GPT & AI 相关的片段记录

记录和整理与 GPT & AI 相关的片段，以方便自己分享和回顾。其中部分内容由向 ChatGPT 提问而生成并翻译。

📌 钉住的页面

• GPT - Produet Hunt - https://www.oroduethunt.com/search?a=GPT
• GPT • GitHub - https://github.com/search?o=desc&q=GPT3&s=stars&type=Repositories
• 500+ ChatGPT and GPT-3 Examples - https://gpt3demo.com/

How ChatGPT actually works

https://www.assemblyai.com/blog/how-chatgpt-actually-works/

Large Language Models, such as GPT-3, are trained on vast amounts of text data from the internet and are capable of generating human-like text, but they may not always produce output that is consistent with human expectations or desirable values. In fact, their objective function is a probability distribution over word sequences (or token sequences) that allows them to predict what the next word is in a sequence (more details on this below).

大型语言模型（例如GPT-3）是通过对互联网上的大量文本数据进行训练而产生的，它们能够生成类似于人类的文本，但它们可能不总是产生符合人类期望或理想价值的输出。事实上，它们的目标函数是一个概率分布，用于预测序列中下一个单词（或标记序列）是什么（有关此更多详细信息请参见下文）。

In practical applications, however, these models are intended to perform some form of valuable cognitive work, and there is a clear divergence between the way these models are trained and the way we would like to use them. Even though a machine calculated statistical distribution of word sequences might be, mathematically speaking, a very effective choice to model language, we as humans generate language by choosing text sequences that are best for the given situation, using our background knowledge and common sense to guide this process. This can be a problem when language models are used in applications that require a high degree of trust or reliability, such as dialogue systems or intelligent personal assistants.

然而，在实际应用中，这些模型旨在执行某种有价值的认知工作，而这些模型的训练方式与我们希望使用它们的方式存在明显的分歧。尽管机器计算的单词序列的统计分布在数学上可能是模拟语言的非常有效的选择，但我们作为人类通过选择最适合特定情况的文本序列来生成语言，使用我们的背景知识和常识来指导这个过程。当语言模型用于需要高度信任或可靠性的应用程序（如对话系统或智能个人助理）时，这可能会成为一个问题。

While these powerful, complex models trained on huge amounts of data have become extremely capable in the last few years, when used in production systems to make human lives easier they often fall short of this potential. The alignment problem in Large Language Models typically manifests as:虽然这些经过大量数据训练的强大、复杂的模型在过去几年中变得非常有能力，但当它们用于生产系统以使人类生活更加便利时，它们经常无法充分发挥潜力。大型语言模型中的对齐问题通常表现为：

• Lack of helpfulness: not following the user's explicit instructions.缺乏帮助性：不遵循用户明确的指示。
• Hallucinations: model making up unexisting or wrong facts.幻觉：制造不存在或错误的事实的模型。
• Lack of interpretability: it is difficult for humans to understand how the model arrived at a particular decision or prediction.缺乏可解释性：人类难以理解模型是如何得出特定的决策或预测的。
• Generating biased or toxic output: a language model that is trained on biased/toxic data may reproduce that in its output, even if it was not explicitly instructed to do so.生成偏见或有毒输出：训练于偏见/有毒数据的语言模型可能会在其输出中复制这些内容，即使它没有明确被指示这样做。

Next-token-prediction and  masked-language-modeling  are the core techniques used for training language models, such as transformers. In the first approach, the model is given a sequence of words (or “tokens”, i.e. parts of words) as input and is asked to predict the next word in the sequence. For example, if the model is given the input sentence

Next-token-prediction 和 masked-language-modeling 是训练语言模型（如transformers）所使用的核心技术。在第一种方法中，模型会被给予一个单词序列（或“标记”，即单词的部分）作为输入，并被要求预测序列中的下一个单词。例如，如果模型被给予输入句子

What is GPT-3

GPT-3, or Generative Pre-trained Transformer 3, is a state-of-the-art language model developed by OpenAI. It's the third iteration in the GPT series and represents a significant advancement in natural language processing and generation. GPT-3 has 175 billion parameters, making it one of the largest language models ever created at the time of its release. It can understand and generate human-like text in various styles and tones, perform language translation, answer questions, generate code, and much more. GPT-3 has been widely used across a range of applications, from chatbots and virtual assistants to content generation and creative writing.

GPT-3，即生成式预训练转换器3，是由OpenAI开发的最新一代语言模型。它是GPT系列的第三个版本，代表了自然语言处理和生成方面的重大进步。GPT-3拥有1750亿个参数，在发布时是有史以来创建的最大语言模型之一。它能够理解和生成各种风格和语调的人类文本，进行语言翻译，回答问题，生成代码等等。GPT-3已广泛应用于各种应用领域，从聊天机器人和虚拟助手到内容生成和创意写作。

Write a map of technology of AI

Here's a map of AI technology, highlighting its various components and their interconnections:

1. Foundational Techniques:
   • Machine Learning (ML):
     • Supervised Learning
     • Unsupervised Learning
     • Reinforcement Learning
   • Deep Learning (DL):
     • Neural Networks
     • Convolutional Neural Networks (CNNs)
     • Recurrent Neural Networks (RNNs)
     • Transformer Models
2. Core Application Areas:
   • Natural Language Processing (NLP):
     • Language Models
     • Named Entity Recognition (NER)
     • Sentiment Analysis
     • Machine Translation
   • Computer Vision (CV):
     • Object Detection
     • Image Classification
     • Semantic Segmentation
     • Image Generation
   • Speech Recognition and Synthesis
   • Robotics and Automation:
     • Perception
     • Motion Planning
     • Control Systems
3. Advanced Techniques:
   • Generative Models:
     • Generative Adversarial Networks (GANs)
     • Variational Autoencoders (VAEs)
     • Flow-Based Models
   • Reinforcement Learning (RL):
     • Policy Gradient Methods
     • Value-Based Methods
     • Actor-Critic Methods
   • Meta-Learning and Transfer Learning
   • Model Compression and Optimization
4. Supporting Infrastructure:
   • Data Management:
     • Data Collection
     • Data Cleaning
     • Data Labeling
     • Data Augmentation
   • Hardware Acceleration:
     • GPUs (Graphics Processing Units)
     • TPUs (Tensor Processing Units)
     • ASICs (Application-Specific Integrated Circuits)
   • Development Tools and Frameworks:
     • TensorFlow
     • PyTorch
     • Keras
     • scikit-learn
5. Ethics and Governance:
   • Fairness and Bias Mitigation
   • Explainability and Interpretability
   • Privacy and Security
   • Regulation and Policy

This map provides a structured overview of AI technology, covering foundational theories, practical applications, advanced methodologies, supporting infrastructure, and ethical considerations. Each area represents a crucial aspect of AI development and deployment, contributing to its growth and impact across various domains.

以下是人工智能技术的地图，突出显示了各个组成部分及其之间的联系：

1. 基础技术：
   • 机器学习 (ML)：
     • 监督学习
     • 无监督学习
     • 强化学习
   • 深度学习 (DL)：
     • 神经网络
     • 卷积神经网络 (CNNs)
     • 循环神经网络 (RNNs)
     • Transformer 模型
2. 核心应用领域：
   • 自然语言处理 (NLP)：
     • 语言模型
     • 命名实体识别 (NER)
     • 情感分析
     • 机器翻译
   • 计算机视觉 (CV)：
     • 目标检测
     • 图像分类
     • 语义分割
     • 图像生成
   • 语音识别和合成
   • 机器人技术与自动化：
     • 感知
     • 运动规划
     • 控制系统
3. 高级技术：
   • 生成模型：
     • 生成对抗网络 (GANs)
     • 变分自编码器 (VAEs)
     • 流模型
   • 强化学习 (RL)：
     • 策略梯度方法
     • 基于值的方法
     • 演员-评论家方法
   • 元学习和迁移学习
   • 模型压缩和优化
4. 支持基础设施：
   • 数据管理：
     • 数据收集
     • 数据清理
     • 数据标注
     • 数据增强
   • 硬件加速：
     • GPU (图形处理器)
     • TPU (张量处理器)
     • ASIC (专用集成电路)
   • 开发工具和框架：
     • TensorFlow
     • PyTorch
     • Keras
     • scikit-learn
5. 伦理与治理：
   • 公平性和偏差调整
   • 可解释性和解释性
   • 隐私和安全
   • 监管与政策

这张地图提供了人工智能技术的结构化概述，涵盖了基础理论、实际应用、高级方法、支持基础设施以及伦理考量。每个领域代表了人工智能发展和应用的关键方面，对各种领域的增长和影响做出了贡献。

What are large language models

Large language models are advanced artificial intelligence systems trained on vast amounts of text data, comprising millions or even billions of parameters. They can understand and generate human-like text across various tasks such as translation, summarization, question answering, and more. These models have wide applications but also raise ethical and societal concerns.

大型语言模型是先进的人工智能系统，它们在海量文本数据上进行训练，包含数百万甚至数十亿个参数。它们可以理解和生成类似于人类的文本，在翻译、摘要、问答等各种任务中发挥作用。这些模型具有广泛的应用，但也引起了伦理和社会问题的关注。

What are parameters in Al area

In the field of artificial intelligence, particularly in machine learning and deep learning, parameters are the internal variables that a model uses to make predictions or generate output based on input data. These parameters are adjusted or learned during the training process by optimizing a certain objective function, typically to minimize error or loss.

In a neural network, for example, parameters include:

1. Weights: These are the coefficients that multiply the input values at each neuron in the network. They determine the strength of the connection between neurons.
2. Biases: Biases are added to the weighted sum of inputs at each neuron to introduce flexibility and enable the model to better fit the data.

The values of these parameters are initially set randomly and then updated iteratively during training using optimization algorithms such as gradient descent. The goal is to adjust these parameters to minimize the difference between the model's predictions and the actual target values in the training data.

The number of parameters in a model can vary significantly depending on its architecture and complexity. In deep learning models, which often have many layers and neurons, the number of parameters can be substantial, sometimes reaching millions or even billions.

在人工智能领域，特别是机器学习和深度学习中，参数是模型用来根据输入数据进行预测或生成输出的内部变量。这些参数通过优化某个目标函数（通常是最小化误差或损失）在训练过程中进行调整或学习。

例如，在神经网络中，参数包括：

1. 权重：这些系数将网络中每个神经元的输入值相乘。它们确定神经元之间连接的强度。
2. 偏置项：偏置项被添加到每个神经元输入的加权总和上，以引入灵活性并使模型更好地适应数据。

这些参数的值最初随机设置，然后使用梯度下降等优化算法在训练期间迭代更新。目标是调整这些参数以尽可能减少模型预测与训练数据实际目标值之间的差异。

模型中的参数数量可以根据其架构和复杂性而有很大差异。在深度学习模型中，通常具有多层和多个神经元时，参数数量可能会非常庞大，有时甚至达到数百万甚至数十亿。

What is fine-tuning

Fine-tuning in machine learning and deep learning refers to the process of taking a pre-trained model and further training it on a specific task or dataset to adapt it for that particular task. Instead of training a model from scratch, which can be computationally expensive and time-consuming, fine-tuning leverages the knowledge learned by the pre-trained model on a large dataset and adjusts its parameters to specialize in a new task or domain.

The steps involved in fine-tuning typically include:

1. Pre-training: Initially, a model is trained on a large and diverse dataset to learn general patterns and features of the data. This pre-training phase often involves tasks like language modeling or image classification.
2. Transfer Learning: Once the pre-training is complete, the knowledge learned by the model is transferred to a new task. The parameters of the pre-trained model are used as the starting point for training on the new dataset.
3. Fine-tuning: The model is further trained on the new dataset, typically with a smaller learning rate, to adapt its parameters to the specifics of the new task. This fine-tuning process helps the model to generalize better and achieve higher performance on the target task.

Fine-tuning is especially useful when working with limited data or computational resources, as it allows practitioners to benefit from the expertise captured by pre-trained models without needing to train them from scratch. It's commonly used in various domains such as natural language processing, computer vision, and speech recognition.

机器学习和深度学习中的微调是指将预训练模型进一步训练以适应特定任务或数据集的过程，从而使其适用于该特定任务。与从头开始训练模型相比，微调利用了预先训练模型在大规模数据集上所学到的知识，并调整其参数以专门针对新任务或领域。

微调通常涉及以下步骤：

1. 预先训练：最初，在大量不同类型的数据集上对一个模型进行训练，以了解数据的一般规律和特征。这个预先训练阶段通常包括语言建模或图像分类等任务。
2. 迁移学习：完成预先训练后，将所学到的知识转移到新任务中。使用预先训练好的模型参数作为在新数据集上进行培训时起点。
3. 微调：在新数据集上进一步对该模型进行培养，通常采用较小的学习率来适应其参数到新任务具体要求。这个微调过程有助于提高该模型泛化能力并实现更高性能目标。

当处理有限数量数据或计算资源时，微调尤为有用，因为它可以使从未经过完全重新培养就可以受益于已经捕获到得专业技术水平。它广泛应用于自然语言处理、计算机视觉和语音识别等各种领域。

What is RAG in AI area

In the field of artificial intelligence, RAG stands for "Retrieval-Augmented Generation." RAG is a framework that combines both retrieval-based and generation-based approaches to natural language processing tasks.

In RAG, a retrieval mechanism is used to select relevant information from a large corpus of text, typically using methods like sparse vector retrieval or dense vector retrieval (such as using embeddings like BERT or RoBERTa). This retrieved information is then used to augment the generation process, where a generative model (such as a language model like GPT) produces a response or output based on the retrieved information as well as the input provided to the system.

RAG models have been shown to improve performance in various natural language understanding and generation tasks, such as question answering, dialogue systems, and text summarization, by leveraging both the benefits of retrieval-based methods (which can provide accurate and relevant information) and generation-based methods (which can produce fluent and diverse responses).

在人工智能领域，RAG代表“检索增强生成”。 RAG是一个框架，将基于检索和基于生成的方法结合起来应用于自然语言处理任务。

在RAG中，使用检索机制从大量文本语料库中选择相关信息，通常使用稀疏向量检索或密集向量检索等方法（例如使用BERT或RoBERTa这样的嵌入）。 然后利用此获取的信息来增强生成过程，在此过程中，生成模型（例如像GPT这样的语言模型）根据提供给系统的输入以及所获取到的信息产生响应或输出。

已经证明了RAG模型可以通过利用基于检索方法（可以提供准确和相关信息）和基于生成方法（可以产生流畅且多样化响应）两者优势来改善各种自然语言理解和生成任务性能。 这些任务包括问答、对话系统和文本摘要等。