청이 들어왔는지를 제일 먼저 확인합니다. 그래야 기대에 부응하며 청자들의 수준에 맞는 강연을 할 수 있기 때문이죠. 물론 다른 강연이 있는지, 강연 시간은 얼마나 되며 시간대도 확인합니다. 그러고 나면 이제는 어떤 내용들로 강연을 해야 할지 고민을 하고 관련 자료들을 모아 정리를 합니다.
강연 내용들은 청자들의 상황에 맞는 내용들을 생각해 본 후에 해당 내용들로 일종의 스토리라인을 만듭니다. 강연이 자연스럽게 흘러가야 하기 때문입니다. 그리고 중간 중간 지루함을 달래기 위한 영상이나 이야기 거리들도 생각해 보고 추가합니다. 이번에도 예외는 아니었습니다.
그렇게 이해하기 쉬운 사례 및 자료들을 모아서 발표 자료를 거의 마무리했을 즈음 갑자기 이런 생각이 떠올랐습니다. 실제로 ChatGPT를 이용하는 방법을 보여주는 것도 좋겠다는 거였죠. 그래서 chatGPT에게 질문을 하기 시작했습니다.
질문은 한글로 해도 되지만, 영어로 하는 것이 더 좋을 것 같아서 직접 영작을 했습니다. 그러다가, 구글 번역기를 이용해서 더 영어같은 문장을 만들기로 했습니다. 그리고 다음과 같이 질문을 했죠.
Q0: If you were to give a lecture on chatGPT, what would be good to give a lecture on?
(챗GPT에 대한 강연을 할 때, 어떤 내용들로 구성하면 좋을까?를 구글 번역기가 번역한 것임)
그랬더니 다음과 같이 답변을 주었습니다. ChatGPT에 대한 강연을 한다면 다음과 같은 타픽이 포함될 수 있을 것이라며 7개 항목을 제시해 줍니다. ChatGPT에 대한 종합적인 이해뿐만 아니라 사회에 미치는 잠재적인 영향에 대해서까지 함께 고민하면 좋을 내용들로 구성했다는 거죠. 하나하나 보니 꽤 괜찮습니다.
A0: If giving a lecture on ChatGPT, the following topics may be covered:
Introduction to OpenAI and its role in advancing AI research
The concept and evolution of Generative Pre-trained Transformer (GPT) models
How ChatGPT works, including the training process and key components
Discussion of the strengths and limitations of ChatGPT
Real-world examples of ChatGPT's applications, including conversational AI, content generation, and language translation
Future prospects for language models and their potential impact on various industries
Ethical considerations and potential risks of using large language models like ChatGPT, such as privacy concerns and the potential for misuse.
The lecture should aim to provide a comprehensive understanding of ChatGPT, while also exploring its implications and potential impact on society.
ChatGPT 강연에 대한 목차를 요청하니 7개 항목을 제시해 주었음
개별 항목에 대한 세부 질문 요청
ChatGPT가 제시한 7개 항목이 마음에 들어서 그대로 사용하기로 했습니다. 그리고 강 주제에 대해서 개별적인 질문을 던졌습니다. chatGPT가 영어로 답변을 주어서 그냥 영어로 옮기고 앞 부분에 Explain, Discuss, Tell me 같은 명령어만 추가하는 정도로 질문을 만들었습니다. 각 질문과 답변은 다음과 같습니다.
Q1: Could you introduce OpenAI and its research?
A1: OpenAI is an artificial intelligence research laboratory consisting of the for-profit technological company OpenAI LP and its parent company, the non-profit OpenAI Inc. It was founded in 2015 with the goal of promoting and developing friendly AI in a responsible way.
OpenAI conducts research in various areas of artificial intelligence, including machine learning, natural language processing, robotics, and computer vision. Some of their notable projects and contributions include GPT-3, a large language model capable of generating human-like text, OpenAI Gym, a toolkit for developing and comparing reinforcement learning algorithms, and DALL-E, an AI system that can generate images from textual descriptions.
The organization aims to advance AI in a way that benefits humanity as a whole, and its research is focused on creating AI systems that are safe, transparent, and trustworthy. OpenAI collaborates with a variety of organizations and institutions to advance AI research and its applications, and it also regularly releases its research findings and software tools to the public.
답변 내용을 바탕으로 강의 자료 만들기
너무 친절하지 않습니까? OpenAI는 언제 설립되었고 어떤 회사들로 구성되었으며 목적은 무엇이다 등등.. 영어 모르면 다시 구글 번역기로 돌려서 내용 확인하시면 됩니다. 이 내용들을 바탕으로 각 타픽에 대한 강의 자료를 만듭니다. 이때는 단순히 텍스트만 나열하는 것이 아니라 텍스트에 포함된 내용들을 바탕으로 이미지나 영상을 검색해서 자료를 만듭니다.
또한, 관련 내용으로 뉴스 검색까지 해서 최신 내용들까지 업데이트를 시켜줍니다. 예를 들면, 마이크로소프트가 검색엔진인 Bing과 chatGPT를 결합하려 한다는 내용이나 이로 인해 타격을 받을 것으로 보이는 구글이 대규모 언어 모델(LLM)인 LamDA를 사용하는 생성형 인공지능 Bard를 발표했고 Antropic에도 4억 달러를 투자했다는 등의 이야기를 넣으면 더 좋겠죠.
강연 및 내용 업데이트
이렇게 해서 강의 자료를 만들어서 강의를 진행하면 되구요, 필요한 경우 해당 내용으로 유튜브를 만들어서 공유를 해도 좋을 것 같습니다. 저도 조만간 유튜브에 강연 영상도 올릴 예정입니다. 이제 필요한 것은 여러 분의 강의 스킬과 강연에서 뿜어지는 신뢰감입니다. 요즘 워낙 양아치 강사들이 많기 때문에 신뢰감을 줄 수 있도록 노력해야 합니다.
예를 들면, 저의 경우는 이 글과 같은 내용이나 최근에 출간한 책의 표지를 만들 때 미드저니를 사용했던 사례를 공유합니다. 제대로 써보지도 않고 강의를 하는 경우 저처럼 외모가 탁월해서 무한 신뢰를 받는 사람들을 제외하고는 그렇게 신뢰를 얻을 수는 없거든요.
미드저니(Midjourney)를 이용해서 책 표지를 만드는 과정 예시그리고 꼭 해야 할 일이 있다면, 자신만의 생각을 강의 자료에 반영하는 것입니다. chatGPT를 써보신 분들은 아시겠지만, 상당히 중립적인 답변을 해 줍니다. 따라서, 해당 내용만으로 강의를 진행하면 균형감은 있겠지만 재미는 없을 가능성이 큽니다. 가능하다면 자신의 생각을 재밌는 방식으로 강의 자료에 반영한다면 더욱 좋을 것 같습니다.
또 하나 해야 할 일은 지속적인 자료 업데이트입니다. chatGPT는 베타 버전이 2022년 11월 30일 발표었고 최근 다양한 이야기들이 끊임없이 나오기 때문에 지속적으로 관련 내용을 업데이트 해주는 것이 필요합니다. 앞에서 말씀 드린 것처럼, 페이스북 등에 남들이 공유한 거나 페이스북의 chatGPT 그룹의 게시글들을 참조하면 좋을 것 같습니다.
나머지 질문들
아래 내용들은 위에서 공유드린 목차 및 OpenAI 소개 내용을 뺀 나머지 타픽들에 대한 설명입니다. 혹시라도 강의자료 만드실 분들은 참고하세요 ^^
Q2: Explain the concept and evolution of the Generative Pre-trained Transformer (GPT) models
A2: Generative Pre-trained Transformer (GPT) models are a type of language model developed by OpenAI. They are based on the Transformer architecture, which was introduced in the paper "Attention is All You Need" in 2017. The Transformer architecture is a neural network architecture designed for processing sequential data, such as text.
GPT models are pre-trained on a large corpus of text data, which allows them to learn patterns and relationships in the language. After the pre-training, the model can be fine-tuned for specific tasks, such as text generation, text classification, and machine translation.
The first GPT model, GPT-1, was introduced in 2018 and had a modest size of 117 million parameters. Since then, the size of GPT models has steadily increased, with the latest models, such as GPT-3, having over 175 billion parameters. This increase in size has allowed GPT models to achieve impressive results on a variety of natural language processing tasks, such as text generation, question answering, and summarization.
The evolution of GPT models has been driven by the rapid progress in hardware and computational resources, which has allowed for the training of larger and more complex models. Additionally, advancements in the understanding of deep learning and language modeling have also contributed to the development of these models.
Overall, the GPT models have been a significant breakthrough in the field of natural language processing and have opened up many new possibilities for AI applications. However, there are also concerns about their potential implications, such as the possibility of generating misleading or malicious content, and the ethical and social implications of their development and deployment.
Q3: Explain how ChatGPT works, including the training process and key components
A3: ChatGPT is a conversational AI model developed by OpenAI that uses the GPT (Generative Pre-trained Transformer) architecture.
Training Process: ChatGPT is trained on a massive amount of text data, such as books, articles, and online conversations. The model is trained using unsupervised learning, which means that it is not explicitly told what the correct outputs are. Instead, it uses an objective function, such as maximum likelihood estimation, to predict the next word in a sequence given the previous words. The model is then fine-tuned for specific tasks, such as answering questions or generating text, using supervised learning.
Key Components:
Attention Mechanism: ChatGPT uses the attention mechanism, which allows the model to focus on relevant information in the input sequence and to weigh the importance of each word in generating the output.
Transformer Architecture: ChatGPT uses the Transformer architecture, which is designed to process sequential data and to handle long-range dependencies in the input data. This makes it well-suited for natural language processing tasks.
Language Modeling: ChatGPT is trained as a language model, which means that it is trained to predict the probability of a sequence of words given the previous words in the sequence. This allows it to generate coherent and fluent text.
Pre-training: ChatGPT is pre-trained on a large corpus of text data, which allows it to learn patterns and relationships in the language. This pre-training allows the model to be fine-tuned for specific tasks using relatively small amounts of task-specific data.
Overall, ChatGPT combines the attention mechanism, the Transformer architecture, language modeling, and pre-training to generate text that is both coherent and relevant to the input. The large size of the model and the massive amount of text data used during training make ChatGPT one of the most advanced models for natural language processing.
Q4: Discuss the strengths and limitations of ChatGPT
A4: Strengths of ChatGPT:
Natural Language Understanding: ChatGPT has a strong understanding of natural language and can generate human-like responses to a wide range of questions and prompts.
Contextual Awareness: ChatGPT is capable of maintaining context across multiple turns of conversation and can generate responses that are relevant to the conversation history.
High Accuracy: ChatGPT is trained on a large corpus of text data and fine-tuned for specific tasks, which leads to high accuracy in generating responses that are appropriate and relevant to the input.
Scalability: The large size of the ChatGPT model allows it to be fine-tuned for a variety of tasks, and its architecture makes it easy to scale to handle larger amounts of data.
Real-Time Processing: ChatGPT is capable of processing input and generating output in real-time, making it suitable for applications where fast response times are required.
Limitations of ChatGPT:
Bias: Like any machine learning model, ChatGPT is only as unbiased as the data it is trained on. If the training data contains biases, such as gender or racial biases, ChatGPT may perpetuate these biases in its responses.
Lack of Common Sense: ChatGPT does not have a built-in understanding of common sense and can sometimes generate nonsensical responses or fail to understand the context of the conversation.
Limited Creativity: While ChatGPT is capable of generating human-like responses, it does not have the creativity of a human and can sometimes generate repetitive or generic responses.
Dependence on Data: ChatGPT's performance is heavily dependent on the quality and quantity of the data it is trained on. If the training data is limited or contains errors, this can negatively impact the accuracy of the model.
Overall, ChatGPT is a powerful tool for natural language processing tasks, but it has limitations that must be taken into consideration when using it for applications that require high levels of accuracy and reliability.
Q5: Tell me about Real-world examples of ChatGPT's applications, including conversational AI, content generation, and language translation
A5:
Conversational AI: ChatGPT can be used to build conversational AI applications, such as chatbots and virtual assistants. These applications can be used for customer service, support, and e-commerce, among other things. They can handle a wide range of tasks, from answering basic questions to handling more complex requests, such as booking a flight or ordering a product.
Content Generation: ChatGPT can be used to generate a variety of content, such as articles, news summaries, and even poetry. This can be useful for content creators and marketers who need to generate large amounts of content quickly and efficiently.
Language Translation: ChatGPT can be used to translate text from one language to another. This can be useful for organizations that need to communicate with customers or partners in different countries and need to translate large amounts of content quickly and accurately.
Question Answering: ChatGPT can be used to build question answering systems, which can be used to answer a wide range of questions, from basic fact-based questions to more complex questions that require understanding of the context and relationships between different pieces of information.
These are just a few examples of the real-world applications of ChatGPT. The model's ability to understand and generate natural language makes it a versatile tool for a wide range of NLP tasks.
Q6: Discuss the future prospects for language models and their potential impact on various industries
A6:
Advancements in NLP: Language models such as ChatGPT have the potential to continue advancing the field of NLP by improving our understanding of human language and enabling the development of more sophisticated language-based applications.
Customer Service and Support: Language models like ChatGPT can be used to automate customer service and support functions, reducing the need for human staff and improving the speed and accuracy of responses.
Content Creation and Marketing: Language models can be used to generate high-quality content quickly and efficiently, making them a valuable tool for content creators, marketers, and advertisers.
Healthcare: Language models can be used to analyze medical records, scientific papers, and other sources of information to improve decision-making and support research in the healthcare industry.
Finance and Banking: Language models can be used to analyze large amounts of financial data to identify trends, make predictions, and support investment decisions.
Education: Language models can be used to develop educational tools and resources, such as personalized tutors and study aids, to improve student learning outcomes.
These are just a few examples of the potential impact of language models on various industries. As the technology continues to evolve, it is likely that new applications and uses will emerge, further shaping the future of language models and their role in our lives.
Q7: Discuss ethical considerations and potential risks of using large language models like ChatGPT, such as privacy concerns and the potential for misuse.
A7:
Privacy Concerns: The use of large language models like ChatGPT involves the collection, storage, and processing of vast amounts of personal information, which raises serious privacy concerns. The potential for misuse or unauthorized access to this information is a significant risk that must be carefully managed.
Bias and Discrimination: Language models can perpetuate and amplify existing biases in society if they are trained on biased data. This can result in discriminatory outcomes and perpetuate existing inequalities.
Misinformation: Large language models like ChatGPT are capable of generating convincing but false information, which can spread rapidly and cause significant harm.
Dependence on Technology: There is a risk of over-reliance on language models, which can lead to a loss of critical thinking skills and the ability to make decisions based on one's own judgment.
Job Displacement: The use of language models for tasks previously performed by humans, such as customer service and support, may result in job displacement and economic disruption.
These are just a few examples of the ethical considerations and potential risks associated with the use of large language models like ChatGPT. It is important for companies, policymakers, and society as a whole to carefully consider these risks and take steps to mitigate them. This may include the development of ethical guidelines and regulations, as well as investment in the development of technologies that can help address these challenges.
