AI Answers

When building a RAG system, choosing the right Embedding model is a crucial step, here are my key factors and suggestions to consider when choosing an Embedding model for your reference: Clarify the application scenarios First of all, it is necessary to clarify the specific application scenarios of the RAG system and...

Weak Artificial Intelligence (Narrow AI) is currently the dominant form of AI technology development in our real world. Weak AI is designed and trained to perform a specific, well-defined task with a level of intelligence that may surpass that of humans in that particular domain.

Yesterday Black Myth: Goku was hot, so many people were playing it. Unfortunately, the blogger is dizzy 3D, can not play these games, steam bought Battlefield 5 and so on have been eating dust. Some handicapped party can not pass the level, checked the modifier can modify the value. If you need it, you can try it. Modifier is wind spirit moon shadow...

The most basic ability of the big model is instruction following, with the document: OpenAI o3-mini system manual (in Chinese) uploaded as an attachment to allow DeepSeek-R1 and ChatGPT to write social media blasts respectively (here I used a completely inappropriate prompt...

Semi-supervised learning is an important branch in the field of machine learning, which uses a small amount of labeled data and a large amount of unlabeled data to co-train a model to improve the learning effect and generalization ability.

Generalized Artificial Intelligence (AGI) is intelligent systems that can understand, learn, reason, adapt and create like or even beyond humans on any cognitive task.

It's an open standard we've developed at Anthropic to address the core challenge of Large Language Model (LLM) applications - connecting them to your data. There's no need to build custom integrations for each data source.MCP provides one protocol to connect all data sources: ...

The core of the problem is not deepseek, there is no difference between deepseek and other big model usage techniques, you can learn a lot of usage techniques in AI commands, and regarding deepseek-specific techniques, you need to know that DeepSeek-r1 belongs to the inference model...

AI governance is a comprehensive framework covering technology, ethics, law, and society that effectively guides, manages, and oversees the entire lifecycle of AI systems-from design, development, deployment, and end use. The core goal is not to hinder technological innovation, but to ensure that the development and application of AI technologies begin...

Meta-Learning, or learning how to learn, is an important branch of the machine learning field that focuses on developing learning algorithms that can quickly adapt to new tasks.

Recurrent Neural Network (RNN) is a neural network architecture designed for processing sequential data. Sequential data refers to a collection of data with temporal order or dependencies, such as linguistic text, speech signals, or time series.

Artificial Intelligence Ethics (AI Ethics) is a cross-disciplinary field that examines the ethical principles, values and social responsibilities that should be followed in the development, deployment and use of AI systems.

Self-Attention is a key mechanism in deep learning, originally proposed and widely used in the Transformer architecture. The core idea is to allow the model to simultaneously attend to all positions in the input sequence, and compute each position by weighted aggregation of...

Digital Twin (Digital Twin) is a technology system for mirror modeling, dynamic mapping and full life cycle management of physical entities or complex systems with high precision, high fidelity and real-time two-way interaction in a virtual digital space.

Self-Supervised Learning (SSL) is an emerging learning paradigm in the field of machine learning, where the core idea is to automatically generate supervised signals from unlabeled data and train models to learn useful representations of the data.

Attention Mechanism (Attention Mechanism) is a computational technique that mimics human cognitive processes, initially applied in the field of machine translation, and later becoming an important part of deep learning.

Large Language Model (LLM) is a deep learning system trained on massive text data, with the Transformer architecture at its core. The self-attention mechanism of this architecture can effectively capture long-distance dependencies in language. The model's "large ...

Diffusion Model (Diffusion Model) is a generative model specialized for creating new data samples such as images, audio or text. The core of the model is inspired by the process of diffusion in physics, which simulates the natural diffusion of particles from a region of high concentration to a region of low concentration. In the machine...

Transfer Learning (Transfer Learning) is an important branch in the field of machine learning, the core idea is to apply the knowledge learned from one task or domain to another related but different task or domain.

AI fairness is the interdisciplinary field of ensuring that AI systems treat all individuals and groups in a fair and unbiased manner throughout their design, development, deployment, and operation lifecycle.

Artificial Intelligence Safety (AI Safety) is the cutting-edge interdisciplinary field of ensuring that AI systems, especially those that are increasingly powerful and autonomous, act reliably and predictably throughout their lifecycle in accordance with human intent, without harmful consequences.

Multi-Task Learning (MTL) is not an isolated algorithm, but an intelligent machine learning paradigm.

Supervised learning is one of the most common and basic methods of machine learning, the core idea is to teach computer models how to make predictions or judgments through existing data sets with "correct answers".

The Transformer architecture is a deep learning model designed for processing sequence-to-sequence tasks such as machine translation or text summarization. The core innovation is the complete reliance on self-attention mechanisms, eschewing traditional loops or convolutional structures. Allowing the model to process all elements of a sequence in parallel, large...

Feedforward Neural Network (FNN) is the basic and widely used artificial neural network model. The core feature is that the connections in the network do not form any loops or feedback paths, and the information flows strictly unidirectionally from the input layer to the output layer, after a...

Federated Learning (FL) is an innovative machine learning approach first proposed by a Google research team in 2016 to address challenges in data privacy and distributed computing.

Unsupervised Learning (ULS) is an important branch of machine learning that focuses on processing data sets that are not pre-labeled.

Deep Learning (DL) is a branch of machine learning that centers on the use of multi-layer artificial neural networks to learn and represent complex patterns in data.

Explainable AI (XAI) is an overarching set of programs that encompasses concepts methods technologies and governance frameworks.

Reinforcement learning is an important branch of machine learning that centers on allowing intelligences to autonomously learn how to make optimal decisions to maximize long-term cumulative rewards through continuous interaction with the environment.

Long Short-Term Memory (LSTM) is a recurrent neural network variant specialized in processing sequence data. In the field of artificial intelligence, sequence data is widely used in tasks such as time series prediction, natural language processing and speech recognition.

The Naive Bayes algorithm is a supervised learning algorithm based on Bayes' theorem. Naive Bayes is based on Bayes' theorem. The "naive" part is the assumption that the features are conditionally independent of each other. Simplifying the assumptions greatly reduces the computational complexity and makes the algorithm efficient in practical applications.

Pre-trained Model is a fundamental and powerful technique in the field of Artificial Intelligence, representing machine learning models that are pre-trained on large-scale datasets. Models form a broad knowledge base by processing massive amounts of information and learning generalized patterns and features from the data...

In machine learning, a hyperparameter is a configuration option that is preset manually before model training begins, rather than learned from data. The central role is to control the learning process itself, as if setting a set of operating rules for the algorithm. For example, the learning...

Regularization is a core technique in machine learning and statistics to prevent model overfitting. Regularization controls the degree of fitting by adding a penalty term to the objective function that is related to the complexity of the model. Common forms include L1 and L2 regularization: the L1 produces sparse solutions and applies...

Neural Network (NN) is a computational model inspired by the way neurons work in the biological brain.

Neural Architecture Search (NAS) represents a cutting-edge branch of artificial intelligence that focuses on automating the design of the structure of neural networks.

Model fine-tuning (Fine-tuning) is a specific implementation of transfer learning in machine learning. The core process is based on pre-trained models, which utilize large-scale datasets to learn generic patterns and develop extensive feature extraction capabilities. The fine-tuning phase then introduces task-specific datasets to ...

Generative Adversarial Network (GAN) is a deep learning model proposed by Ian Goodfellow et al. in 2014. The framework implements generative modeling by training two neural networks against each other...

Cross-Validation is a core method for assessing the generalization ability of a model in machine learning.The basic idea is to split the original data into a training set and a test set, and to obtain more reliable performance estimates by rotating the use of different data subsets for training and validation. This approach simulates ...

Conditional Generative Adversarial Network (CGAN) is an important variant of Generative Adversarial Networks (GANs), proposed in 2014 by Mehdi Mirza et al. In contrast to traditional generative adversarial ...

Decision Tree (DT) is a tree-shaped predictive model that simulates the human decision-making process, classifying or predicting data through a series of rules. Each internal node represents a feature test, branches correspond to test results, and leaf nodes store the final decision. This algorithm uses a divide-and-conquer strategy...

Gradient Descent is the core optimization algorithm for solving function minimization. The algorithm determines the direction of descent by calculating the gradient of the function (the vector consisting of the partial derivatives of each), and iteratively updating the parameters according to the rule θ = θ - η - ∇J(θ).

Loss Function (Loss Function) is a core concept in Machine Learning, undertaking the important task of quantifying the prediction error of a model. This function mathematically measures the degree of difference between the model's predicted value and the true value, providing a clear directional guide for model optimization.

Random Search (RS) is a hyperparametric optimization method that finds the optimal configuration by randomly sampling candidate points in the parameter space.

Support Vector Machine (SVM) is a supervised learning algorithm based on statistical learning theory, mainly used for classification and regression analysis. The core objective is to find an optimal decision hyperplane that separates data points of different categories and maximizes the difference between the two...

Convolutional Neural Network (CNN), an artificial neural network specifically designed to process data with a grid structure, has excelled in the field of image and video analysis.

Random Forest (Random Forest) is an integrated learning algorithm that accomplishes machine learning tasks by constructing multiple decision trees and synthesizing their predictions. The algorithm is based on the Bootstrap aggregation idea, where multiple subsets of samples are randomly drawn from the original dataset with putback for each tree...

Grid Search is an automated method for systematically finding optimal hyperparameter combinations in machine learning. This method exhausts all possible parameter combinations by predefining a range of candidate values for each hyperparameter, training the model one by one and evaluating the performance, and ultimately selecting the best-performing hyper...

Logistic Regression is a statistical learning method used to solve binary classification problems. The central goal is to predict the probability that a sample belongs to a particular category based on input features. The model maps the linear output to between 0 and 1 by linearly combining the eigenvalues using an S-shaped function...

K-Means Clustering (K-Means Clustering) is a classical unsupervised machine learning algorithm. It is mainly used to divide a dataset into K disjoint clusters. The goal of the algorithm is to assign n data points to the K clusters so that each data point belongs to the cluster corresponding to its nearest cluster center.

Evaluation Metrics (Evaluation Metrics) is a system of quantitative criteria to measure the performance of machine learning models. It is like a multidimensional physical examination report to comprehensively assess the health status of the human body. In the classification task, Accuracy reflects the overall rate of correct judgment of the model, and Precision (Pr...

Data Augmentation (DA) is a technical approach to expanding training datasets by artificially creating new data.

K-Nearest Neighbors (K-Nearest Neighbors) are instance-based supervised learning algorithms that can be used for classification and regression tasks.