1. What is the definition of artificial intelligence?
Answer: Artificial Intelligence (AI for short) is a technology and science that simulates human intelligence. It involves various fields, including machine learning, natural language processing, computer vision, speech recognition, decision trees, etc. The goal of artificial intelligence is to enable computers to have human-like intelligence and be able to think, learn, reason and make decisions autonomously. Artificial intelligence has a wide range of applications, including smart homes, autonomous driving, medical diagnosis, financial risk control, intelligent customer service, etc. The development of artificial intelligence technology is of great significance to promoting social and economic development and the improvement of human life.
2. What is machine learning?
Answer: Machine learning is a branch of artificial intelligence. It refers to the ability of a computer system to automatically complete certain tasks or improve certain performance by learning and training data. Simply put, machine learning allows computers to automatically learn and improve their abilities based on existing data and experience without explicitly writing instructions. The main purpose of machine learning is to enable computers to make decisions and predictions autonomously, thereby achieving the capabilities of autonomous learning and decision-making. Machine learning has a wide range of applications in various fields, such as natural language processing, computer vision, speech recognition, recommendation systems, financial risk control, medical diagnosis, etc.
3. What is a neural network?
Answer: Neural network is a computational model that simulates the human nervous system. It simulates the interaction and information transmission process between neurons in the human brain. Neural networks can continuously improve their accuracy and precision through training and learning, thereby achieving various complex tasks, such as image recognition, speech recognition, natural language processing, etc. The neural network model is composed of multiple neurons. Each neuron receives multiple input signals, then weights and sums these input signals through an activation function, and finally outputs a result. The training process of neural networks is usually implemented through the back-propagation algorithm, which can automatically adjust the connection weights between neurons, so that the neural network can predict and classify more accurately. Neural networks have extensive applications in the field of artificial intelligence and are the basis of deep learning.
4. What is deep learning?
Answer: Deep learning is a machine learning method that simulates the structure and function of the human nervous system and uses multi-layer neural networks to learn and extract features of data to achieve various intelligent tasks, such as image recognition and speech. Recognition, natural language processing, etc. Compared with traditional machine learning methods, deep learning has stronger adaptive and generalization capabilities, and can automatically learn and discover patterns from large amounts of data, thereby achieving more accurate and efficient prediction and classification. The core of deep learning is the neural network. Through the connections and weight adjustments between multiple layers of neurons, efficient extraction and learning of data features can be achieved. Deep learning has been widely used in image recognition, speech recognition, natural language processing, intelligent recommendation and other fields, and is one of the important research directions in the field of artificial intelligence.
5. What is reinforcement learning?
Answer: Reinforcement learning is an important method in machine learning. It is the process of learning how to make optimal decisions through the interaction between an agent and the environment. In reinforcement learning, an agent achieves optimal control of the environment by observing the state of the environment, performing certain actions, and then adjusting its strategy based on feedback from the environment (reward or punishment). The core of reinforcement learning is learning based on reward signals, that is, the agent tries different actions to maximize the expected reward signals. Reinforcement learning has wide applications in many fields, such as games, robot control, natural language processing, etc. The main advantage of reinforcement learning is that it can learn in unknown environments and can achieve autonomous decision-making and control, with strong real-time and adaptability.
6. What is natural language processing?
Answer: Natural Language Processing (NLP) is an artificial intelligence technology that aims to enable computers to understand, process and generate natural language, so that computers can understand and use language like humans. Natural language processing involves many fields such as linguistics, computer science, mathematics and statistics. Its main tasks include text classification, information extraction, text generation, machine translation, speech recognition, etc. Natural language processing technology has a wide range of applications, such as intelligent customer service, intelligent translation, intelligent search, intelligent writing, etc. The development of natural language processing technology is of great significance to the further development and application of artificial intelligence.
7. What is computer vision?
Answer: Computer Vision is an artificial intelligence technology designed to allow computers to "see" and understand images or videos like humans. Computer vision involves many fields such as image processing, pattern recognition, and machine learning. Its main tasks include image classification, target detection, image segmentation, face recognition, behavior analysis, etc. Computer vision technology has a wide range of applications, such as autonomous driving, intelligent security, medical imaging diagnosis, virtual reality, etc. The development of computer vision technology is of great significance to the further development and application of artificial intelligence.
8. What is data mining?
Answer: Data mining is a process of extracting useful information and knowledge from large amounts of data. It is a multidisciplinary field that involves statistics, machine learning, database technology, data visualization and other fields. The goal of data mining is to discover patterns, regularities, and trends in data in order to better understand the data, make predictions, and make decisions. Data mining technology can be applied to various fields, including business, finance, medical care, social networks, etc. The main tasks of data mining include classification, clustering, association rule mining, anomaly detection, etc. The development and application of data mining technology is of great significance to help people better understand and utilize massive data and improve the accuracy and efficiency of decision-making.
9. What is a recommendation system?
Answer: A recommendation system is an information filtering system that can recommend items or services to users that may be of interest to them based on their historical behavior, interests, and preferences. Recommendation systems are mainly used in e-commerce, social networks, news media, music and video and other fields. The core of the recommendation system is the algorithm model, which can analyze the user's historical behavioral data, such as purchase records, click records, rating records, etc., and then predict the user's interests and preferences based on these data, and finally recommend the most relevant items or services to the user. Recommendation systems can help users discover new products and services, improve user satisfaction and loyalty, and can also help companies increase sales and customer satisfaction.
10. What is a neural language model?
Answer: Neural language model is a natural language processing technology based on neural networks, which can be used to predict the probability distribution of the next word or character in a text sequence. Neural language models usually use deep learning models such as recurrent neural networks or convolutional neural networks. By learning a large amount of language data, they can automatically learn the rules and patterns of language and be able to generate natural language text that conforms to grammatical and semantic rules. Neural language models are widely used in fields such as machine translation, speech recognition, and automatic text generation. In recent years, with the continuous development of deep learning technology, neural language models have become more and more widely used in the field of natural language processing.
11. What is a convolutional neural network?
Answer: Convolutional Neural Network (CNN) is a deep learning model mainly used in the processing of two-dimensional data such as images and videos. Its main feature is to extract image features through convolution operations, then reduce the size of the feature map through pooling operations, and finally perform tasks such as classification or regression through fully connected layers. The convolutional layer and pooling layer of the convolutional neural network can be stacked in multiple layers, thereby increasing the depth of the network and improving the network's expressive ability and performance. Convolutional neural networks are widely used in the field of computer vision, such as image classification, target detection, semantic segmentation and other tasks.
12. What is a recurrent neural network?
Answer: Recurrent Neural Network (RNN) is a deep learning model, mainly used in the processing of sequence data, such as speech, text, etc. Its main feature is that it has a loop structure, which can transfer the information of the previous time step to the current time step when processing the data of each time step, thereby realizing the modeling of sequence data. Each time step of the recurrent neural network has the same neural network structure, but the parameters are shared, which can reduce the number of parameters of the model and improve the training efficiency and generalization ability of the model. Recurrent neural networks are widely used in natural language processing, speech recognition, video analysis and other fields.
13. What is a generative adversarial network?
Answer: Generative Adversarial Networks (GAN) is a deep learning model consisting of a generator and a discriminator. It achieves modeling and generation of data distribution through adversarial learning between the two models. The generator model is responsible for generating data samples that are similar to real data, while the discriminator model is responsible for determining whether the data samples generated by the generator are consistent with real data samples. The two models are trained at the same time. The generator model continuously optimizes the generated data samples to make them closer to the real data, while the discriminator model continuously optimizes its discriminative ability so that it can more accurately determine whether the data samples generated by the generator are Real data samples. Through adversarial learning between the two models, the generator model can gradually learn the characteristics of the real data distribution, thereby generating more realistic data samples. Generative adversarial networks are widely used in image generation, natural language processing, audio generation and other fields.
14. What is transfer learning?
Answer: Transfer learning is a machine learning method that transfers learned knowledge or models to new tasks or fields to accelerate the learning process and improve learning effects. In transfer learning, an already trained model or a pre-trained model is usually used as the basic model, and then fine-tuned or retrained for new tasks or fields to adapt to new data distribution and task requirements. Transfer learning can effectively solve problems such as insufficient data volume and poor model generalization ability, while also saving training time and computing resources. Transfer learning has been widely used in computer vision, natural language processing, speech recognition and other fields.
15. What is model compression?
Answer: Model compression refers to the process of compressing and simplifying deep neural network models through a series of technical means to achieve the purpose of reducing model storage space, accelerating model inference speed, and reducing model calculation complexity. Common model compression techniques include pruning, quantization, distillation, etc. Among them, pruning is to reduce the amount of parameters and calculations of the model by deleting unnecessary connections or neurons in the network; quantization is to convert the model parameters from floating point numbers to low-precision integers or fixed-point numbers, thereby reducing the model storage space and calculation amount; distillation is to approximate the behavior of a large model by learning a small model, thereby reducing model storage space and calculation amount. Model compression technology has been widely used in deep learning applications, which can improve model efficiency and usability without reducing model performance.
16. What is model distillation?
Answer: Model Distillation refers to the process of transferring knowledge from a complex model (teacher model) to a simple model (student model). In this process, the prediction results of the teacher model are used as the training targets of the student model to help the student model learn better representations. Model distillation is commonly used in deep learning, especially when computing resources are limited. Through model distillation, the performance of a complex model can be transferred to a simple model, thereby improving the efficiency and speed of the model. Model distillation can be achieved in many ways, such as temperature-based distillation, attention-based distillation, feature-based distillation, etc. Model distillation is a very useful technique that can help deep learning models achieve efficient predictions under limited computing resources.
17. What is model integration?
Answer: Model Ensemble refers to the technology of combining multiple models to produce more accurate and stable prediction results. Model ensembles are commonly used in machine learning, especially in classification and regression tasks. Model integration can be achieved in a variety of ways, such as voting, averaging, weighted averaging, stacking, etc. In the voting method, multiple models predict the same sample, and then the final prediction result is determined based on the results of the majority vote. In averaging methods, predictions from multiple models are simply averaged. In the weighted average method, the prediction results of each model are assigned a weight and then weighted and averaged to obtain the final prediction result. In the stacking approach, predictions from multiple models are used as input to train a meta-model that can produce more accurate predictions. Model integration can improve the performance and stability of the model and reduce the risk of over-fitting and under-fitting, thus having a wide range of applications in practical applications.
18. What is semi-supervised learning?
Answer: Semi-supervised learning is a machine learning technique that combines the characteristics of supervised and unsupervised learning. In semi-supervised learning, the algorithm uses labeled and unlabeled data to train the model. Since labeled data is typically much less available than unlabeled data, semi-supervised learning can improve model performance with relatively less labeled data. Semi-supervised learning is commonly used in tasks such as classification, clustering, and dimensionality reduction. In semi-supervised learning, algorithms use labeled data to learn the parameters of a model and unlabeled data to discover patterns and structures in the data. Typically, unlabeled data can be processed through unsupervised learning techniques such as clustering or dimensionality reduction. Semi-supervised learning is a flexible technique that can be applied to many practical problems.
19. What is unsupervised learning?
Answer: Unsupervised learning is a machine learning technique that discovers hidden structures and patterns from unlabeled data without the help of any labeled data. This technique is commonly used for tasks such as clustering, dimensionality reduction, and anomaly detection. In unsupervised learning, algorithms automatically identify patterns and regularities in data and generate a model that can be used to predict new data. Unlike supervised learning, unsupervised learning does not require knowing the categories or labels of the data in advance, so it is suitable for many practical problems, such as image and speech processing, and natural language processing.