- Artificial General（通用） Intelligence (AGI) represents the hypothetical（假想） ability of an AI system to understand, learn, and apply knowledge across a wide range of tasks at a level of complexity comparable to human intelligence. Unlike narrow AI designed for specific tasks, AGI would possess the versatility and adaptability to perform any intellectual task that a human being can. This includes reasoning, problem-solving, abstract thinking, understanding natural language, and learning from experience. Achieving AGI is considered a monumental milestone in AI research, marking the advent of machines with human-like cognitive abilities.
人工通用智能（AGI）代表了一个AI系统的假想能力，即理解、学习并应用知识去完成广泛的任务，这些任务的复杂度与人类智能相当。与为特定任务设计的狭义人工智能不同，AGI将拥有执行人类可以执行的任何智力任务的多功能性和适应性。这包括推理、解决问题、抽象思考、理解自然语言以及从经验中学习。实现AGI被认为是AI研究的一个重要里程碑，标志着具有类似人类认知能力的机器的到来。
- Supervised learning is a machine learning approach where models are trained on labeled data, meaning each input example is paired with the correct output. The model learns by comparing its predictions to the actual outputs during training, adjusting until it can accurately predict outcomes for unseen data. Unsupervised learning, conversely, involves training models on data without explicit instructions on what to predict. The model identifies patterns and structures within the data autonomously. Supervised learning is typically used for classification and regression tasks, while unsupervised learning is used for clustering, dimensionality reduction, and association rule learning.
监督学习是一种机器学习方法，其中模型是在标注数据上训练的，这意味着每个输入实例都与正确的输出配对。模型通过在训练期间将其预测与实际输出进行比较来学习，并调整直到它能够准确预测未见数据的结果。相反，无监督学习涉及在没有关于应预测什么的明确指导的数据上训练模型。模型自主地识别数据中的模式和结构。监督学习通常用于分类和回归任务，而无监督学习用于聚类、降维和关联规则学习。
- A Multilayer Perception (MLP) is a class of feedforward artificial neural network (ANN) that consists of at least three layers of nodes: an input layer, one or more hidden layers, and an output layer. Each node, except for input nodes, uses a nonlinear activation function. MLP utilizes a supervised learning technique called backpropagation for training. Its architecture of multiple layers and nonlinear processing enables complex pattern recognition and decision-making, making it foundational to deep learning. Deep learning involves networks with many layers (deep architectures) that can learn hierarchical representations of data, significantly advancing capabilities in AI research and applications.
多层感知机（MLP）是一种前馈人工神经网络（ANN），至少包括三层节点：一个输入层、一个或多个隐藏层以及一个输出层。除输入节点外，每个节点都使用非线性激活函数。MLP利用一种称为反向传播的监督学习技术进行训练。它的多层结构和非线性处理使得复杂的模式识别和决策成为可能，是深度学习的基础。深度学习涉及有许多层（深度架构）的网络，这些网络可以学习数据的层次化表示，显著推进了AI研究和应用能力。
- Backpropagation is a cornerstone algorithm for training neural networks, particularly in deep learning. It operates by propagating the error backward from the output layer to the input layer, allowing the algorithm to adjust the weights of connections in order to minimize the error in predictions. This process involves two key phases: forward pass, where input data is fed through the network to generate output predictions, and backward pass, where the gradient of the loss function is computed with respect to each weight by the chain rule, enabling the network to learn from errors systematically. This iterative adjustment refines the model's accuracy over time.
反向传播是训练神经网络，尤其是在深度学习中的基石算法。它通过将错误从输出层反向传播到输入层来运作，允许算法调整连接权重以最小化预测中的错误。这个过程涉及两个关键阶段：前向传播，输入数据通过网络生成输出预测；反向传播，利用链式规则计算损失函数的梯度相对于每个权重，使网络能够系统地从错误中学习。这种迭代调整随着时间提高了模型的准确性。
- Overfitting is a common problem in machine learning, where a model learns the training data too well, including its noise and outliers, rather than generalizing from the pattern it should learn. This results in high accuracy on training data but poor performance on new, unseen data. Essentially, the model becomes overly complex, capturing spurious correlations that do not exist in real-world data. To combat overfitting, techniques such as cross-validation, regularization, and pruning can be used, along with ensuring a sufficient amount of diverse training data. Overfitting highlights the delicate balance between model complexity and its generalization ability.
过拟合是机器学习中的一个常见问题，其中模型过于完美地学习了训练数据，包括其噪音和异常值，而不是从它应该学习的模式中泛化。这导致在训练数据上的高准确度但在新的、未见过的数据上的性能差。本质上，模型变得过于复杂，捕捉到了在现实世界数据中不存在的假相关性。为了对抗过拟合，可以使用交叉验证、正则化和剪枝等技术，同时确保有足够数量的多样化训练数据。过拟合突出了模型复杂度和其泛化能力之间微妙的平衡。
- A Convolutional Neural Network (CNN) is a class of deep learning algorithm that can take in an input image, assign importance (learnable weights and biases) to various aspects or objects in the image, and differentiate from one another. The preprocessing required in a CNN is much lower compared to other classification algorithms. The architecture of a CNN is analogous to that of the connectivity pattern of neurons in the human brain and was inspired by the organization of the visual cortex. CNNs are primarily used in image recognition, image classification, object detection, and similar tasks, leveraging their ability to learn spatial hierarchies of features.
卷积神经网络（CNN）是一种深度学习算法，可以接受输入图像，为图像中的不同方面或对象分配重要性（可学习的权重和偏差），并且彼此区分开来。与其他分类算法相比，CNN所需的预处理要少得多。CNN的架构类似于人脑神经元的连接模式，并且受到视觉皮层组织的启发。CNN主要用于图像识别、图像分类、对象检测以及类似任务，利用它们学习特征的空间层次结构的能力。
- Deep Reinforcement Learning (DRL) combines deep learning and reinforcement learning principles to create systems that can learn to make decisions. Deep learning processes vast amounts of data through neural networks, enabling feature detection and recognition. Reinforcement learning, on the other hand, is about agents learning to make actions in an environment to achieve a goal, guided by rewards or penalties. DRL utilizes deep neural networks to interpret complex, high-dimensional inputs, allowing the agent to learn optimal actions from its experiences, without explicit programming. This approach has led to significant breakthroughs, such as mastering complex games and improving decision-making in robotics and autonomous vehicles.**
深度强化学习（DRL）结合了深度学习和强化学习原理，创造了能够学习做出决策的系统。深度学习通过神经网络处理大量数据，使特征检测和识别成为可能。另一方面，强化学习是关于代理在环境中学习采取行动以实现目标，由奖励或惩罚指导。DRL利用深度神经网络来解释复杂的、高维输入，允许代理从经验中学习最优行动，无需显式编程。这种方法已经在如掌握复杂游戏和改善机器人与自动驾驶车辆的决策制定方面取得了重大突破。
- In deep learning, activation functions are crucial for neural networks to learn complex patterns. They introduce non-linearity into the network, allowing it to model complicated relationships between inputs and outputs that linear equations cannot. Without activation functions, a neural network, regardless of its depth, would behave like a single-layer perceptron, only capable of solving linear problems. Activation functions, such as Sigmoid, ReLU, and Tanh, help decide whether a neuron should be activated or not, determining the output of neural networks based on input features. This enables deep learning models to tackle non-linear problems, like image recognition and natural language processing, effectively.**
在深度学习中，激活函数对于神经网络学习复杂模式至关重要。它们为网络引入非线性，使其能够模拟输入与输出之间的复杂关系，而这是线性方程无法做到的。没有激活函数，无论神经网络的深度如何，都会表现得像一个只能解决线性问题的单层感知机。激活函数，如Sigmoid、ReLU和Tanh，帮助决定一个神经元是否应该被激活，决定了神经网络基于输入特征的输出。这使得深度学习模型能够有效地处理非线性问题，如图像识别和自然语言处理。
- A generative model in artificial intelligence is an approach used to automatically generate new data instances that resemble a given set of data. Unlike discriminative models, which focus on determining the boundary between different classes, generative models learn the underlying distribution of input data, enabling them to produce new examples that could plausibly come from the original dataset. This ability makes generative models especially valuable in tasks such as image and text generation, data augmentation, and unsupervised learning. Popular examples of generative models include Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), which have been applied in creating realistic images, text-to-image synthesis, and more.
在人工智能中，生成模型是一种用于自动生成类似于给定数据集的新数据实例的方法。与专注于确定不同类别之间边界的判别模型不同，生成模型学习输入数据的底层分布，使它们能够生成可能来自原始数据集的新例子。这种能力使得生成模型在图像和文本生成、数据增强和无监督学习等任务中特别有价值。流行的生成模型例子包括生成对抗网络（GANs）和变分自编码器（VAEs），这些已被应用于创造逼真的图像、文本到图像的合成等。
- Transformer is a groundbreaking architecture in deep learning, introduced for dealing with sequential data, notably in natural language processing (NLP). Unlike its predecessors that relied on recurrence or convolutions, the transformer utilizes attention mechanisms to weigh the significance of different words within the input data. This allows it to capture complex dependencies and relationships within the data more effectively. The architecture comprises an encoder to process the input and a decoder for output generation. Transformers have led to significant advancements in tasks such as machine translation, text summarization, and language understanding, serving as the foundation for models like BERT and GPT.
Transformer 是深度学习中的一种开创性架构，专门用于处理顺序数据，特别是在自然语言处理（NLP）中。与依赖于递归或卷积的前身不同，Transformer 利用注意力机制来权衡输入数据中不同单词的重要性。这使它能够更有效地捕捉数据内的复杂依赖关系。该架构包括一个编码器来处理输入和一个解码器来生成输出。Transformer 在机器翻译、文本摘要和语言理解等任务上取得了重大进展，为像BERT和GPT这样的模型提供了基础。
