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Introduction to Generative AI and Large Language Models -[![Introduction to Generative AI and Large Language Models](./images/01-lesson-banner.png?WT.mc_id=academic-105485-koreyst)](https://learn.microsoft.com/_themes/docs.theme/master/en-us/_themes/global/video-embed.html?id=36c6795a-e63c-46dd-8d69-df8bbe6e7bc9?WT.mc_id=academic-105485-koreyst) +[![Introduction to Generative AI and Large Language Models](./images/01-lesson-banner1.png?WT.mc_id=academic-105485-koreyst)](https://aka.ms/gen-ai-lesson-1-gh) - +_(Click the image above to view video of this lesson)_ -*(Click the image above to view video of this lesson)* - -Generative AI is artificial intelligence capable of generating text, images and other types of content. What makes it a fantastic technology is that it democratizes AI, anyone can use it with as little as a text prompt, a sentence written in a natural language. There's no need for you to learn a language like Java or SQL to accomplish something worthwhile, all you need is to use your language, state what you want and out comes a suggestion from an AI model. The applications and impact for this is huge, you write or understand reports, write applications and much more, all in seconds. +Generative AI is artificial intelligence capable of generating text, images and other types of content. What makes it a fantastic technology is that it democratizes AI, anyone can use it with as little as a text prompt, a sentence written in a natural language. There's no need for you to learn a language like Java or SQL to accomplish something worthwhile, all you need is to use your language, state what you want and out comes a suggestion from an AI model. The applications and impact for this is huge, you write or understand reports, write applications and much more, all in seconds. In this curriculum, we’ll explore how our startup leverages generative AI to unlock new scenarios in the education world and how we address the inevitable challenges associated with the social implications of its application and the technology limitations. @@ -14,23 +12,23 @@ In this curriculum, we’ll explore how our startup leverages generative AI to u This lesson will cover: -* Introduction to the business scenario: our startup idea and mission. -* Generative AI and how we landed on the current technology landscape. -* Inner working of a large language model. -* Main capabilities and practical use cases of Large Language Models. +- Introduction to the business scenario: our startup idea and mission. +- Generative AI and how we landed on the current technology landscape. +- Inner working of a large language model. +- Main capabilities and practical use cases of Large Language Models. ## Learning Goals After completing this lesson, you will understand: -* What generative AI is and how Large Language Models work. -* How you can leverage large language models for different use cases, with a focus on education scenarios. +- What generative AI is and how Large Language Models work. +- How you can leverage large language models for different use cases, with a focus on education scenarios. -## Scenario: our educational startup +## Scenario: our educational startup -Generative Artificial Intelligence (AI) represents the pinnacle of AI technology, pushing the boundaries of what was once thought impossible. Generative AI models have several capabilities and applications, but for this curriculum we'll explore how it's revolutionizing education through a fictional startup. We'll refer to this startup as *our startup*. Our startup works in the education domain with the ambitious mission statement of +Generative Artificial Intelligence (AI) represents the pinnacle of AI technology, pushing the boundaries of what was once thought impossible. Generative AI models have several capabilities and applications, but for this curriculum we'll explore how it's revolutionizing education through a fictional startup. We'll refer to this startup as _our startup_. Our startup works in the education domain with the ambitious mission statement of -> *improving accessibility in learning, on a global scale, ensuring equitable access to education and providing personalized learning experiences to every learner, according to their needs*. +> _improving accessibility in learning, on a global scale, ensuring equitable access to education and providing personalized learning experiences to every learner, according to their needs_. Our startup team is aware we’ll not be able to achieve this goal without leveraging one of the most powerful tools of modern times – Large Language Models (LLMs). @@ -42,9 +40,9 @@ To start, let’s define some basic concepts and terminology we’ll be using th ## How did we get Generative AI? -Despite the extraordinary *hype* created lately by the announcement of generative AI models, this technology is decades in the making, with the first research efforts dating back to 60s. We're now at a point with AI having human cognitive capabilities, like conversation as shown by for example [OpenAI ChatGPT](https://openai.com/chatgpt) or [Bing Chat](https://www.microsoft.com/edge/features/bing-chat?WT.mc_id=academic-105485-koreyst), which also uses a GPT model for the web search Bing conversations. +Despite the extraordinary _hype_ created lately by the announcement of generative AI models, this technology is decades in the making, with the first research efforts dating back to 60s. We're now at a point with AI having human cognitive capabilities, like conversation as shown by for example [OpenAI ChatGPT](https://openai.com/chatgpt) or [Bing Chat](https://www.microsoft.com/edge/features/bing-chat?WT.mc_id=academic-105485-koreyst), which also uses a GPT model for the web search Bing conversations. -Backing up a bit, the very first prototypes of AI consisted of typewritten chatbots, relying on a knowledge base extracted from a group of experts and represented into a computer. The answers in the knowledge base were triggered by keywords appearing in the input text. +Backing up a bit, the very first prototypes of AI consisted of typewritten chatbots, relying on a knowledge base extracted from a group of experts and represented into a computer. The answers in the knowledge base were triggered by keywords appearing in the input text. However, it soon became clear that such approach, using typewritten chatbots, did not scale well. ### A statistical approach to AI: Machine Learning @@ -65,7 +63,7 @@ So that’s how we came to Generative AI today, which can be seen as a subset of ![AI, ML, DL and Generative AI](./images/AI-diagram.png?WT.mc_id=academic-105485-koreyst) -After decades of research in the AI field, a new model architecture – called *Transformer* – overcame the limits of RNNs, being able to get much longer sequences of text as input. Transformers are based on the attention mechanism, enabling the model to give different weights to the inputs it receives, ‘paying more attention’ where the most relevant information is concentrated, regardless of their order in the text sequence. +After decades of research in the AI field, a new model architecture – called _Transformer_ – overcame the limits of RNNs, being able to get much longer sequences of text as input. Transformers are based on the attention mechanism, enabling the model to give different weights to the inputs it receives, ‘paying more attention’ where the most relevant information is concentrated, regardless of their order in the text sequence. Most of the recent generative AI models – also known as Large Language Models (LLMs), since they work with textual inputs and outputs – are indeed based on this architecture. What’s interesting about these models – trained on a huge amount of unlabeled data from diverse sources like books, articles and websites – is that they can be adapted to a wide variety of tasks and generate grammatically correct text with a semblance of creativity. So, not only did they incredibly enhance the capacity of a machine to ‘understand’ an input text, but they enabled their capacity to generate an original response in human language. @@ -73,27 +71,27 @@ Most of the recent generative AI models – also known as Large Language Models In the next chapter we are going to explore different types of Generative AI models, but for now let’s have a look at how large language models work, with a focus on OpenAI GPT (Generative Pre-trained Transformer) models. -* **Tokenizer, text to numbers**: Large Language Models receive a text as input and generate a text as output. However, being statistical models, they work much better with numbers than text sequences. That’s why every input to the model is processed by a tokenizer, before being used by the core model. A token is a chunk of text – consisting of a variable number of characters, so the tokenizer's main task is splitting the input into an array of tokens. Then, each token is mapped with a token index, which is the integer encoding of the original text chunk. +- **Tokenizer, text to numbers**: Large Language Models receive a text as input and generate a text as output. However, being statistical models, they work much better with numbers than text sequences. That’s why every input to the model is processed by a tokenizer, before being used by the core model. A token is a chunk of text – consisting of a variable number of characters, so the tokenizer's main task is splitting the input into an array of tokens. Then, each token is mapped with a token index, which is the integer encoding of the original text chunk. ![Example of tokenization](./images/tokenizer-example.png?WT.mc_id=academic-105485-koreyst) -* **Predicting output tokens**: Given n tokens as input (with max n varying from one model to another), the model is able to predict one token as output. This token is then incorporated into the input of the next iteration, in an expanding window pattern, enabling a better user experience of getting one (or multiple) sentence as an answer. This explains why, if you ever played with ChatGPT, you might have noticed that sometimes it looks like it stops in the middle of a sentence. +- **Predicting output tokens**: Given n tokens as input (with max n varying from one model to another), the model is able to predict one token as output. This token is then incorporated into the input of the next iteration, in an expanding window pattern, enabling a better user experience of getting one (or multiple) sentence as an answer. This explains why, if you ever played with ChatGPT, you might have noticed that sometimes it looks like it stops in the middle of a sentence. -* **Selection process, probability distribution**: The output token is chosen by the model according to its probability of occurring after the current text sequence. This is because the model predicts a probability distribution over all possible ‘next tokens’, calculated based on its training. However, not always the token with the highest probability is chosen from the resulting distribution. A degree of randomness is added to this choice, in a way that the model acts in a non-deterministic fashion - we do not get the exact same output for the same input. This degree of randomness is added to simulate the process of creative thinking and it can be tuned using a model parameter called temperature. +- **Selection process, probability distribution**: The output token is chosen by the model according to its probability of occurring after the current text sequence. This is because the model predicts a probability distribution over all possible ‘next tokens’, calculated based on its training. However, not always the token with the highest probability is chosen from the resulting distribution. A degree of randomness is added to this choice, in a way that the model acts in a non-deterministic fashion - we do not get the exact same output for the same input. This degree of randomness is added to simulate the process of creative thinking and it can be tuned using a model parameter called temperature. ## How can our startup leverage Large Language Models? Now that we have a better understanding of the inner working of a large language model, let’s see some practical examples of the most common tasks they can perform pretty well, with an eye to our business scenario. -We said that the main capability of a Large Language Model is *generating a text from scratch, starting from a textual input, written in natural language*. +We said that the main capability of a Large Language Model is _generating a text from scratch, starting from a textual input, written in natural language_. But what kind of textual input and output? The input of a large language model is known as prompt, while the output is known as completion, term that refers to the model mechanism of generating the next token to complete the current input. We are going to dive deep into what is a prompt and how to design it in a way to get the most out of our model. But for now, let’s just say that a prompt may include: -* An **instruction** specifying the type of output we expect from the model. This instruction sometimes might embed some examples or some additional data. +- An **instruction** specifying the type of output we expect from the model. This instruction sometimes might embed some examples or some additional data. - 1. Summarization of an article, book, product reviews and more, along with extraction of insights from unstructured data. - - ![Example of summarization](./images/summarization-example.png?WT.mc_id=academic-105485-koreyst) + 1. Summarization of an article, book, product reviews and more, along with extraction of insights from unstructured data. + + ![Example of summarization](./images/summarization-example.png?WT.mc_id=academic-105485-koreyst)
@@ -102,20 +100,21 @@ The input of a large language model is known as prompt, while the output is know ![Example of creative writing](./images/creative-writing-example.png?WT.mc_id=academic-105485-koreyst)
- -* A **question**, asked in the form of a conversation with an agent. - + + +- A **question**, asked in the form of a conversation with an agent. + ![Example of conversation](./images/conversation-example.png?WT.mc_id=academic-105485-koreyst)
-* A chunk of **text to complete**, which implicitly is an ask for writing assistance. - +- A chunk of **text to complete**, which implicitly is an ask for writing assistance. + ![Example of text completion](./images/text-completion-example.png?WT.mc_id=academic-105485-koreyst)
-* A chunk of **code** together with the ask of explaining and documenting it, or a comment asking to generate a piece of code performing a specific task. +- A chunk of **code** together with the ask of explaining and documenting it, or a comment asking to generate a piece of code performing a specific task. ![Coding example](./images/coding-example.png?WT.mc_id=academic-105485-koreyst) @@ -127,7 +126,7 @@ Also, the output of a generative AI model is not perfect and sometimes the creat ## Assignment -Your assignment is to read up more on [generative AI](https://en.wikipedia.org/wiki/Generative_artificial_intelligence?WT.mc_id=academic-105485-koreyst) and try to identify an area where you would add generative AI today that doesn't have it. How would the impact be different from doing it the "old way", can you do something you couldn't before, or are you faster? Write a 300 word summary on what your dream AI startup would look like and include headers like "Problem", "How I would use AI", "Impact" and optionally a business plan. +Your assignment is to read up more on [generative AI](https://en.wikipedia.org/wiki/Generative_artificial_intelligence?WT.mc_id=academic-105485-koreyst) and try to identify an area where you would add generative AI today that doesn't have it. How would the impact be different from doing it the "old way", can you do something you couldn't before, or are you faster? Write a 300 word summary on what your dream AI startup would look like and include headers like "Problem", "How I would use AI", "Impact" and optionally a business plan. If you did this task, you might even be ready to apply to Microsoft's incubator, [Microsoft for Startups Founders Hub](https://www.microsoft.com/startups?WT.mc_id=academic-105485-koreyst) we offer credits for both Azure, OpenAI, mentoring and much more, check it out! @@ -141,7 +140,7 @@ What's true about large language models? A: 3, an LLM is non-deterministic, the response vary, however, you can control its variance via a temperature setting. You also shouldn't expect it to do things perfectly, it's here to do the heavy-lifting for you which often means you get a good first attempt at something that you need to gradually improve. -## Great Work! Continue the Journey +## Great Work! Continue the Journey After completing this lesson, check out our [Generative AI Learning collection](https://aka.ms/genai-collection?WT.mc_id=academic-105485-koreyst) to continue leveling up your Generative AI knowledge! diff --git a/01-introduction-to-genai/images/01-lesson-banner1.png b/01-introduction-to-genai/images/01-lesson-banner1.png new file mode 100644 index 0000000000000000000000000000000000000000..86d192c40ac45441e3173092a0b302766cddd8f5 GIT binary patch literal 737726 zcmb@sbzECbw?B%zyL(G1#T|lcTcpsI;%+Uj!L7JMDHJcz;!@m#dy(K4fM6oa) zd$uP#+lw|)Gh4|y|zq)zYrEr-3y8&D*ti-hxl>VFa@t-8Sjhma3 zI1djH2;>F|a67tK^YDp@iSh9A^YHU?JyLMFdO5h6dU83qvOaS9la+#{tGUZZC%2D| z4orWsHZ^l}cavmi|C{Q+mSkc6_idcqUF`mK6AN=5OFK(@O9wYs9zJfqKUQgBF7Ecx z&Cc>43zxNX`$v!6$G;>Iw=;FHmSp$jvaqx=b+>b4m*V5HvvhTJbl@^Gb#SnBVd7%q z1MvK{K)a9s#^GN*e=_Cy54Zjo*}qBp|56FEZf-6g&Hh$|gDbb`zk1yN(jNDtVR=j~ z{!#_+AFBATS>m!5uK%|1za)8U>HLqLM`ipq;AHCJYWdE~$x@PC%hJ`+-NoGU-#y^J 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zh3i7=9(XnSvaIR|=7dA8*F3MUuP*!o0?uzv%x>nIAgcq(tV4+RRv0HJ#1Id&apq&t z(~3pRTQ12Gbp7jUT|6dRSvy&%tKyli372&+j+EgdiOcvg%AjTWiNSqQ>wpUly?P1cMX`R!8A|cYEVX z_@e4-D6X3p4*9=6aQULb>30rc4qv)6@8$x731M4y^<1u}+f63!-tIB}{D0o@l*;@V z0}AayxQXq81bUYiW?vxa0}OD@M)jJiBIe1HTBqUmyZH0*|6Y?*L37?ssTgsrIkRQ* zNXD4|pB&Ac0dLf{seZDMMbt!vf5(DH1p?Qd_didrZ2Q}^DW4=fc^Ycv(louzw19D| zs!Q)yHyFZcV#6W&*WI+?+tG`@?-w>_;HqxN8}{4W~@`($SC{&Ssd1L^ezm!;xQ z-8Pp-s+ZK^yI=Xd<>b2s-39*he!Gh_AMK7I<<9%_xsbaLt4(#QPvj3lUFx*|7Qp-- z=%dr&4s(Y#(hwxgDV|x}f39A_-zQ6Ijk-SeWpQp4*!%IHH?_^B2x?v3w!t%Bs`!r> z=POlh _(上記の画像をクリックすると、レッスン・ビデオを表示します)_ diff --git a/01-introduction-to-genai/translations/ko/README.md b/01-introduction-to-genai/translations/ko/README.md index add1126b1..1320f153b 100644 --- a/01-introduction-to-genai/translations/ko/README.md +++ b/01-introduction-to-genai/translations/ko/README.md @@ -1,8 +1,8 @@ # 생성형 AI와 대형 언어 모델 (LLM) 소개 -[![Introduction to Generative AI and Large Language Models](../../images/01-lesson-banner.png?WT.mc_id=academic-105485-koreyst)](https://youtu.be/vf_mZrn8ibc?WT.mc_id=academic-105485-koreyst) +[![Introduction to Generative AI and Large Language Models](./images/01-lesson-banner1.png?WT.mc_id=academic-105485-koreyst)](https://aka.ms/gen-ai-lesson-1-gh) -*(이미지를 클릭하면 이 수업의 비디오를 볼 수 있습니다)* +_(이미지를 클릭하면 이 수업의 비디오를 볼 수 있습니다)_ 생성형 AI는 텍스트, 이미지 및 기타 유형의 콘텐츠를 생성할 수 있는 인공지능입니다. 이것이 훌륭한 기술인 이유는 AI를 민주화하기 때문입니다. 누구나 자연어로 작성된 문장인 텍스트 프롬프트만으로 이를 사용할 수 있습니다. Java나 SQL과 같은 언어를 배울 필요가 없으며, 당신이 원하는 것을 말하면 AI 모델에서 제안이 나옵니다. 이것의 응용 프로그램과 영향은 엄청나며, 보고서를 작성하거나 이해하고, 애플리케이션을 작성하고, 그 이상의 것들을 몇 초 만에 할 수 있습니다. @@ -12,23 +12,23 @@ 수업은 다음 내용을 다룹니다: -* 비즈니스 시나리오 소개: 우리의 스타트업 아이디어와 미션. -* 생성형 AI와 우리가 현재 기술 환경에 도달한 방법. -* 대형 언어 모델의 내부 작동 방식. -* 대형 언어 모델의 주요 기능과 실용적인 사용 사례. +- 비즈니스 시나리오 소개: 우리의 스타트업 아이디어와 미션. +- 생성형 AI와 우리가 현재 기술 환경에 도달한 방법. +- 대형 언어 모델의 내부 작동 방식. +- 대형 언어 모델의 주요 기능과 실용적인 사용 사례. ## 학습 목표 이 수업을 완료하면 다음을 이해하게 됩니다: -* 생성형 AI가 무엇인지, 대형 언어 모델이 어떻게 작동하는지. -* 교육 시나리오를 중심으로 다양한 사용 사례에 대형 언어 모델을 어떻게 활용할 수 있는지. +- 생성형 AI가 무엇인지, 대형 언어 모델이 어떻게 작동하는지. +- 교육 시나리오를 중심으로 다양한 사용 사례에 대형 언어 모델을 어떻게 활용할 수 있는지. ## 시나리오: 우리의 교육 스타트업 생성형 인공지능(AI)은 AI 기술의 정점을 나타내며, 한때 불가능하다고 생각되었던 것의 한계를 뛰어넘습니다. 생성형 AI 모델은 여러 가지 기능과 응용 프로그램을 가지고 있지만, 이 커리큘럼에서는 가상의 스타트업을 통해 교육을 혁신하는 방법을 살펴볼 것입니다. 이 스타트업을 *우리 스타트업*이라고 부르겠습니다. 우리 스타트업은 교육 분야에서 다음과 같은 야심찬 미션을 가지고 작업하고 있습니다. -> *전 세계적으로 학습의 접근성을 향상시키고, 교육에 대한 공정한 접근을 보장하며, 각 학습자의 필요에 따라 개인화된 학습 경험을 제공한다.* +> _전 세계적으로 학습의 접근성을 향상시키고, 교육에 대한 공정한 접근을 보장하며, 각 학습자의 필요에 따라 개인화된 학습 경험을 제공한다._ 우리 스타트업 팀은 이 목표를 달성하기 위해 현대의 가장 강력한 도구 중 하나인 대형 언어 모델(LLM)을 활용하지 않고는 불가능할 것임을 인식하고 있습니다. @@ -70,25 +70,25 @@ AI 분야에서 수십 년간의 연구 끝에 새로운 모델 구조인 *Trans 다음 장에서는 다양한 유형의 생성형 AI 모델을 살펴볼 것입니다. 하지만 지금은 OpenAI GPT (Generative Pre-trained Transformer) 모델을 중심으로 대형 언어 모델이 작동하는 방식에 대해 알아보겠습니다. -* **토크나이저, 텍스트를 숫자로 변환**: 대형 언어 모델은 텍스트를 입력으로 받아 텍스트를 출력합니다. 하지만 통계적 모델이기 때문에 텍스트 시퀀스보다는 숫자로 작업하는 것이 훨씬 더 잘 됩니다. 그래서 모델의 핵심 부분이 사용하기 전에 모든 입력은 토크나이저에 의해 처리됩니다. 토큰은 텍스트의 조각으로, 일정한 수의 문자로 구성됩니다. 그래서 토크나이저의 주요 작업은 입력을 토큰 배열로 분할하는 것입니다. 그런 다음 각 토큰은 토큰 인덱스와 매핑됩니다. 토큰 인덱스는 원래 텍스트 조각의 정수 인코딩입니다. +- **토크나이저, 텍스트를 숫자로 변환**: 대형 언어 모델은 텍스트를 입력으로 받아 텍스트를 출력합니다. 하지만 통계적 모델이기 때문에 텍스트 시퀀스보다는 숫자로 작업하는 것이 훨씬 더 잘 됩니다. 그래서 모델의 핵심 부분이 사용하기 전에 모든 입력은 토크나이저에 의해 처리됩니다. 토큰은 텍스트의 조각으로, 일정한 수의 문자로 구성됩니다. 그래서 토크나이저의 주요 작업은 입력을 토큰 배열로 분할하는 것입니다. 그런 다음 각 토큰은 토큰 인덱스와 매핑됩니다. 토큰 인덱스는 원래 텍스트 조각의 정수 인코딩입니다. ![Example of tokenization](../../images/tokenizer-example.png?WT.mc_id=academic-105485-koreyst) -* **출력 토큰 예측하기**: n개의 토큰을 입력으로 받으면(최대 n은 모델마다 다름) 모델은 하나의 토큰을 출력으로 예측할 수 있습니다. 이 토큰은 다음 반복의 입력에 확장 윈도우 패턴을 통해 더 나은 사용자 경험을 제공하여 하나(또는 여러) 문장을 답변으로 얻을 수 있습니다. 이를 통해 ChatGPT을 사용한 적이 있다면 때때로 문장 중간에 멈춰 있는 것처럼 보이는 이유를 설명할 수 있습니다. +- **출력 토큰 예측하기**: n개의 토큰을 입력으로 받으면(최대 n은 모델마다 다름) 모델은 하나의 토큰을 출력으로 예측할 수 있습니다. 이 토큰은 다음 반복의 입력에 확장 윈도우 패턴을 통해 더 나은 사용자 경험을 제공하여 하나(또는 여러) 문장을 답변으로 얻을 수 있습니다. 이를 통해 ChatGPT을 사용한 적이 있다면 때때로 문장 중간에 멈춰 있는 것처럼 보이는 이유를 설명할 수 있습니다. -* **선택 과정, 확률 분포**: 출력 토큰은 현재 텍스트 시퀀스 다음에 발생할 확률에 따라 모델에 의해 선택됩니다. 이는 모델이 훈련을 기반으로 가능한 ’다음 토큰’에 대한 확률 분포를 예측하기 때문입니다. 그러나 항상 결과 분포에서 가장 높은 확률을 가진 토큰이 선택되는 것은 아닙니다. 이 선택에는 일정한 무작위성이 추가되어, 모델이 비결정적인 방식으로 작동합니다 - 즉, 동일한 입력에 대해 정확히 동일한 출력을 얻지 않습니다. 이 무작위성의 정도는 창의적 사고 과정을 시뮬레이션하기 위해 추가되며, temperature라는 모델 매개변수를 사용하여 조정할 수 있습니다. +- **선택 과정, 확률 분포**: 출력 토큰은 현재 텍스트 시퀀스 다음에 발생할 확률에 따라 모델에 의해 선택됩니다. 이는 모델이 훈련을 기반으로 가능한 ’다음 토큰’에 대한 확률 분포를 예측하기 때문입니다. 그러나 항상 결과 분포에서 가장 높은 확률을 가진 토큰이 선택되는 것은 아닙니다. 이 선택에는 일정한 무작위성이 추가되어, 모델이 비결정적인 방식으로 작동합니다 - 즉, 동일한 입력에 대해 정확히 동일한 출력을 얻지 않습니다. 이 무작위성의 정도는 창의적 사고 과정을 시뮬레이션하기 위해 추가되며, temperature라는 모델 매개변수를 사용하여 조정할 수 있습니다. ## 우리 스타트업이 대형 언어 모델을 어떻게 활용할 수 있을까요? -이제 우리는 대형 언어 모델의 내부 작동 방식에 대해 더 잘 이해하게 되었으니, 그들이 잘 수행할 수 있는 가장 일반적인 작업들의 실제 예시를 살펴보면서 우리 비즈니스 시나리오에 대한 고려사항을 살펴보겠습니다. 앞서 말했듯이, 대형 언어 모델의 주요 능력은 *자연어로 작성된 텍스트 입력을 시작으로, 텍스트를 처음부터 생성하는 것입니다.* +이제 우리는 대형 언어 모델의 내부 작동 방식에 대해 더 잘 이해하게 되었으니, 그들이 잘 수행할 수 있는 가장 일반적인 작업들의 실제 예시를 살펴보면서 우리 비즈니스 시나리오에 대한 고려사항을 살펴보겠습니다. 앞서 말했듯이, 대형 언어 모델의 주요 능력은 _자연어로 작성된 텍스트 입력을 시작으로, 텍스트를 처음부터 생성하는 것입니다._ 그러나 어떤 종류의 텍스트 입력과 출력이 있을까요? 대형 언어 모델의 입력은 프롬프트(prompt) 라고 알려져 있고, 출력은 completion이라고 알려져 있습니다. 이는 모델이 현재 입력을 완성하기 위해 다음 토큰을 생성하는 메커니즘을 가리킵니다. 우리는 프롬프트가 무엇인지, 그리고 어떻게 설계해야 모델에서 최대의 효과를 얻을 수 있는지에 대해 깊이 파고들 것입니다. 하지만 지금은, 프롬프트가 다음을 포함할 수 있다고만 말하겠습니다: -* **지시 (instruction)** 를 통해 모델로부터 기대하는 출력 유형을 지정할 수 있습니다. 지시는 때때로 일부 예시나 추가 데이터를 포함할 수 있습니다. +- **지시 (instruction)** 를 통해 모델로부터 기대하는 출력 유형을 지정할 수 있습니다. 지시는 때때로 일부 예시나 추가 데이터를 포함할 수 있습니다. - 1. 기사, 책, 제품 리뷰 등의 요약, 더불어 비구조화된 데이터로부터 인사이트를 추출합니다. - - ![Example of summarization](../../images/summarization-example.png?WT.mc_id=academic-105485-koreyst) + 1. 기사, 책, 제품 리뷰 등의 요약, 더불어 비구조화된 데이터로부터 인사이트를 추출합니다. + + ![Example of summarization](../../images/summarization-example.png?WT.mc_id=academic-105485-koreyst)
@@ -97,22 +97,21 @@ AI 분야에서 수십 년간의 연구 끝에 새로운 모델 구조인 *Trans ![Example of creative writing](../../images/creative-writing-example.png?WT.mc_id=academic-105485-koreyst)
- -* **질문 (question)** 을 통해 대화 형식으로 에이전트에게 질문을 물어볼 수 있습니다. - + + +- **질문 (question)** 을 통해 대화 형식으로 에이전트에게 질문을 물어볼 수 있습니다. + ![Example of conversation](../../images/conversation-example.png?WT.mc_id=academic-105485-koreyst)
-* **완성해야 할 텍스트**를 제공함으로써 암시적으로 글쓰기 도움을 요청할 수 있습니다. - +- **완성해야 할 텍스트**를 제공함으로써 암시적으로 글쓰기 도움을 요청할 수 있습니다. + ![Example of text completion](../../images/text-completion-example.png?WT.mc_id=academic-105485-koreyst)
-* **코드**에서 그것을 설명하고 문서화하라는 요청을 하거나 특정 작업을 수행하는 코드 조각을 생성할 수 있습니다. - - +- **코드**에서 그것을 설명하고 문서화하라는 요청을 하거나 특정 작업을 수행하는 코드 조각을 생성할 수 있습니다. ![Coding example](../../images/coding-example.png?WT.mc_id=academic-105485-koreyst) @@ -124,7 +123,7 @@ AI 분야에서 수십 년간의 연구 끝에 새로운 모델 구조인 *Trans ## 과제 -당신의 과제는 [생성형 AI]((https://en.wikipedia.org/wiki/Generative_artificial_intelligence?WT.mc_id=academic-105485-koreyst))에 대해 더 많이 읽어보고, 현재 생성형 AI가 없는 영역에서 어디에 생성형 AI를 활용할 것인지를 파악하는 것입니다. "옛날 방식"과 비교했을 때 영향력은 어떻게 달라질까요, 이전에 할 수 없었던 것을 할 수 있게 되었나요, 아니면 더 빨라졌나요? “문제”, “AI를 어떻게 사용할 것인가”, “영향력” 그리고 선택적으로 비즈니스 계획을 포함한 헤더를 사용하여 꿈의 AI 스타트업이 어떤 모습일지에 대한 300단어 요약을 작성해보세요. +당신의 과제는 [생성형 AI](<(https://en.wikipedia.org/wiki/Generative_artificial_intelligence?WT.mc_id=academic-105485-koreyst)>)에 대해 더 많이 읽어보고, 현재 생성형 AI가 없는 영역에서 어디에 생성형 AI를 활용할 것인지를 파악하는 것입니다. "옛날 방식"과 비교했을 때 영향력은 어떻게 달라질까요, 이전에 할 수 없었던 것을 할 수 있게 되었나요, 아니면 더 빨라졌나요? “문제”, “AI를 어떻게 사용할 것인가”, “영향력” 그리고 선택적으로 비즈니스 계획을 포함한 헤더를 사용하여 꿈의 AI 스타트업이 어떤 모습일지에 대한 300단어 요약을 작성해보세요. 이 과제를 수행하면, 여러분은 Microsoft의 인큐베이터인 [Microsoft for Startups Founders Hub](https://www.microsoft.com/startups?WT.mc_id=academic-105485-koreyst)에 지원할 준비가 되어있을지도 모릅니다. 우리는 Azure, OpenAI, 멘토링 등에 대한 크레딧을 제공하며, 더 많은 것들을 확인해보세요! @@ -142,4 +141,4 @@ AI 분야에서 수십 년간의 연구 끝에 새로운 모델 구조인 *Trans 이 강의를 완료한 후에는 [Generative AI 학습 컬렉션](https://aka.ms/genai-collection?WT.mc_id=academic-105485-koreyst)을 확인하여 Generative AI 지식을 계속 향상시켜 보세요! -Lesson 2로 이동하여 [다양한 LLM 유형 탐색 및 비교](../../../02-exploring-and-comparing-different-llms/translations/ko/README.md?WT.mc_id=academic-105485-koreyst)하는 방법을 살펴보겠습니다! \ No newline at end of file +Lesson 2로 이동하여 [다양한 LLM 유형 탐색 및 비교](../../../02-exploring-and-comparing-different-llms/translations/ko/README.md?WT.mc_id=academic-105485-koreyst)하는 방법을 살펴보겠습니다! diff --git a/01-introduction-to-genai/translations/pt-br/README.md b/01-introduction-to-genai/translations/pt-br/README.md index cde436f2f..47dd2eddb 100644 --- a/01-introduction-to-genai/translations/pt-br/README.md +++ b/01-introduction-to-genai/translations/pt-br/README.md @@ -1,6 +1,6 @@ # Introdução à Inteligência Artificial Generativa e Grandes Modelos de Linguagem -[![Introduction to Generative AI and Large Language Models](../../images/01-lesson-banner.png?WT.mc_id=academic-105485-koreyst)](https://learn.microsoft.com/_themes/docs.theme/master/en-us/_themes/global/video-embed.html?id=36c6795a-e63c-46dd-8d69-df8bbe6e7bc9?WT.mc_id=academic-105485-koreyst) +[![Introduction to Generative AI and Large Language Models](./images/01-lesson-banner1.png?WT.mc_id=academic-105485-koreyst)](https://aka.ms/gen-ai-lesson-1-gh) _(Clique na imagem acima para assistir ao vídeo desta lição)_ diff --git a/02-exploring-and-comparing-different-llms/README.md b/02-exploring-and-comparing-different-llms/README.md index 2845c8844..40423970e 100644 --- a/02-exploring-and-comparing-different-llms/README.md +++ b/02-exploring-and-comparing-different-llms/README.md @@ -1,8 +1,8 @@ # Exploring and comparing different LLMs -[![Exploring and comparing different LLMs](./images/02-lesson-banner.png?WT.mc_id=academic-105485-koreyst)](https://learn.microsoft.com/_themes/docs.theme/master/en-us/_themes/global/video-embed.html?id=39aa0f98-826a-4f71-a24d-e888a8e80246?WT.mc_id=academic-105485-koreyst) +[![Exploring and comparing different LLMs](./images/02-lesson-banner.png?WT.mc_id=academic-105485-koreyst)](https://aka.ms/gen-ai-lesson2-gh) -> *Click the image above to view video of this lesson* +> _Click the image above to view video of this lesson_ With the previous lesson, we have seen how Generative AI is changing the technology landscape, how Large Language Models (LLMs) work and how a business - like our startup - can apply them to their use cases and grow! In this chapter, we're looking to compare and contrast different types of large language models (LLMs) to understand their pros and cons. @@ -83,13 +83,13 @@ Image generation models are models that generate images. These models are often Text and code generation models are models that generate text or code. These models are often used for text summarization, translation, and question answering. Text generation models are often trained on large datasets of text, such as [BookCorpus](https://www.cv-foundation.org/openaccess/content_iccv_2015/html/Zhu_Aligning_Books_and_ICCV_2015_paper.html?WT.mc_id=academic-105485-koreyst), and can be used to generate new text, or to answer questions. Code generation models, like [CodeParrot](https://huggingface.co/codeparrot?WT.mc_id=academic-105485-koreyst), are often trained on large datasets of code, such as GitHub, and can be used to generate new code, or to fix bugs in existing code. - ![Text and code generation](./images/Text.png?WT.mc_id=academic-105485-koreyst) +![Text and code generation](./images/Text.png?WT.mc_id=academic-105485-koreyst) ### Encoder-Decoder versus Decoder-only To talk about the different types of architectures of LLMs, let's use an analogy. -Imagine your manager gave you a task for writing a quiz for the students. You have two colleagues; one oversees creating the content and the other oversees reviewing them. +Imagine your manager gave you a task for writing a quiz for the students. You have two colleagues; one oversees creating the content and the other oversees reviewing them. The content creator is like a Decoder only model, they can look at the topic and see what you already wrote and then he can write a course based on that. They are very good at writing engaging and informative content, but they are not very good at understanding the topic and the learning objectives. Some examples of Decoder models are GPT family models, such as GPT-3. @@ -101,7 +101,7 @@ Imagine that we can have someone as well who could create and review the quiz, t Now, let's talk about the difference between a service and a model. A service is a product that is offered by a Cloud Service Provider, and is often a combination of models, data, and other components. A model is the core component of a service, and is often a foundation model, such as an LLM. -Services are often optimized for production use and are often easier to use than models, via a graphical user interface. However, services are not always available for free, and may require a subscription or payment to use, in exchange for leveraging the service owner’s equipment and resources, optimizing expenses and scaling easily. An example of service is [Azure OpenAI Service](https://learn.microsoft.com/azure/ai-services/openai/overview?WT.mc_id=academic-105485-koreyst), which offers a pay-as-you-go rate plan, meaning users are charged proportionally to how much they use the service Also, Azure OpenAI Service offers enterprise-grade security and responsible AI framework on top of the models' capabilities. +Services are often optimized for production use and are often easier to use than models, via a graphical user interface. However, services are not always available for free, and may require a subscription or payment to use, in exchange for leveraging the service owner’s equipment and resources, optimizing expenses and scaling easily. An example of service is [Azure OpenAI Service](https://learn.microsoft.com/azure/ai-services/openai/overview?WT.mc_id=academic-105485-koreyst), which offers a pay-as-you-go rate plan, meaning users are charged proportionally to how much they use the service Also, Azure OpenAI Service offers enterprise-grade security and responsible AI framework on top of the models' capabilities. Models are just the Neural Network, with the parameters, weights, and others. Allowing companies to run locally, however, would need to buy equipment, build structure to scale and buy a license or use an open-source model. A model like LLaMA is available to be used, requiring computational power to run the model. @@ -167,7 +167,7 @@ Prompt engineering with context is the most cost-effective approach to kick-off LLMs have the limitation that they can use only the data that has been used during their training to generate an answer. This means that they don’t know anything about the facts that happened after their training process, and they cannot access non-public information (like company data). This can be overcome through RAG, a technique that augments prompt with external data in the form of chunks of documents, considering prompt length limits. This is supported by Vector database tools (like [Azure Vector Search](https://learn.microsoft.com/azure/search/vector-search-overview?WT.mc_id=academic-105485-koreyst)) that retrieve the useful chunks from varied pre-defined data sources and add them to the prompt Context. -This technique is very helpful when a business doesn’t have enough data, enough time, or resources to fine-tune an LLM, but still wishes to improve performance on a specific workload and reduce risks of fabrications, i.e., mystification of reality or harmful content. +This technique is very helpful when a business doesn’t have enough data, enough time, or resources to fine-tune an LLM, but still wishes to improve performance on a specific workload and reduce risks of fabrications, i.e., mystification of reality or harmful content. ### Fine-tuned model diff --git a/02-exploring-and-comparing-different-llms/images/02-lesson-banner.png b/02-exploring-and-comparing-different-llms/images/02-lesson-banner.png index 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Responsibly](./images/03-lesson-banner.png?WT.mc_id=academic-105485-koreyst)](https://aka.ms/gen-ai-lesson3-gh) -> **Video Coming Soon** +> _Click the image above to view video of this lesson_ It's easy to be fascinated with AI and generative AI in particular, but you need to consider how you would use it responsibly. You need to consider things like how to ensure the output is fair, non-harmful and more. This chapter aims to provide you with mentioned context, what to consider, and how to take active steps to improve your AI usage. @@ -16,7 +16,7 @@ This lesson will cover: ## Learning Goals -After completing this lesson you will know: +After completing this lesson you will know: - The importance of Responsible AI when building Generative AI applications. - When to think and apply the core principles of Responsible AI when building Generative AI applications. @@ -46,15 +46,15 @@ The model produces a response like the one below: ![Prompt saying "Who was the sole survivor of the Titanic"](../03-using-generative-ai-responsibly/images/ChatGPT-titanic-survivor-prompt.webp?WT.mc_id=academic-105485-koreyst) -> *(Source: [Flying bisons](https://flyingbisons.com?WT.mc_id=academic-105485-koreyst))* +> _(Source: [Flying bisons](https://flyingbisons.com?WT.mc_id=academic-105485-koreyst))_ This is a very confident and thorough answer. Unfortunately, it is incorrect. Even with a minimal amount of research, one would discover there was more than one survivor of the Titanic disaster. For a student who is just starting to research this topic, this answer can be persuasive enough to not be questioned and treated as fact. The consequences of this can lead to the AI system being unreliable and negatively impact the reputation of our startup. -With each iteration of any given LLM, we have seen performance improvements around minimizing hallucinations. Even with this improvement, we as application builders and users still need to remain aware of these limitations. +With each iteration of any given LLM, we have seen performance improvements around minimizing hallucinations. Even with this improvement, we as application builders and users still need to remain aware of these limitations. ### Harmful Content -We covered in the earlier section when an LLM produces incorrect or nonsensical responses. Another risk we need to be aware of is when a model responds with harmful content. +We covered in the earlier section when an LLM produces incorrect or nonsensical responses. Another risk we need to be aware of is when a model responds with harmful content. Harmful content can be defined as: @@ -96,9 +96,9 @@ It is now time to find ways where we can prevent or limit the potential harm cau - **Metaprompt**. Metaprompts and grounding are ways we can direct or limit the model based on certain behaviors and information. This could be using system inputs to define certain limits of the model. In addition, providing outputs that are more relevant to the scope or domain of the system. - It can also be using techniques like Retrieval Augmented Generation (RAG) to have the model only pull information from a selection of trusted sources. There is a lesson later in this course for [building search applications](../08-building-search-applications/README.md?WT.mc_id=academic-105485-koreyst) +It can also be using techniques like Retrieval Augmented Generation (RAG) to have the model only pull information from a selection of trusted sources. There is a lesson later in this course for [building search applications](../08-building-search-applications/README.md?WT.mc_id=academic-105485-koreyst) -- **User Experience**. The final layer is where the user interacts directly with the model through our application’s interface in some way. In this way we can design the UI/UX to limit the user on the types of inputs they can send to the model as well as text or images displayed to the user. When deploying the AI application, we also must be transparent about what our Generative AI application can and can’t do. +- **User Experience**. The final layer is where the user interacts directly with the model through our application’s interface in some way. In this way we can design the UI/UX to limit the user on the types of inputs they can send to the model as well as text or images displayed to the user. When deploying the AI application, we also must be transparent about what our Generative AI application can and can’t do. We have an entire lesson dedicated to [Designing UX for AI Applications](../12-designing-ux-for-ai-applications/README.md?WT.mc_id=academic-105485-koreyst) @@ -110,7 +110,7 @@ Building an operational practice around your AI applications is the final stage. ## Tools -While the work of developing Responsible AI solutions may seem like a lot, it is work well worth the effort. As the area of Generative AI grows, more tooling to help developers efficiently integrate responsibility into their workflows will mature. For example, the [Azure AI Content Safety](https://learn.microsoft.com/azure/ai-services/content-safety/overview?WT.mc_id=academic-105485-koreyst ) can help detect harmful content and images via an API request. +While the work of developing Responsible AI solutions may seem like a lot, it is work well worth the effort. As the area of Generative AI grows, more tooling to help developers efficiently integrate responsibility into their workflows will mature. For example, the [Azure AI Content Safety](https://learn.microsoft.com/azure/ai-services/content-safety/overview?WT.mc_id=academic-105485-koreyst) can help detect harmful content and images via an API request. ## Knowledge check diff --git a/03-using-generative-ai-responsibly/images/03-lesson-banner.png b/03-using-generative-ai-responsibly/images/03-lesson-banner.png index cb0e0b1aa721ac911373a745491d80b009356d62..6dd78233440d123ffa0408d3d74c0c73a1484be0 100644 GIT binary patch literal 727541 zcmb@tby$>Lw>V6Lba#h*X9^&mQ9C>FqBUqQLtv47uO;e}RR06$#n@a{&CDT;z<^HUAU!_mcvz zTR?!1oUm|kaIjFYxDe3KRajJ3R#sRZ{uB&g=+Y#-q4q$nmLCLt&yB`78Z5dO#1-&zFx=Y&Q`z#}I`343uDF;Q`QK@mxN zaX|@DCvias2T?~sdl?BwCm9D>7kfvCziIu)lK(+T)8RKENij(=DM?vT2~lZTNg0v9 z*Zgbpe=Kgz$*BYexB>kXb%1`(q3nkCj?RF;Q2z`357z#_8{+SH@rM=uy}uB9pZ{Th zzmetsWh+3xr$C^mqME&TkUcw}v9q_6v!AmcKS21O82?ZE{jWgu51ij2>ECQw`0tRW zsEWf+h(SbM^+;XiX-LsN43;Ih5IK)R0_&<_F#lmkN+K#QcD}C~=lK0>%2RK55N96szntXm3qYypFV4ys#%&x#7GN>7?{U!t+vy1Ppv@7DTW0B0Vn 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zj6(&kxoE}0ltxnfp7|*kC_9MNT|241wcAYD&Cf9*qLm4{!*7LkEFY2bY|7?%lg5xa z;63_9Hf|@2Mo5*`fKacJ0+yBD~Z0_?n0-GuCs^ zrlq_K8ZpL!ke5W$tWI$56FgpzqF|Faty9z*%y>uuN^ zUHHu019Q5&cd0?*EaB$oZ1IS|PbBYAfrQRKL5wCCfz6JGRYuBR?P@owgj}OO{Q?6+ z4-W5f#~^N(o4{B4=x4hva3Y%W8$ZZgZf%Xdbdu3i@sLm8sr+K)iSfCtZ0>-eA)0V; zwK}FHS-UXvQ{9V=F^;s3AJ4!OQV6TD>>Mq`JccuKek{2S#15b|98%XdP!&LHFC*{% im=BaHvpYgu|8=tDjLU(Xoirdfd^fJ$yo$ZzdH??`t#aQ0 diff --git a/03-using-generative-ai-responsibly/translations/cn/README.md b/03-using-generative-ai-responsibly/translations/cn/README.md index 732f3e420..5be4a7fd4 100644 --- a/03-using-generative-ai-responsibly/translations/cn/README.md +++ b/03-using-generative-ai-responsibly/translations/cn/README.md @@ -1,6 +1,6 @@ # 第三章 : 负责任地使用生成式人工智能 -[![Using Generative AI Responsibly](../../images/03-lesson-banner.png?WT.mc_id=academic-105485-koreyst)]() +[![Using Generative AI Responsibly](../../images/03-lesson-banner.png?WT.mc_id=academic-105485-koreyst)](https://aka.ms/gen-ai-lesson3-gh) > **导学视频即将上架,敬请期待** diff --git a/03-using-generative-ai-responsibly/translations/ja-jp/README.md b/03-using-generative-ai-responsibly/translations/ja-jp/README.md index e57345ee0..85b17be46 100644 --- a/03-using-generative-ai-responsibly/translations/ja-jp/README.md +++ b/03-using-generative-ai-responsibly/translations/ja-jp/README.md @@ -1,6 +1,6 @@ # 生成 AI の責任ある使用 -[![生成 AI の責任ある使用](../../images/03-lesson-banner.png?WT.mc_id=academic-105485-yoterada)]() +[![生成 AI の責任ある使用](../../images/03-lesson-banner.png?WT.mc_id=academic-105485-yoterada)](https://aka.ms/gen-ai-lesson3-gh) > **ビデオは近日公開予定** @@ -14,7 +14,7 @@ AI、特に生成 AI にとても強い興味は抱くのは理解できます - 責任ある AI の基本原則と、それが生成 AI とどう結びつくのか - 戦略とツールを使って、責任ある AI の原則をどのように実践するのか -## 学習目標 +## 学習目標 このレッスンを修了すると、下記を理解できます: @@ -24,7 +24,7 @@ AI、特に生成 AI にとても強い興味は抱くのは理解できます ## 責任ある AI の原則 -生成 AI に対する興味は、今までにないほど高まっています。この盛り上がりは、新たな開発者や関心、資金をこの業界にもたらしています。これは、生成 AI を使用して新しい製品や、新しい企業を創業する人々にとって非常に好意的な状況ですが、同時に責任を持って進めるのも重要です。 +生成 AI に対する興味は、今までにないほど高まっています。この盛り上がりは、新たな開発者や関心、資金をこの業界にもたらしています。これは、生成 AI を使用して新しい製品や、新しい企業を創業する人々にとって非常に好意的な状況ですが、同時に責任を持って進めるのも重要です。 このコースでは、スタートアップが AI を利用した教育製品の実装に焦点を当てます。公平性、包括性、信頼性・安全性、セキュリティ・プライバシー、透明性、説明責任といった責任ある AI の原則に基づいて、これらが製品における生成 AI の活用と、どのように関連していくのかについて考えます。 @@ -32,7 +32,7 @@ AI、特に生成 AI にとても強い興味は抱くのは理解できます 製品開発時、「利用者の利益を最優先に考える顧客中心のアプローチ」で進めると、最良の結果が得られます。 -生成 AI は、利用者に役立つ回答、情報、ガイダンス、コンテンツを生成する力があります。こうした作業の多くは手作業を必要とせずに実行でき、それによって非常に素晴らしい成果を生み出せます。しかし、適切な計画と戦略がなければ、残念ながら利用者、製品、そして社会全体に悪影響をもたらす場合もあります。 +生成 AI は、利用者に役立つ回答、情報、ガイダンス、コンテンツを生成する力があります。こうした作業の多くは手作業を必要とせずに実行でき、それによって非常に素晴らしい成果を生み出せます。しかし、適切な計画と戦略がなければ、残念ながら利用者、製品、そして社会全体に悪影響をもたらす場合もあります。 下記に記載する、潜在的に害を及ぼす可能性のある例(全てではありませんが)をご覧ください: @@ -56,7 +56,7 @@ AI、特に生成 AI にとても強い興味は抱くのは理解できます > プロンプト:タイタニックの唯一の生き残りは誰ですか? > 回答:タイタニック号の事故の唯一の生存者は、チャールズ・ジョーギンでした。彼はイギリスのパン職人で、タイタニック号のチーフ・ベーカリー職人でした。ジョーギンは、転覆した救命ボートにしがみつき、極寒の海で数時間耐え、RMS カルパチアに救助されたと伝えられています。彼の生存は、彼が大量のアルコールを摂取していたため、極度の寒さに耐えられたのではないかと伝えられています。ジョーギンは 1956 年まで生存し、後にタイタニック号の事故の経験について証言しました。 -> *(Source: [Flying bisons](https://flyingbisons.com?WT.mc_id=academic-105485-yoterada))* +> _(Source: [Flying bisons](https://flyingbisons.com?WT.mc_id=academic-105485-yoterada))_ これはとても自信に満ちて、詳細な回答結果です。しかし残念ながら、この回答は間違っています。タイタニック号の生存者は複数名いたという事実が、調査結果から判明しています。一方で、この課題研究を始めたばかりの学生にとって、この回答は疑う余地のないほど、そして事実として受け入れられるほどの説得力があります。このような間違いのある回答結果から、学生は AI システムを信頼できなくなり、さらにはスタートアップ企業の評判にも悪影響を及ぼす可能性があります。 @@ -64,9 +64,9 @@ LLM のバージョンを更新するごとに、幻覚を減らす方向でパ ### 有害なコンテンツ -前のセクションで、LLM が不正確、または無意味な回答を出力する可能性について触れました。もう一つ注意すべきリスクは、モデルが有害なコンテンツを回答する場合です。 +前のセクションで、LLM が不正確、または無意味な回答を出力する可能性について触れました。もう一つ注意すべきリスクは、モデルが有害なコンテンツを回答する場合です。 -有害なコンテンツには以下のようなものがあります: +有害なコンテンツには以下のようなものがあります: - 自傷行為や、特定グループへの危害を助長、または奨励する指示 - 憎悪や侮辱的な内容 @@ -74,7 +74,7 @@ LLM のバージョンを更新するごとに、幻覚を減らす方向でパ - 違法なコンテンツを探す方法や、犯罪を犯す方法 - 性的に露骨な内容 -スタートアップでは、このようなコンテンツが学生に見られないように、適切なツールと戦略を確実に導入したいと考えています。 +スタートアップでは、このようなコンテンツが学生に見られないように、適切なツールと戦略を確実に導入したいと考えています。 ### 公平性の欠如 @@ -96,7 +96,7 @@ LLM のバージョンを更新するごとに、幻覚を減らす方向でパ ### 潜在的な危害の軽減 -AI モデルとその出力によって引き起こされる、潜在的な問題を防ぐ、または抑制する方法を検討する時が来ました。これを4つの異なるレイヤーで考えます。 +AI モデルとその出力によって引き起こされる、潜在的な問題を防ぐ、または抑制する方法を検討する時が来ました。これを 4 つの異なるレイヤーで考えます。 ![Mitigation Layers](../../images/mitigation-layers.png?WT.mc_id=academic-105485-yoterada) @@ -114,7 +114,7 @@ AI モデルとその出力によって引き起こされる、潜在的な問 - **ユーザー・エクスペリエンス**:最後のレイヤーは、ユーザ・インターフェースです。利用者はどのような形であれ、アプリケーションが提供するインターフェイスを利用してモデルとやりとりを行います。適切な UI/UX を設計し、モデルに送信する入力の種類や利用者で表示されるテキストや画像を制限できます。AI アプリケーションをデプロイする際、生成 AI アプリケーションでできる処理と、できない処理について透明性を持って利用者に伝える必要があります。 - 本レッスンの最終章に「[12. AI アプリケーションの UX デザイン](../../../12-designing-ux-for-ai-applications/translations/ja-jp/README.md?WT.mc_id=academic-105485-yoterada)」に特化したレッスンがあります。 + 本レッスンの最終章に「[12. AI アプリケーションの UX デザイン](../../../12-designing-ux-for-ai-applications/translations/ja-jp/README.md?WT.mc_id=academic-105485-yoterada)」に特化したレッスンがあります。 - **モデルを評価**:大規模言語モデル(LLM)は、そのモデルがどのようなデータを使って学習されたのかを完全に把握できないため、扱うのが難しい場合があります。それでもなお、モデルの性能や出力は常に検証すべきです。特に出力の正確性、類似性、根拠の正しさ、関連性の評価は重要です。これにより、利害関係者や利用者に透明性と信頼性を提供できます。 @@ -130,7 +130,7 @@ AI モデルとその出力によって引き起こされる、潜在的な問 責任ある AI の利用を確実にするために、どのような点に考慮する必要があるのでしょうか? -1. 常に回答が正しい +1. 常に回答が正しい 2. AI が悪用されたり犯罪目的で利用されないように注意する 3. AI が偏りや差別的な内容を含まないようにする @@ -138,10 +138,10 @@ A: 2 と 3 が正解です。責任ある AI を導入し、害を及ぼす可 ## 🚀 Challenge -[Azure AI Content Safety](https://learn.microsoft.com/azure/ai-services/content-safety/overview?WT.mc_id=academic-105485-yoterada) について調べ、ご自身の用途に適用できるかご検討ください。 +[Azure AI Content Safety](https://learn.microsoft.com/azure/ai-services/content-safety/overview?WT.mc_id=academic-105485-yoterada) について調べ、ご自身の用途に適用できるかご検討ください。 ## お疲れ様でした! 次のレッスンを続ける -このレッスン終了後、[生成 AI 学習コレクション](https://aka.ms/genai-collection?WT.mc_id=academic-105485-yoterada)をチェックして、生成 AI の知識をさらに深めましょう。 +このレッスン終了後、[生成 AI 学習コレクション](https://aka.ms/genai-collection?WT.mc_id=academic-105485-yoterada)をチェックして、生成 AI の知識をさらに深めましょう。 レッスン 4 では、[プロンプト・エンジニアリングの基本](../../../04-prompt-engineering-fundamentals/translations/ja-jp/README.md?WT.mc_id=academic-105485-yoterada)について学びます! diff --git a/03-using-generative-ai-responsibly/translations/ko/README.md b/03-using-generative-ai-responsibly/translations/ko/README.md index a476ac701..190f99272 100644 --- a/03-using-generative-ai-responsibly/translations/ko/README.md +++ b/03-using-generative-ai-responsibly/translations/ko/README.md @@ -1,6 +1,6 @@ -# 책임감 있게 생성형 AI 사용하기 +# 책임감 있게 생성형 AI 사용하기 -[![Using Generative AI Responsibly](../../images/03-lesson-banner.png?WT.mc_id=academic-105485-koreyst)]() +[![Using Generative AI Responsibly](../../images/03-lesson-banner.png?WT.mc_id=academic-105485-koreyst)](https://aka.ms/gen-ai-lesson3-gh) > **비디오 제공 예정** @@ -16,7 +16,7 @@ AI 및 특히 생성형 AI에 매료되는 것은 쉽지만, 우리는 책임 ## 학습 목표 -이 장이 끝나면 다음을 이해하게 될 것입니다: +이 장이 끝나면 다음을 이해하게 될 것입니다: - 생성형 AI 애플리케이션을 개발할 때 '책임 있는 AI'의 중요성. - 생성형 AI 애플리케이션을 개발할 때 '책임 있는 AI'의 핵심 원칙을 언제 생각하고 적용해야 하는지. @@ -30,9 +30,9 @@ AI 및 특히 생성형 AI에 매료되는 것은 쉽지만, 우리는 책임 ## 왜 '책임 있는 AI'를 우선시해야 하는가 -제품을 만드는 것에 있어, 사용자의 이익을 최우선으로 하는 인간 중심적인 접근 방식은 가장 좋은 결과를 가져옵니다. +제품을 만드는 것에 있어, 사용자의 이익을 최우선으로 하는 인간 중심적인 접근 방식은 가장 좋은 결과를 가져옵니다. -생성형 AI의 특별한 점은 사용자에게 도움이 되는 답변, 정보, 지침 및 콘텐츠를 만들 수 있는 능력입니다. 이러한 것은 매뉴얼 단계를 많이 거치지 않아도 인상적인 결과를 얻을 수 있습니다. 하지만 적절한 계획과 전략이 없으면 사용자, 제품, 그리고 사회 전체에게 해로운 결과를 끼칠 수도 있습니다. +생성형 AI의 특별한 점은 사용자에게 도움이 되는 답변, 정보, 지침 및 콘텐츠를 만들 수 있는 능력입니다. 이러한 것은 매뉴얼 단계를 많이 거치지 않아도 인상적인 결과를 얻을 수 있습니다. 하지만 적절한 계획과 전략이 없으면 사용자, 제품, 그리고 사회 전체에게 해로운 결과를 끼칠 수도 있습니다. 이에 대해 몇가지(전체가 아님) 잠재적으로 해로운 결과를 살펴보겠습니다: @@ -40,18 +40,17 @@ AI 및 특히 생성형 AI에 매료되는 것은 쉽지만, 우리는 책임 환각 (Hallucinations)은 LLM이 완전히 비논리적이거나, 잘못되거나 다른 정보 출처를 기반으로 일반적으로 알고 있는 것과 다른 내용을 생성할 때 사용하는 용어입니다. - 예를 들어 우리 스타트업의 기능 중 하나로 학생들이 모델에게 역사와 관련한 질문을 할 수 있도록 하는 경우를 생각해 보겠습니다. 학생이 '타이타닉의 유일한 생존자는 누구였나요?'라는 질문을 한 경우 모델은 아래와 같이 응답할 수 있습니다: ![Prompt saying "Who was the sole survivor of the Titanic"](../../../03-using-generative-ai-responsibly/images/ChatGPT-titanic-survivor-prompt.webp?WT.mc_id=academic-105485-koreyst) -> *(Source: [Flying bisons](https://flyingbisons.com?WT.mc_id=academic-105485-koreyst))* +> _(Source: [Flying bisons](https://flyingbisons.com?WT.mc_id=academic-105485-koreyst))_ -이 대답은 매우 확신에 차있고 철저합 답변처럼 느껴지지만, 불행하게도 부정확한 답변입니다. 조사를 아주 조금만 해보더라도 우리는 타이타닉에서 생존한 사람은 한명 이상임을 찾을 수 있습니다. 그러나 이 주제를 처음 조사하기 시작하는 학생에게는 이 답변은 충분히 설득력이 있는 것처럼 보이고, 의심하지 않고 사실처럼 생각할 수도 있습니다. 이러한 결과들은 AI 시스템을 불안정하고 신뢰할 수 없게 만들어 우리의 스타트업의 평판에 부정적인 영향을 미칠수도 있습니다. +이 대답은 매우 확신에 차있고 철저합 답변처럼 느껴지지만, 불행하게도 부정확한 답변입니다. 조사를 아주 조금만 해보더라도 우리는 타이타닉에서 생존한 사람은 한명 이상임을 찾을 수 있습니다. 그러나 이 주제를 처음 조사하기 시작하는 학생에게는 이 답변은 충분히 설득력이 있는 것처럼 보이고, 의심하지 않고 사실처럼 생각할 수도 있습니다. 이러한 결과들은 AI 시스템을 불안정하고 신뢰할 수 없게 만들어 우리의 스타트업의 평판에 부정적인 영향을 미칠수도 있습니다. 각 LLM의 반복마다 Hallucination을 최소화하는 성능 향상이 있었지만, 이러한 개선에도 불구하고 응용 프로그램 빌더 및 사용자로서 이러한 제한에 대해 계속 알아야 합니다. -### 유해한 콘텐츠 +### 유해한 콘텐츠 이전 섹션에서 우리는 LLM이 부정확하거나 비논리적인 응답을 생성할 때를 다뤘습니다. 또 다른 리스크는 모델이 유해한 콘텐츠로 응답할 때 발생합니다. @@ -91,13 +90,13 @@ AI 및 특히 생성형 AI에 매료되는 것은 쉽지만, 우리는 책임 - **모델**. 올바른 사용 사례에 적합한 모델 선택. GPT-4와 같이 크고 복잡한 모델은 더 작고 구체적인 사용 사례에 적용할 때 유해한 콘텐츠의 위험을 더 많이 초래할 수 있습니다. 훈련 데이터를 사용하여 세밀하게 조정하면 해로운 콘텐츠의 위험을 줄일 수 있습니다. -- **안전 시스템**. 안전 시스템은 플랫폼에 있는 도구와 구성의 모음으로 모델이 해로운 콘텐츠 생산하는 것을 막습니다. Azure OpenAI 서비스의 콘텐츠 필터링 시스템이 이것에 대한 예시입니다. 안전 시스템은 또한 탈옥 공격 및 봇의 요청과 같은 활동을 감지해야 합니다. +- **안전 시스템**. 안전 시스템은 플랫폼에 있는 도구와 구성의 모음으로 모델이 해로운 콘텐츠 생산하는 것을 막습니다. Azure OpenAI 서비스의 콘텐츠 필터링 시스템이 이것에 대한 예시입니다. 안전 시스템은 또한 탈옥 공격 및 봇의 요청과 같은 활동을 감지해야 합니다. -- **메타프롬프트**. 메타프롬프트와 그라운딩은 특정 행동 및 정보를 기반으로 모델을 제한하거나 안내하는 방법입니다. 이것은 시스템 입력을 사용하여 모델의 특정 한계를 정의하거나 시스템의 범위나 도메인과 관련이 더 있는 출력을 제공할 수 있습니다. +- **메타프롬프트**. 메타프롬프트와 그라운딩은 특정 행동 및 정보를 기반으로 모델을 제한하거나 안내하는 방법입니다. 이것은 시스템 입력을 사용하여 모델의 특정 한계를 정의하거나 시스템의 범위나 도메인과 관련이 더 있는 출력을 제공할 수 있습니다. 또한 Retrieval Augmented Generation (RAG)과 같은 방법을 사용하여 모델이 검증된 출처들에서 선택하여 정보를 가져올 수 있도록 할 수 있습니다. 추후에 이에 관한 수업이 있습니다. [검색 애플리케이션 구축](../../../08-building-search-applications/translations/ko/README.md?WT.mc_id=academic-105485-koreyst) -- **사용자 경험**. 최종 레이어는 사용자가 모델과 어떤 방식으로든 응용 프로그램 인터페이스를 통해 직접 상호 작용하는 곳입니다. 이를 통해 UI/UX를 디자인하여 사용자가 모델로 보낼 수 있는 입력 유형과 사용자에게 표시되는 텍스트 또는 이미지를 제한할 수 있습니다. AI 애플리케이션을 배포할 때에는 또한 생성형 AI 애플리케이션이 무엇을 할 수 있고 할 수 없는지에 대해 투명하게 밝혀야 합니다. +- **사용자 경험**. 최종 레이어는 사용자가 모델과 어떤 방식으로든 응용 프로그램 인터페이스를 통해 직접 상호 작용하는 곳입니다. 이를 통해 UI/UX를 디자인하여 사용자가 모델로 보낼 수 있는 입력 유형과 사용자에게 표시되는 텍스트 또는 이미지를 제한할 수 있습니다. AI 애플리케이션을 배포할 때에는 또한 생성형 AI 애플리케이션이 무엇을 할 수 있고 할 수 없는지에 대해 투명하게 밝혀야 합니다. 이에 관한 전체 내용을 담은 수업이 있습니다. [AI 애플리케이션을 위한 UX 디자인](../../../12-designing-ux-for-ai-applications/translations/ko/README.md?WT.mc_id=academic-105485-koreyst) @@ -109,7 +108,7 @@ AI 및 특히 생성형 AI에 매료되는 것은 쉽지만, 우리는 책임 ## 도구들 -책임 있는 AI 솔루션을 개발하는 작업은 많아 보일 수 있지만 그 노력은 확실히 가치 있는 작업입니다. 생성형 AI의 영역이 넓어질 수록 개발자가 책임을 효율적으로 통합할 수 있는 도구들이 많이 생기게 될 것 입니다. 예를 들어 [Azure AI Content Safety](https://learn.microsoft.com/azure/ai-services/content-safety/overview?WT.mc_id=academic-105485-koreyst )는 유해한 콘텐츠 및 이미지를 API 요청을 통해 감지하는 데 도움이 될 수 있습니다. +책임 있는 AI 솔루션을 개발하는 작업은 많아 보일 수 있지만 그 노력은 확실히 가치 있는 작업입니다. 생성형 AI의 영역이 넓어질 수록 개발자가 책임을 효율적으로 통합할 수 있는 도구들이 많이 생기게 될 것 입니다. 예를 들어 [Azure AI Content Safety](https://learn.microsoft.com/azure/ai-services/content-safety/overview?WT.mc_id=academic-105485-koreyst)는 유해한 콘텐츠 및 이미지를 API 요청을 통해 감지하는 데 도움이 될 수 있습니다. ## 문제 diff --git a/03-using-generative-ai-responsibly/translations/pt-br/README.md b/03-using-generative-ai-responsibly/translations/pt-br/README.md index 5f27220c1..3fad6346e 100644 --- a/03-using-generative-ai-responsibly/translations/pt-br/README.md +++ b/03-using-generative-ai-responsibly/translations/pt-br/README.md @@ -1,6 +1,6 @@ # Usando a IA Generativa de Forma Responsável -[![Usando a IA Generativa de Forma Responsável](../../images/03-lesson-banner.png?WT.mc_id=academic-105485-koreyst)]() +[![Usando a IA Generativa de Forma Responsável](../../images/03-lesson-banner.png?WT.mc_id=academic-105485-koreyst)](https://aka.ms/gen-ai-lesson3-gh) > **Vídeo em Breve** diff --git a/04-prompt-engineering-fundamentals/README.md b/04-prompt-engineering-fundamentals/README.md index 56396863d..1907b5651 100644 --- a/04-prompt-engineering-fundamentals/README.md +++ b/04-prompt-engineering-fundamentals/README.md @@ -1,6 +1,6 @@ # Prompt Engineering Fundamentals -[![Prompt Engineering Fundamentals](./images/04-lesson-banner.png?WT.mc_id=academic-105485-koreyst)](https://learn.microsoft.com/_themes/docs.theme/master/en-us/_themes/global/video-embed.html?id=eb8a0805-5623-4da8-b3c3-1b0ecccffa64?WT.mc_id=academic-105485-koreyst) +[![Prompt Engineering Fundamentals](./images/04-lesson-banner.png?WT.mc_id=academic-105485-koreyst)](https://aka.ms/gen-ai-lesson4-gh) The way your write your prompt to an LLM also matters. A carefully-crafted prompt can achieve a better quality of response. But what exactly do terms like _prompt_ and _prompt engineering_ mean? And how do I improve the prompt _input_ that I send to the LLM? These are the questions we'll try to answer with in this chapter and the next. @@ -39,7 +39,6 @@ Want to get the big picture of what this lesson covers before you dive in? Check ![Illustrated Guide to Prompt Engineering](./images/04-prompt-engineering-sketchnote.png?WT.mc_id=academic-105485-koreyst) - ## Our Startup Now, let's talk about how _this topic_ relates to our startup mission to [bring AI innovation to education](https://educationblog.microsoft.com/2023/06/collaborating-to-bring-ai-innovation-to-education?WT.mc_id=academic-105485-koreyst). 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