Artificial intelligence is Artificial Intelligence, the English abbreviation is AI. It is a new technical science that studies and develops theories, methods, technologies and application systems for simulating, extending and expanding human intelligence.
Artificial intelligence is a branch of computer science that attempts to understand the nature of intelligence and produce a new class of intelligent machines that respond in ways similar to human intelligence.
practical application
Machine Vision, Fingerprint Recognition, Face Recognition, Retina Recognition, Iris Recognition, Palmprint Recognition, Expert System, Automatic Planning, Intelligent Search, Theorem Proving, Game, Automatic Programming, Intelligent Control, Robotics, Language and Image Understanding, Genetics programming etc.
subject category
Artificial intelligence is a marginal subject that belongs to the intersection of natural science and social science.
Involved subjects
Philosophy and cognitive science, mathematics, neurophysiology, psychology, computer science, information theory, cybernetics, uncertainty theory
research area
Natural Language Processing, Knowledge Representation, Intelligent Search, Reasoning, Planning, Machine Learning, Knowledge Acquisition, Combinatorial Scheduling Problems, Perception Problems, Pattern Recognition, Logic Programming Soft Computing, Imprecise and Uncertain Management, Artificial Life, Neural Networks, Complex Systems, Genetic Algorithms
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1. Artificial intelligence industry development policy
In recent years, the impact of artificial intelligence on society and economy has become increasingly prominent. Since 2015, my country has included the development and planning of artificial intelligence in national policies many times, gradually establishing the importance of artificial intelligence technology in strategic development. All provinces and cities responded to the call of the central government and launched corresponding local development plans and policies; among the 31 provinces and cities across the country, 19 provinces and cities have issued artificial intelligence plans, and 16 of them have formulated specific industrial-scale development goals.
2. Development Status of Artificial Intelligence Industry
1. Market size
The scale of China's artificial intelligence market continued to grow from 2016 to 2020, and the market scale increased from 15.4 billion yuan in 2016 to 128 billion yuan in 2020, with a compound annual growth rate of 69.79%. As the new infrastructure industry is increasingly valued by the state, the artificial intelligence industry will continue to grow in the future, and is expected to reach 272.9 billion yuan in 2022.
Data source: CIC Consulting, China Business Industry Research Institute
2. Market structure
China's artificial intelligence industry can be divided into four categories according to application fields: decision-making artificial intelligence, visual artificial intelligence, speech and semantic artificial intelligence, and artificial intelligence robots. At present, visual artificial intelligence accounts for the largest proportion, reaching 43.4%. This is followed by decision-making artificial intelligence, voice and semantic artificial intelligence, and artificial intelligence robots, accounting for 20.9%, 18.2%, and 17.4% respectively.
Data source: CIC Consulting, China Business Industry Research Institute
3. Investment and financing situation
From 2016 to 2018, China's artificial intelligence investment and financing showed an increasing trend. Beginning in 2019, the number of investment and financing events in China's artificial intelligence market began to decline, the overall market began to calm down, and the investment amount increased. As of July 2021, there have been 506 investment and financing incidents, with an investment and financing amount of 183.992 billion yuan.
Data source: Compiled by China Business Industry Research Institute
4. Enterprise registration volume
In the past two or three years, the number of registrations of artificial intelligence-related companies has increased rapidly. According to data from Qichacha, after artificial intelligence became a national strategy in 2017, the annual registration volume of relevant enterprises exceeded 10,000 for the first time, and the number of registrations in 2019 has reached 42,600. In 2020, the link value and empowerment value of new artificial intelligence technologies will be more prominent, and the number of registrations for the whole year will increase to 171,000.
Data source: Enterprise Search, China Business Industry Research Institute
3. Key enterprises in the artificial intelligence industry
1. BOE
Founded in April 1993, BOE Technology Group Co., Ltd. (BOE) is a world-leading provider of semiconductor display technology, products and services. Based on the technical foundations of display, sensing, artificial intelligence, and big data accumulated in the development of the display business, BOE (BOE) launched the DSH strategic transformation in 2014, from the original port device business to the smart IoT business and smart medical industry business extend.
In the first three quarters of 2021, BOE achieved operating income of 163.278 billion yuan, a year-on-year increase of 72.05%, and achieved a net profit of 20.015 billion yuan, a year-on-year increase of 708.36%.
2. HKUST Xunfei
HKUST Xunfei Co., Ltd. is a national-level backbone software company specializing in the research of artificial intelligence core technologies such as speech and language, natural language understanding, machine learning reasoning, and autonomous learning, as well as the research and development of artificial intelligence products and the implementation of industrial applications.
In the first three quarters of 2021, HKUST Xunfei achieved revenue of 10.868 billion yuan, a year-on-year increase of 49.2%; realized net profit attributable to the parent company of 729 million yuan, a year-on-year increase of 30.88%.
3. Cambrian
Zhongke Cambrian Technology Co., Ltd. was founded in 2016. Its main business is the research and development, design and sales of artificial intelligence core chips used in various cloud servers, edge computing equipment, and terminal equipment. The company's main products include cloud product line, edge product line, processor IP licensing and software.
In the first three quarters of 2021, Cambrian’s operating income reached 222 million yuan, a year-on-year increase of 40.51%; the net profit loss attributable to the parent was 629 million yuan, a year-on-year decrease of 102.9%.
4. Alibaba
Relying on Alibaba's leading cloud infrastructure, big data and AI engineering capabilities, scenario algorithm technology, and years of industry practice, Ali AI (Ali Lingjie) provides a one-stop cloud-native AI capability system for enterprises and developers. Help improve the efficiency of AI application development, promote the large-scale implementation of AI in the industry, and stimulate business value.
5. Baidu AI
Baidu Artificial Intelligence has fully opened Baidu Brain's leading capabilities, including 335 scene-based capabilities such as speech recognition and text recognition, Flying Paddle Enterprise Edition EasyDL and BML, intelligent dialogue customization platform UNIT, AI learning and training community AI Studio, and the implementation of algorithms and Hardware-in-depth integration of software and hardware integrated product projects, etc. At present, Baidu has ranked first in the country in the number of AI patent applications and grants for four consecutive years, and the Baidu AI Open Platform has become China's leading AI production platform integrating software and hardware. And Baidu's mobile ecosystem is built powerfully driven by such artificial intelligence technology. Driven by artificial intelligence, the three pillars of the mobile ecosystem consisting of Baijiahao, Mini Programs, and hosting pages have grown steadily, and a complete mobile ecosystem integrating content and services has been built.
4. Eight major development trends of artificial intelligence
1、AI-on-5G
In 2022, industrial AI and AI-on-5G IoT applications will become mainstream.
The AI-on-5G Composite Computing Infrastructure provides a high-performance, secure link fabric for the integration of sensors, computing platforms and AI applications, whether on-site, on-premises or in the cloud. Specifically include:
- automotive systems;
- smart space;
- Industry 4.0, such as new automation and robotics systems.
my country's 5G development has achieved a leading edge, and has built more than 819,000 5G base stations, accounting for about 70% of the world; the number of 5G mobile terminal user connections has reached 280 million, accounting for more than 80% of the world; the number of 5G standard essential patent declarations accounts for More than 38%, an increase of nearly 5 percentage points since the first half of 2020, ranking first in the world. The 5G/6G special meeting of the Ministry of Industry and Information Technology stated that it is necessary to continue to promote the rapid and healthy development of 5G.
5G is the accelerator of artificial intelligence, and 5G will also provide new momentum for artificial intelligence. 5G has three core characteristics of large connection, low latency and high bandwidth. These characteristics can further accelerate the development, application and implementation of artificial intelligence technology from different aspects, and promote the intelligent upgrade of the entire supply chain.
2. Generative artificial intelligence
Generative artificial intelligence, or algorithms that evaluate existing data such as text, audio or visual files, focuses on identifying underlying patterns in that data and then replicating that pattern to generate similar content. This algorithm is being gradually improved. As the input data to the model changes and the business outcomes change, the model itself needs to be adjusted. Lack of maintenance can lead to the eventual loss of value of AI algorithms.
Generative AI includes a variety of techniques:
(1) GAN Generative Adversarial Network: The Generative Adversarial Network is two neural networks: a generator and a discriminator, which compete with each other to find the balance between the two networks. The generator network is responsible for generating new data or content similar to the source data. The discriminator network is responsible for distinguishing between source data and generated data in order to identify which data is closer to the original data.
(2) Transformer: Proposed by the paper "Attention is All You Need", it is now the reference model recommended by Google Cloud TPU. The Tensorflow code related to the paper can be obtained from GitHub as part of the Tensor2Tensor package. Harvard's NLP team also implemented a PyTorch-based version and annotated the paper. Transformers like GPT-3, LaMDA, and Wu-Dao model cognitive attention with differential measures of the importance of parts of the input data. They are trained to understand language or images, learn some classification tasks, and generate text or images from massive datasets.
(3) Variational auto-encoder (VAE) is an important type of generative model (generative model), which was proposed by Diederik P.Kingma and Max Welling in 2013
3. Augmented workforce or human-AI hybrid work
The future of work is more about being paired with artificial intelligence in an augmented environment. All repetitive tasks are possible and will be automated.
As AI/ML tools continue to proliferate, so will your productivity
In every industry, there will be an emergence of AI-powered smart tools that help individuals in that industry work efficiently.
4. Cloud Computing and Edge Management in IT
While edge computing is quickly becoming a must-have tool for many businesses, deployment is still in its early stages. Cloud computing and edge-native business processes will become more dominant in IT and more ubiquitous in the business world.
Some believe that AI management will become the responsibility of the IT department. To address edge computing challenges related to manageability, security, and scale, IT departments will turn to cloud-native technologies. For example, as a platform for containerized microservices, Kubernetes has become the main tool for managing edge AI applications at scale.
Those IT departments using Kubernetes in the cloud can use their experience to build their own edge cloud-native management solutions. It is expected that more third-party and related services will be adopted.
5. The application of artificial intelligence in network security
When it comes to cybersecurity, the role of artificial intelligence must be enhanced by automation. 69% of organizations believe that artificial intelligence is a must for dealing with cyber attacks, but this area will need to be upgraded between 2022 and 2032.
- threat detection;
- combat robot;
- endpoint protection;
- default risk protection;
- Service downtime protection.
6. Better and stronger language model
While the continued development of OpenAI's large-scale generatively pretrained Transformer (GPT) model grabs trendy headlines, the approaches of DeepMind, Microsoft Research, and others are also worth watching. Dozens of new startups have emerged around highly evolved large-scale AI language models.
7. The application of artificial intelligence in the metaverse
Metaverse is a term that refers to an environment, more specifically a digital environment, where multiple users can work and play together
New types of apps, smarter digital agents, deep fake humans (robots, actually), all of these await us in the future of the Internet, seemingly metaverse products.
8. The democratization and accessibility of artificial intelligence - low-code / no-code artificial intelligence
One of the major challenges facing organizations today is the lack of experienced AI engineers who can develop the tools and algorithms needed. With the advent of no-code or low-code solutions, this challenge can be addressed by providing simple and intuitive interfaces that can be used to create complex systems on artificial intelligence.
As we accelerate the adoption of AI in business and upgrade AI processes, the way we build products through software engineering will change fundamentally and become more Easily accepted by all, thus distributing some of its value in a more decentralized manner.
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