Source: Wise things
Abstract: The latest map of global ARTIFICIAL intelligence (AI) industry released by China Information and Communications Institute (ICT) reviews the development trend of AI from underlying technologies to vertical applications.
This year, did not hang AI name, are embarrassed to say that he is the flagship machine. Deloitte also predicts that ai chips will be standard in smartphones by 2023. In addition to mobile phones, smart speakers, a major entry point for AI, are also rapidly catching on: 1.65 million units were sold in 2017.
Artificial intelligence (AI), a term full of science fiction, is coming into reality: countries are integrating AI into top-level design, giants are actively deploying ecology, small manufacturers are seeking breakthroughs in various scenarios, and new applications in finance, medical care, public services and other fields are springing up.
In this issue, we recommend the latest global AI industry map released by ICT to review the overall development trend of ai industry chain.
If you want to collect the full report of this article (the most comprehensive AI industry chain map in history), you can download the key words [AI Map] in the navigation bar (public account: Pegasus).
The following is the dry goods presented by the arrangement:
Behind the AI wave
As the PC market and mobile terminal market are gradually saturated, the Internet is embracing the era of artificial intelligence.
The term artificial intelligence (AI) first appeared at the Dartmouth Conference in 1956. Limited by early computational performance and algorithm developments, AI has experienced more than half a century of ups and downs and conceptual changes.
In the 21st century, artificial intelligence has entered a new stage of accelerated growth with the significant improvement of computing power (especially the introduction of Gpus), the rise of cloud computing, machine learning (especially deep learning) becoming more powerful, and the rapid growth of Internet data.
In 2016, “artificial intelligence” became a hot word and became popular overnight as AlphaGo completed the impossible task of traditional algorithms under traditional hardware architecture in the man-machine war (South Korean Go master Lee Se-dol lost to Google’s ARTIFICIAL intelligence AlphaGo).
Humans see the dawn of machine intelligence, and industry sees the cusp of an information revolution.
According to PWC’s analysis, AI will add 14% to global GDP by 2030, or $15.7 trillion ($6.6 trillion from productivity gains and $9.1 trillion from related consumer/business markets).
▲ Major countries accelerate the deployment of ARTIFICIAL intelligence
To this end, governments around the world are integrating AI into the top-level design. Relevant policies are emerging one after another. Tech giants are focusing on the underlying technologies of AI, and the number of mergers and acquisitions is soaring, building an AI ecosystem around their main businesses and accelerating the spread of AI to other businesses. Small and medium-sized enterprises have found living space in various segments and have taken sides, while traditional enterprises have embarked on the road of transformation…
At present, the whole industrial chain of artificial intelligence has basically taken shape.
From a productivity perspective, AI provides deep connectivity at all levels of transportation, health care, education, industry, and other industries, making a qualitative leap in ICT (information and communication technology) capabilities. From the perspective of relevant consumer/business markets, the digitalization, networking and intelligent transformation and upgrading of the real economy are accelerating with the support of AI.
In terms of the activity of AI innovation, scientific research institutions and enterprises are accelerating AI research and innovation, and the number of related patents is increasing exponentially. In 2016, the related investment of tech giants in AI has reached 20 billion to 30 billion US dollars. In 2017, the amount of AI investment and financing showed a blowout. In 2018, domestic AI unicorn startups ushered in the listing opportunity.
In terms of scale, voice, vision and other AI technologies have entered the practical and commercial, smart speakers, intelligent security and other emerging markets are highly sought after. The number of ai enterprises in China is close to 1500. Globally, the number of new AI companies is increasing in Europe and Asia.
Global Industry Map
Ai industrial chain structure is divided into basic layer (computing infrastructure), technology layer (software algorithm and platform) and application layer (industry application and products).
Base layer
The basic layer mainly includes computing hardware (AI chips), computing system technology (cloud computing, big data and 5G communication) and data (data acquisition, annotation and analysis).
Take AI chips for example. As the core hardware of the AI industry, the market size of AI chips is expected to reach 14.616 billion DOLLARS by 2020, accounting for 12.18% of the global ARTIFICIAL intelligence market.
AI chips, also known as AI accelerators or computing cards, are modules dedicated to processing a large number of computing tasks in AI applications (other non-computing tasks are still handled by the CPU), supporting side and side AI computing requirements.
At present, AI chips are mainly divided into GPU, DSP, FPGA, ASIC and brain-like chips, especially the demand for end-deep learning computing platform is rapidly released. Among them, the bright spot enterprises from China have Cambrian, Shenjian technology and so on.
In addition to the support of computing hardware, the rapid growth of global data traffic also provides a good foundation for deep learning relying on AI. Public data sets provide high-quality data for innovation, entrepreneurship and industry competitions, and bring indispensable resources to start-ups.
There is a saying that deep learning is the engine of the rocket of artificial intelligence, the fuel is big data, and cloud computing is the engine.
Technology layer
At the technical level, we can understand it from three dimensions: algorithm theory (machine learning algorithm, brain-like algorithm), development platform (basic open source framework, open technology platform) and application technology (computer vision, natural language understanding and human-computer interaction).
Among them, the development platform can be described as a giant gathering. Taking the platform as the core can not only longitudinally open up the whole industry, but also take the business as the guidance to grab key industries and expand the basic technology, so as to cooperate with their own hardware strategy.
In terms of the basic open source framework, dominant enterprises such as Google, Amazon and Facebook have accelerated the deployment of machine learning and deep learning underlying platforms and established industry fact standards. At present, there are nearly 40 AI learning frameworks in the industry, and ecological competition is extremely fierce.
In terms of open technology platform, typical enterprises such as IFlytek and Sensetime take advantage of their technological advantages to build open technology platform, provide AI development environment for developers and build upper application ecology.
In terms of application technology, artificial intelligence technology represented by speech recognition and machine vision has matured rapidly and reached the practical level (refer to the period 209 of zhiwei for detailed maturity assessment).
In view of the maturity of speech recognition and machine vision, machine vision and intelligent speech have become the AI field with the highest level of AI industrialization, and the number of enterprises and start-ups are growing rapidly.
The application layer
At the application level, we can look at both industry solutions (” AI+ “) and typical products (robots, smart speakers, smart cars, drones, etc.).
First, the industry solutions are mainly “AI+ traditional industries”, which cover many vertical fields such as security, transportation, medical, manufacturing, education, finance, home furnishing and so on.
For the security industry, AI points to intelligent detection, early warning and control, which will bring industry changes; For traffic, AI schemes such as driving mode and traffic optimization will improve the efficiency of urban traffic and change people’s travel patterns. For the consumer market, multi-channel interactive products such as smart speakers and smart homes will change the user model and give birth to new O2O platforms. For industry, AI-directed industrial robots and even unmanned factories will improve the operating environment, increase productivity and reduce costs.
The development of indigenous AI
At present, the United States is still one of the core birthplaces of ARTIFICIAL intelligence, with excellent technology RESEARCH and development institutions, theoretical disciplines and various laboratories. With favorable capital and policies, the AI industry has an optimistic prospect. It is relatively leading in basic algorithm and theoretical research, and other countries are rapidly following the development of ARTIFICIAL intelligence.
China has become one of the world’s AI centers. However, the data environment, talent shortage and the immaturity of intelligent hardware, especially microchips and CPUS, are arguably the biggest challenges facing China’s AI development.
However, under the guidance and supervision of policies, strong market demand, including industrial, commercial and consumer markets, will exert a strong influence on AI technology development and talent cultivation. At present, Beijing leads the country in the development of ARTIFICIAL intelligence, and the development of Shanghai, Guangdong, Jiangsu and Zhejiang is gradually accelerating.
After the explosive development of ARTIFICIAL intelligence in 2017, it did not cool down. The giants made a strong layout, and the major innovative enterprises entered the growth stage one after another. With the support of policy and capital, they accelerated the construction of technical barriers and promoted commercial applications. AI+ security, mobility, industry, finance and other industries are expected to take the lead in growth, the usability of computing chips, pattern recognition (voice and image), natural language understanding will be tested, and new consumption scenarios and business models will be explored.
If you want to collect the full report of this article (the most comprehensive AI industry chain map in history), you can download the key words [AI Map] in the navigation bar (public account: Pegasus).
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