1. 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.


  1. 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.


  1. 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.


  1. 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.


  1. 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.


  1. 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.


  1. 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.**


  1. 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.**


  1. 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.


  1. 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。