AI Business Overview - AI Technical Framework
According to the industrial ecology, the technical framework of artificial intelligence can usually be divided into three major sections: the basic layer, the technical layer, and the application layer. Among them, the basic layer provides infrastructure and technologies supporting artificial intelligence applications, including the ability to store and process large-scale data, as well as high-performance computing and communication infrastructure; the technical layer provides various artificial intelligence technologies and algorithms for Process and analyze data, and extract useful information and knowledge; the application layer is the final application field of artificial intelligence technology, which applies the algorithms and models provided by the technology layer to specific problems and scenarios to achieve intelligent decision-making and optimization. The following figure describes the technical framework of artificial intelligence:
This division of the basic layer, technology layer, and application layer can help people understand the hierarchical structure and application process of the artificial intelligence ecosystem, from infrastructure to technical tools to final applications, and gradually explain the resources and conditions required to realize artificial intelligence. Among them, computing power, data, and algorithms are the most important three elements. Below we detail the technologies and resources involved in each layer.
1. Three core elements of AI
Data, computing power, and algorithms are the three core elements of the development of artificial intelligence. Every rapid development of artificial intelligence technology benefits from major technological breakthroughs in these three core elements. These three core technical elements are the core driving force for the continuous development of artificial intelligence, which promotes the technical iteration and commercialization of artificial intelligence.
"Data" - the fuel of AI
Data provides learning materials and training basis, large-scale, high-quality data is crucial for the training and optimization of algorithms such as machine learning and deep learning. Data can come from multiple sources, including structured data (such as databases), unstructured data (such as text, images, audio, and video), and data generated in real time (such as sensor data). Through data processing processes such as collection, cleaning, transformation and storage, data can be made more useful and reliable. At the same time, the diversity of data has also played a role in promoting the development of artificial intelligence. Through the combination and analysis of multi-source data, more comprehensive information can be obtained and the accuracy and predictive ability of artificial intelligence systems can be improved.
"Computing power" - the infrastructure of AI
Computing power is the ability of a computer system to handle complex computing tasks and large-scale data. As the complexity of artificial intelligence tasks continues to increase, so does the need for efficient computing power. In particular, computing-intensive tasks such as deep learning require a large number of matrix operations and neural network model training, which puts forward higher requirements for computing power. To meet this demand, the application of specially designed hardware devices such as graphics processing units (GPUs) and dedicated AI chips is becoming more and more widespread. These hardware devices have parallel computing capabilities and high-performance computing, which can greatly improve computing speed and efficiency, and accelerate the processing of artificial intelligence tasks.
"Algorithm" — the engine of AI
Algorithm is a mathematical model to realize intelligent decision-making and prediction. The field of artificial intelligence covers a variety of algorithms, such as machine learning, deep learning, and reinforcement learning. These algorithms work by training a model so that it learns from data and makes predictions or decisions. With the continuous innovation and improvement of the algorithm, remarkable results have been achieved in the fields of speech recognition, image processing, natural language processing, etc., and it provides a basis for realizing higher-level artificial intelligence capabilities.
To sum up, as the scale of AI large models continues to expand, the demand for computing resources is also increasing. High-performance hardware devices, massive scene data, strong computing power foundation, and upgraded and iterative algorithm models have become the key to supporting the development of AI large models.
2. AI base layer
The AI base layer provides the most basic, basic, and bottom-level business services for artificial intelligence systems, including computing power and data among the three core elements of artificial intelligence, as well as platform software running on hardware resources. That is, it corresponds to the basic hardware, data resources, and platform software in the above figure.
Basic hardware Basic
hardware refers to the hardware device resources required to support the operation of artificial intelligence systems, such as AI chips, storage devices, sensors and network devices. Among them, the AI chip is the most important hardware resource, which provides "computing power" for the artificial intelligence system. Since artificial intelligence systems use large-scale data training and complex artificial neural network algorithms, hardware processing chips need to have powerful parallel computing capabilities to accelerate training and reasoning processes. At the basic level of hardware computing power, the main goal is to improve computing efficiency, reduce energy consumption, and create a hardware architecture suitable for artificial intelligence computing.
AI chips are chips specially designed to handle artificial intelligence tasks, with efficient computing and reasoning capabilities. The current mainstream hardware computing power solutions include:
NVIDIA GPU (Graphics Processing Unit): It is one of the most mainstream and widely used solutions at present. It greatly improves the neural network model through powerful parallel computing capabilities and high-speed storage bandwidth. The speed of training and inference; has played an important role in the field of artificial intelligence. NVIDIA provides GPU architectures and development tools optimized for AI tasks, such as the latest Ampere architecture and CUDA programming environment. Chip models such as: A100, A800, H100, V100, RTX 30 series (such as RTX 3080, RTX 3090)
Google TPU (Tensor Processing Unit): It is an ASIC designed by Google to accelerate artificial intelligence tasks ( application specific integrated circuit) chips. Through customized hardware design, TPU has excellent performance in terms of energy efficiency ratio and performance, and is suitable for various machine learning and deep learning workloads. Such as TPUv1, TPUv2, TPUv3
In addition to the above-mentioned AI chips, there are many AI chips from other companies, such as Intel (NNP chips), Huawei (Ascend chips), Apple (A-series chips), ARM (Ethos chips), etc., which have also invested a lot of resources in research and development. And promote artificial intelligence chips. These mainstream AI chips are optimized and customized for artificial intelligence tasks to varying degrees, providing high-performance, energy-efficient computing and reasoning capabilities, and further promoting the development and application of artificial intelligence technology.
Data resources
Data is the fuel of artificial intelligence, and the quality of training and feeding data directly determines the performance indicators of the artificial intelligence system. AI systems for different purposes require different types of data for training. For example, general data is generally used to train general knowledge systems, and industry data is used to train industry knowledge systems. The training data also needs to confirm whether and how to label according to the specific algorithm. In artificial intelligence systems, the processing and application of data is also a very complex task, and the technologies involved are: In artificial intelligence, the processing and application of data involves some technologies and knowledge. The following are some of the important technologies and knowledge: data collection and cleaning, data storage and management, data preprocessing and feature engineering, data annotation and annotation, data visualization and analysis, privacy and security protection, etc. Data processing in intelligence plays a key role. Through the rational use and integration of these technologies and knowledge, data can be better processed and applied to provide effective training and decision-making basis for artificial intelligence systems.
The platform software
has data resources and hardware resources, but also needs to integrate data and hardware resources through the platform software, and efficiently use the hardware resources to complete the training and reasoning of the data. The platform software involved includes: specific types of operating systems, database processing software , cloud computing, and big data platforms.
3. AI technology layer
The technical layer is located on top of the basic layer, providing various artificial intelligence technologies and algorithms for processing and analyzing data, and extracting useful information and knowledge. It mainly includes AI framework, AI algorithm and application algorithm.
AI framework
AI framework is the software basic framework for realizing artificial intelligence business. It uses AI algorithm to complete the construction of the overall business framework. There are completely open-source basic frameworks, such as TensorFlow, PyTorch, Transformer, GLM, etc.; there are also private AI developments that are not open-source Frameworks, such as Caffe and CNTK; there are also some semi-open source AI frameworks, which are partially open source, some core components or basic functions are open, but may also contain some additional proprietary components or extensions, or part of the entire framework is Open source: such as Keras, MXNet, GPT, etc.
Depending on the open source of the AI framework, there are two different ways to develop AI, namely development based on the open source framework and development based on the online framework API. Development based on the open source framework is based on the published open source framework system for development and training. Due to the open source code, developers can freely view, modify and customize the system to suit specific needs and tasks. The API development model based on the online framework is based on large-scale machine learning or deep learning models deployed in the cloud, accessed and used through interfaces or APIs. The advantage is that developers do not need to pay attention to the underlying hardware and software architecture, and only need to request through the network The prediction result of the system can be obtained. Both development modes have their own advantages, and developers can choose according to specific business needs.
AI algorithm
AI algorithm is a data calculation method that can specifically realize artificial intelligence business, such as machine learning algorithm, deep learning algorithm, artificial neural network algorithm, etc. It and the AI framework jointly complete the tasks of data training, optimization and reasoning , such as the current mainstream generative pre-training model is an AI algorithm, which also includes three modeling methods, such as self-encoding model, auto-regressive model, encoding-decoding model, corresponding to BERT, GPT and Transformer models.
Applying Algorithms
AI application algorithms are business computing based on AI frameworks and algorithms involving specific application fields, involving computer vision, speech recognition, natural language processing, etc.
Computer Vision refers to the process of enabling computers to simulate and understand human vision through image and video data. It involves the use of computer algorithms and techniques to process, analyze, and understand images, videos, and other visual inputs to achieve a series of visual tasks such as recognition, detection, classification, and tracking.
Speech Recognition (Speech Recognition) is a technology that converts speech signals into text or other operable forms. It works to enable computers to understand and process human spoken language. The technologies involved include: speech signal preprocessing, feature extraction, acoustic model, language model, speech synthesis, voiceprint recognition, etc.
Natural Language Processing (NLP) is a discipline that studies how computers understand, process, and generate human language. It involves the analysis, understanding and application of text data through computer technology, such as natural language understanding NLU technology and natural language generation NLG technology.
4. AI application layer
The AI technology layer provides text, audio, image, video, code, strategy, and multi-modal understanding and generation capabilities, and can be specifically applied to finance, e-commerce, media, education, games, medical care, industry, government affairs, etc. through the application layer Provide products and services for enterprise-level users, government agency users, and mass consumer users in multiple fields.
The application layer is the final application field of artificial intelligence technology, which applies the algorithms and models provided by the technology layer to specific problems and scenarios to achieve intelligent decision-making and optimization. At this layer, artificial intelligence is integrated into various application fields, including natural language processing, computer vision, speech recognition, intelligent recommendation, unmanned driving, etc., which can empower various industries and realize Business intelligence improves work efficiency and quality.
The mainstream solutions of the application layer will vary according to the specific application fields. For example, in natural language processing, mainstream solutions include text classification, sentiment analysis, machine translation, etc.; in computer vision, mainstream solutions include image recognition, object detection, image generation, etc. These applications apply artificial intelligence technology to practical problems by utilizing the tools and models provided by the technical layer, and provide users with intelligent services and experiences.
5 Conclusion
The above-mentioned three-layer technical framework of artificial intelligence is intertwined and closely related, and the functions and roles of each layer also overlap and interact. In practical applications, it also needs to be customized and integrated according to specific needs to form a complete artificial intelligence solution.