1. 'AI chip' is still in the grasshopper era, rushing in the 'bubble' to be squeezed;
Artificial intelligence is changing all walks of life, and chips are the carrier of artificial intelligence.
In 2018, capital's enthusiasm for semiconductor chips was completely ignited by AI technology. Whether it is a giant company or a startup company, a traditional manufacturing company or an Internet company, the enthusiasm for chips is high. From May to July, Yun Zhisheng, go out and ask, Rokid, Baidu has released AI chips or chip modules, and Spirit confirmed that it is building AI voice chips. Shenjian Technology announced that its AI chips will be available in the second half of this year. Yun Zhisheng founder and CEO Huang Wei even used 'No. Do the chip, mortal' to express your determination to make AI chips.
However, 'until now, there has not been a real AI chip in the world, because the real artificial intelligence is far from being realized.' Zhou Bin, general manager of Heterogeneous Intelligence China, represents the views of some insiders.
"IT Times" reporter interviewed a large number of people in the AI industry and found that for the concept of AI chip, there is no unified consensus at present, and even some investors believe that there is a bubble in the field of AI chips, and most startup companies will disappear.
But in any case, in the age of AI chips in the grass-roots era, there will always be people who have made a " bloody road" for the localization of China's core technology.
Different definitions of AI chips
'Start-ups are probably not worth the AI chip. ' Li Yangyuan, the founder of the chip company Suzhou Mindray Microsystems, has been immersed in the industry for more than a decade. He told the IT Times reporter that a chip from 0 to 1 would take more than 10 years. Cycle, his own chip products are also officially commercialized after 5 times of streaming, and can be placed in the top position in the industry 20 times. If the company develops its own AI chip and adopts 40nm (nano) process, the cost may rise. It is not a reduction. The chip must share the R&D cost by scale. The 40nm process only costs up to 10 million yuan, and allocates it to 1 million PCS (a certain number of product units). The average cost per piece is as high as 10 yuan, which does not include a higher amount. R&D expenses. '
However, Zhu Bin, the head of Rokid's platform research and development, is exactly the opposite of Li Yangyuan. 'Smart devices use general-purpose chips to kill chickens. Special needs require special chips to solve the pain points. Custom AI chips are precisely reducing costs. Artificial intelligence hardware is calculating. There is demand, the low-end general-purpose chip is not enough, and the high-end general-purpose chip has many redundant designs, resulting in high power consumption.
AI chips are also known as AI accelerators or computing cards. In general, AI chips are modules that are used to handle a large number of computing tasks in artificial intelligence applications (other non-computing tasks are still handled by the CPU). Currently, they are mainly divided into GPUs. , FPGA, ASIC and other types, unlike general-purpose chips such as Qualcomm Xiaolong, AI chips are mainly used to handle special tasks, such as the identification of high-definition video in security, data calculation during automatic driving, and so on.
On June 26th, Rokid released a KAMINO18 self-developed AI voice dedicated chip that can support smart speakers and children's story machines. It is different from traditional general-purpose chips such as Intel and Qualcomm. This is an SoC (system-on-a-chip) dedicated to AI voice. The chip, which inherits several core components such as ARM, NPU, DSP, DDR, DAC, etc., is about the same size as a one-dollar coin. Zhu Bin used to design the hardware on the wake-up of the voice, and found many functions in the general-purpose chip. Not enough, but the cost of the attachment still exists. So, Rokid began to customize its own chip in September last year, using DSA (Domain specific architecture) architecture, from the product and algorithm requirements, integrated into the chip by means of heterogeneous computing. In the whole working state, the power consumption of the product can be reduced by 30% to 50%.
The different views of Li Yangyuan and Zhu Bin represent the two different perceptions of AI chips in the traditional semiconductor industry and Internet startups. The people born in the semiconductor circle value the breakthrough of the chip from 0 to 1, and 'from soft to hard' Internet entrepreneurs are more eager to become directly on the basis of 1, 2, 3, 4, through algorithms and software design. From the perspective of the currently released AI chips, their main purpose is to Power consumption to speed up some kind of machine learning algorithms. For example, when used on edge-side and terminal-side devices, it requires extremely low power consumption and extremely high matrix/floating point computing power, which is difficult for general-purpose chips. .
It can be proved by the explanation of the engineer of another leading chip manufacturer. In his view, most AI voice chips on the market belong to a chip that serves 'proprietary' functions (similar to DSP data signal processing). Because it focuses on one or several functions and is limited to a specific scenario, the complexity of design and production is lower than that of a general-purpose chip. In addition, such chips rarely involve technical authorization, and it is easier to do. The AI chip does not start from the bottom layer, but directly from the SoC or general-purpose processor plug-in architecture optimization such as 'coprocessor', through the combination of various IP to accelerate the scene of the upper application, such as language, image.
Heterogeneous
At present, there is no clear definition of AI chips, so how to calculate 'real' is not a good measure. Not long ago, at the 2018 Moving Point International Summit (Hangzhou), Zhou Bin, general manager of Silicon Valley startup heterogeneous smart China, tried to give AI chips. Under the definition, 'the core algorithm of artificial intelligence can be completed with high efficiency and high performance. Since the mainstream algorithm is deep learning now, it means that AI chips must have very good support for deep learning.' From the data point of view, Zhou Bin believes that the computing power of AI chips must exceed 5 trillion times per second, because only by achieving such performance indicators, many specific application calculation results can be comparable to human capabilities.
Zhou Bin's name, 'heterogeneous', is essentially the most direct interpretation of the Internet background of AI chip entrepreneurs. Heterogeneous, as the name implies, is composed of different sources, and the Internet is a typical heterogeneous network. Later Evolved heterogeneous computing, a special form of parallel and distributed computing, often used to coordinate different hardware to meet different computing needs, and to enable code (or code segments) to perform in the most overall performance .
At present, AI chips basically perform heterogeneous computing through a variety of chips. In the past, traditional chip companies only focused on a few types of chips, but now chip companies are beginning to focus on horizontal development, integrating different types of chips, for example, mobile phone SoCs in traditional CPUs. In addition to the (central processing unit), GPU (graphics processor), ISP (online programming), there are also additional processing cores such as NPU (embedded neural network processor) to accelerate AI. 'There are some parts of the heterogeneous chip. It is a universal function. 'Cheng Zhisheng co-founder Kang Heng told the IT Times reporter.
Cost-determining path
An interesting phenomenon is that Internet-born AI entrepreneurs are running into the AI chip hardware field, while traditional chip vendors are using algorithms such as 'soft' to implement AI.
AI technology has three major elements, algorithms, computing power and data. From the perspective of international AI technology, the research and development of algorithm models such as deep learning is not mature, and new algorithm models such as migration learning and capsule networks are developing rapidly in synchronization, AI chips. The method and principle of using this method is still in the exploration stage. In fact, the current mainstream chip manufacturers have not introduced AI chips, and many AI functions are completed by general-purpose chips plus special algorithms and software.
Qualcomm's artificial intelligence engine (AI Engine), which was launched earlier this year, includes both hardware and software. It is equipped with a Neural Processing Engine (NPE) on Qualcomm's core hardware architecture (CPU, GPU, VPS vector processor). NN API, Hexagon neural network library and other software, make artificial intelligence on the terminal side (such as smart phone) application faster and more efficient. Qualcomm's chip products 骁龙 845, 骁龙835, 骁龙820, 骁龙660 support AI Engine, and many domestic mobile phones with AI flag also basically adopt Qualcomm solution, and the face recognition function is better realized by the AI Engine.
But for the current AI chip entrepreneurs and smart home manufacturers, the general-purpose chip is 'too expensive'.
Kang Heng told the IT Times reporter that TV, air-conditioning and other household appliances have a profit that covers the high cost of voice modules, but the cost of small appliances such as fans and lights is greatly limited, and the advantages of the modules are weakened. Do more smart products, sink to low-end products, but the market can not find the right chip, a product within 100 yuan, the general-purpose chip is not cost-effective. ' After building their own AI chip, Yun Zhisheng The chip solution of voice AI technology can be opened to customers, with greater initiative in cost and supply cycle.
However, Li Yangyuan believes that the key technologies of artificial intelligence are different in different stages. 'The processor is not the key technology of artificial intelligence. The dedicated processor only enhances the competitiveness in some working segments. 'He thinks that the sensing segment is sensor-centric. The existence value of the processor is not high; the cognitive segment, the learning segment and the decision segment, too much emphasis on the processor but affect the cost, including the one-time cost and the power consumption cost.
'No part of the premise of a dedicated artificial intelligence chip, the development of artificial intelligence chips should be regarded as an independent matter, rather than a natural extension of software research. 'Li Yangyuan uses human analogy, the sensor is the human body, the brain depends on The algorithm wins.
Li Shoupeng, a semiconductor analyst, also believes that artificial intelligence relies on algorithms, and chips are only carriers. If you want to use ASIC (full custom chip) to do better speech recognition processing, the hardware difference is not big. For speech recognition, recognition Statements are more related to software, network, and training, and the existing cloud and end data exchange delay problems will come along with 5G.
First stop on the ground: Intelligent audio
R&D AI chips are simpler than high-end general-purpose chips and are widely recognized by the AI circle.
Li Zhihan, co-founder of Yunzhisheng and vice president of IoT Business Unit, believes that after decades of development, the chip industry has precipitated a lot of modular things. For example, Qualcomm and MediaTek are ARM-based architecture design chips. Therefore, not every An AI chip must start from scratch and can use mature modules and products in the industry. However, the core acceleration module of the AI chip needs to be designed from the bottom. From 2014, the cloud, the end, the core strategy will be established, and the R&D will be officially established in 2015. The team, and then in 2018, launched the AI chip 'Yuyan', Yunzhisheng spent a full four years, and gradually realized that AI can not only be in the cloud, to land.
In 2018, it was called the AI chip landing year. The so-called landing, refers to the AI chip to be put on the terminal for commercial use. In 2017, China's investment in the chip field exceeded 150 billion yuan. Starting in 2018, these investment industries will continue to be intensive. Landing. The founder of Xinli Capital Group, Chairman Wang Chaoyong recently pointed out that China’s makers spent more than 300 billion US dollars each year to import various types of chips, consuming one-third of the world’s chips, self-sufficiency. The rate is less than 10%. Therefore, the investment in AI chip is the top priority. However, due to the high cost of film and R&D of security AI chips, large-scale shipments have not yet been formed to offset the cost; Mass production, autopilot AI chip security is not up to standard; other specific areas of AI chip overall downstream demand is insufficient, supply exceeds demand, 'the current AI field has a large bubble.'
According to Moore's Law, the performance of the chip will double every 18 months, and the cost will be reduced by half. But the fundamental secret of making money in the semiconductor industry is still large-scale. Can it have enough large shipments and commercial market, the AI chip is smooth? The key to 'landing' on paper. Earlier media reports said that even in the security field, 'Dayu' Hikvision, the annual demand for Nvidia is only 200,000.
From this point of view, intelligent audio may be the earliest market for AI chips. Research firm Canalys Research (hereinafter referred to as Canalys) released a report that by the end of this year, the number of smart audio will reach 100 million, almost last year. 2.5 times. Last year, the number of intelligent audio is less than 50 million. In the next few years, the number of smart audio will continue to grow, and by 2020 its holdings will more than double, reaching 225 million.
In May of this year, Yunzhisheng released the AI chip UniOne for the Internet of Things, which is used for edge calculation in the terminal. It can provide service solutions for smart audio, smart home, smart home appliances, etc. The AI voice chip module released for questioning 'Question' has been mass-produced, customers can place orders.
Li Zhifei, the CEO who went out to ask, believes that the chip is a long-cycle industry. From the concept, it must go through system design, module design, simulation verification, line synthesis, place and route, flow film production, package testing, driver development, solution adaptation. After a very long process, once the chip is made difficult to modify it like software, it must be redesigned, the iteration cycle is long, and the cost is high. The chip itself is a computing hardware carrier, and different chip adaptations are different. Algorithms and application scenarios, AI chips must have enough computing power to run various voice AI algorithms on the one hand, and a large number of interfaces for various scenarios on the other hand, while allowing cost and power consumption to meet large-scale quantities. Commercial requirements for production.
'Can be industrialized and can pose a competitive threat to foreign similar products in the market is the standard for a successful chip.' An industry insider said. From this point of view, China's AI chip has just started, Jianghu Cao, The bubble is going to break.
'This switched IT Times, author: Wu Yuxin Qi night cloud; original entitled:' AI chip 'of wilderness rivers and lakes: bolted in' bubble 'to be crowded'
2. China has become the largest country in the world for artificial intelligence investment and financing;
According to China Voice "News and Newspapers Summary", the "China Artificial Intelligence Development Report 2018" issued by Tsinghua University pointed out that China has become the world's largest country for artificial intelligence investment and financing. .
According to the report, as of June 2018, the number of artificial intelligence enterprises in China has reached 1,011, ranking second in the world. In the first quarter of 2013 to 2018, investment and financing in the field of artificial intelligence in China accounted for 60% of the world. %, become the world's most 'golden' country. Internet experts Wang Yue pointed out that China's artificial intelligence has a relatively complete infrastructure, is developing in the direction of the whole industry. People feel deeper in the B2B field.
Wang Yue: For example, intelligent sorting is used in the field of logistics; in the field of transportation, road control, vehicle deployment; for example, in security, camera capture on the street; more user-level retail.
In terms of paper output, the total number of Chinese artificial intelligence papers and high number of cited papers is the highest in the world. The number of Chinese patents is slightly ahead of the United States and Japan. However, the proportion of outstanding talents is still low. Wang Yue believes that China still needs to Smart chips, 5G and other core technologies are on the rise, and the future is expected to move from an artificial intelligence country to an artificial intelligence power.
3. Xie Wei: The essence of artificial intelligence technology and the significance of systemic innovation
Everything can only be accurately understood in the environment in which it exists. Today, big data, artificial intelligence and other concepts have become blurred due to many reasons such as commercial hype. Many specific technologies are also It is covered with a dazzling aura, or deliberately given a name that can trigger a wonderful imagination, such as 'deep learning'.
Below, let's look at the different elements of the information technology industry, in the chain from science to application, in which position. In order to not reveal unnecessary details and reveal the essence, we divide this chain into Five links: scientific principles, basic common technologies, specific application technologies, basic system principles/technologies and specific application systems, see Figure 1.
Figure 1 From scientific principles to application systems, image source: author for the picture, the same below
The scientific principle is a summary of the basic laws of motion, and technology is the application of the law. Therefore, the introduction of new scientific principles often has a profound and extensive impact on society. It is precisely because the meaning of scientific principles is so great, so ' Science' is also often stolen. Many technical outputs have also been brought to the 'scientific' hat. In the computer field, Turing machine and computational complexity theory are basically in the category of scientific principles. It is precisely because of this, computers Was named as 'scientific'.
Fundamentally, the artificial intelligence boom cooled in the 1990s because people have worked hard in the field of artificial intelligence for decades, and have not been able to understand the nature of intelligent processes in the general sense, and thus have not been able to achieve scientific significance. The principle breakthrough, in theory, abstracts the basic intelligent operation like digital basic calculation to support more advanced and complex intelligent processes. Therefore, the output of artificial intelligence field, although rich and influential, has never been achieved. The height of scientific principles.
In an industry, there are some basic common technologies, sometimes called core technologies, which support the entire industry. In the information technology industry, operating systems, databases, integrated circuits, etc. belong to this level of technology. These technologies The progress of the whole industry is also global. It is the progress of integrated circuit technology that has led to a historic turning point in the entire information technology industry around 2010 (see "Turning - Looking at IT Peaks" Chapter 2 Section III) The field of artificial intelligence has not only the results of scientific principles, but also has not been able to produce basic common technologies supporting an industry, no matter what name we give to those technologies/methods.
On the basis of basic common technology, there are specific application techniques for different problems to solve different types of problems. At this level, we have encountered the trace of artificial intelligence. For example, we are in the book "Turning - Looking at IT Peak" In the second section of Chapter 8: 'When people realize that we are not capable of effectively solving various 'intelligent problems' with some basic basic logic rules or mechanisms, the research of artificial intelligence has entered various kinds. Among the specific problems. Different kinds of problems have been developed, and a lot of solutions have been developed. Great progress has also been made.. .. Because of this, artificial intelligence is now more and more regarded as specific. The application tool method is integrated into different types of applications, appearing under its own specific technical name, and playing its own role in obscurity. The name of a classic textbook about artificial intelligence in these years is 'artificial intelligence--a kind of The modern method ', its subtitle 'A Modern Approach' refers to the attempt to adopt the concept of 'Agent' to artificial intelligence in different fields. Among the methods integrated into a unified framework. '
In fact, the use of the concept of agent to integrate technical methods related to artificial intelligence is also a way to show no help. It shows a helpless reality in this field: only practical specific technical methods, lack of scientific principles or basic common technologies. Support, there is no effective theory at the basic system level. The 'deep learning' that has been popular these years is also the technology at this level.
The concept of 'deep learning' includes deep belief networks, convolutional neural networks, cyclical and recursive networks, and many different specific network models and corresponding algorithms to solve different types of problems. They are actually based on computers' Violence' computing power, using large-scale, nonlinear artificial neural networks with tunable parameters of up to tens of millions or more, using specific 'learning/training' algorithms, adjusting these parameters through statistical processing of large numbers of samples, achieving non- Linear fitting (transformation) to achieve the functions of extracting and subsequently classifying the input data features.
It's a concrete way of solving a particular type of problem, not a general learning ability like humans, although the name does trigger the imagination of many people who don't understand the technology. In fact, information technology Most of the technologies in the field belong to this level, including technologies related to big data, and they all belong to technologies that assist in intelligent nature. Therefore, the boundaries between big data, artificial intelligence and other technologies are increasingly blurred.
These specific practical techniques, including 'deep learning' (artificial neural networks), are often experimental techniques. Before applying to a new specific problem, we are not sure whether it can effectively solve the problem, or can solve the problem. To what extent.
Because of this, take deep learning as an example, in Deep Learning ('Mei' Ian Goodfellow, MIT Press, 2016), which is considered to be the foundation of the 'deep learning' field. In the textbook, in order to explain the experimental characteristics of deep learning, the author specifically sets Chapter 11 to discuss this issue. Its topic is taken as 'practical methodology'.
At the beginning of this chapter, the author wrote a passage: 'To successfully use deep learning techniques, it is not enough to know which algorithms exist and why they are effective. A good machine learning practitioner also needs to know how to Select a suitable algorithm for the specific application and how to monitor it, and improve the machine learning system based on experimental feedback. In the daily development of the machine learning system, the practitioner needs to decide whether to collect more data, increase or decrease the model capacity, add or delete Regularization, improved model optimization, improved model inference or adjustment of the software implementation of the model. Trying these operations requires a lot of time, so it is especially important to determine the correct approach without blind guessing. Deep learning The experimental features of this specific technology.
This state of artificial intelligence is somewhat like the traditional field. Before the advent of modern science, people have been able to design and manufacture many different types of sophisticated tools to solve various specific problems. Exquisite, it may not be able to produce a deeper principle, the result of universality. In the history of China, countless skilled craftsmen have not been able to let China catch up with the trend of modern science and technology, which illustrates this problem.
The output of the above layers cannot be directly served to people. So on top of them, there are principles and techniques for turning technology into a practical product/system. Here we have only two layers for the sake of simplicity. The fact that technologies such as artificial intelligence belong to specific application technologies rather than system-level technologies, so in fact they cannot be the basis for constructing actual application systems/products, but must be attached to system-level principles and related technologies to function. At the beginning, there were efforts to construct systems based mainly on artificial intelligence technology, such as the fifth-generation computer in Japan. In the future, this kind of effort will not completely disappear. However, from the objective nature of artificial intelligence technology, it is applied as a specific level. Technology to use, is a reasonable choice.
What is decisive for the underlying technology to play its own value is the basic system principle and related technologies. For example, the von Neumann architecture belongs to this category. It has become a core achievement in the computer field because it allows us to take advantage of this architecture. The related specific technology design and manufacture of the computer system products that can be actually used, and the computer system products enable the relevant specific technologies to play their due value in various fields.
The basic system principle and related technologies of this layer are relatively independent from the specific application fields, so the impact is also global. They include not only the principle and phase relationship technology of independent basic systems, but also a large number of independent systems. The interactive links constitute the principle and related technology of the macroscopic basic large system. Internet, cloud computing belongs to this category. In the field of networked information technology, the principles and technologies that constitute the macroscopic system play an increasingly important role. Of course, the innovation of independent basic systems is the premise of large-scale interconnected systems, and its role is more fundamental.
The importance of this layer of basic system principles and related technical innovations far exceeds those specific application technologies, at least comparable to basic common technologies, and some even close to scientific principles. So the von Neumann architecture is only available in the computer field. Such a lofty position. The powerful function of the human brain is not only reflected in the specific intellectual ability, but also in its system level. This system level advantage, in the first section of this chapter, we use examples to point out that it It is not just a highly distributed network formed by the distributed connection between a large number of neurons. In the next section, we will analyze another important aspect of the human brain at the system level, which is not fully valued.
In addition to the Internet, cloud computing has a recently-recognized 'blockchain'. The blockchain technology that followed Bitcoin in 2009 is the best way to explain the breakthrough at the system level. How can the lower layer be specific? Application technology has the greatest value.
In November 2008, an anonymous person who called himself 'Zhong Ben Cong' published a short but influential article on the Internet: 'Bitcoin: A Peer-to-Peer Electronic Cash System' (Bitcoin: One Equivalent The cash system on the network.) On January 3, 2009, Nakamoto founded his first block (Creation Block) in the Bitcoin system, and the Bitcoin system supported by the blockchain began to operate. Chain technology officially debut. The schematic of the technology is shown in Figure 2.
A closer look at the specific techniques in the blockchain reveals that the techniques used to solve specific problems in blockchains, such as asymmetric encryption, tamper-proof, peer-to-peer networks, etc., are all existing technologies, none of which It was invented or improved by Nakamoto. Using only these off-the-shelf technologies, Nakamoto has created a distributed operation on the open Internet. Without supervision, everyone can participate in accounting, and the accounts are transparent and transparent. Trustworthy, reliable, secure, accurate cash system.
If there is no basic feasibility yet, but countless people are still eager to create an intelligent system that is as intelligent as people, even surpassing people, a cash system can be said to be basically anyone before it is created. I have never thought of achieving this goal, nor will I believe that someone can create it. This is an information technology miracle that is built on the specific technology of existing information and beyond everyone's imagination.
This miracle relies not on new breakthroughs in technology to solve specific problems, but in an innovative systemic principle and design. Nakamoto takes full advantage of the behavioral characteristics of people in a distributed open network environment, and skillfully creates a Open block-based distributed system on the Internet. The so-called 'consensus algorithm' in Bitcoin to ensure accurate accounting records is part of the system-level operating mechanism of this distributed system. Or the consensus algorithm is adopted. The design of the system-level operating mechanism of a distributed system is realized. The reason why blockchain technology is difficult to be accurately grasped and understood is that many of its essential characteristics are determined by system-level mechanisms rather than relying on single-point technology. achieve.
Regardless of the application of the blockchain technology in the future, its emergence has brought us a very profound inspiration.
First of all, it allows us to see in the information technology industry a living, using mature and specific technology to create system-level innovation, beyond everyone's imagination, beyond the specific technology can achieve, miraculous subversive Effect. The importance of the system was highly valued in the 1980s. At that time, there were famous three theories in the scientific and technological world, namely, information theory, cybernetics and system theory. The result of deepening the understanding of the development of modern science and technology. The most important historical contribution made by Qian Xuesen is to deeply implant the viewpoints and methods of system theory into many fields of China, especially in the aerospace field.
In the traditional industry, we can also see a large number of systematic innovations surpassing specific technologies. Let's take a brief look at the example of the aviation industry. The power of the early aircraft was the piston engine. Later in the late 1930s. Germany took the lead in sending jets to the sky. Then the turbojet became the protagonist of aerodynamics. The two engines are scientifically identical, using fuel combustion to cause gas expansion and thermal energy into mechanical energy. But the system-level principles of the two are completely different. Turbojet engines are a fundamental system-level disruptive innovation in aerospace systems. The advances it brings are not the ability of piston engines to improve technology or materials at any particular level. Comparable. It has brought about tremendous changes in the aviation industry.
In today's military field, the decision of the battlefield advantage is no longer just the advancement of the specific weapon in the traditional sense, but the super system of sea, land and air integration. This is a qualitative leap in contemporary warfare.
Secondly, in the face of the dazzling results brought by the blockchain, we must not only ask: After the information technology industry has opened its golden age, will there be more, beyond our imagination, based on system level Innovations are constantly emerging, which have a subversive impact on all areas of human society.
Compared to the miracle created by blockchain, we focus our imagination on the improvement of tool intelligence. Although it makes sense, the horizon may be too narrow. At present, the miracle of blockchain and the more complicated intelligent technology are not too much. The direct connection is not a repetition or imitation of human intelligent activities, but it has an amazing subversive impact. Of course, if the blockchain is widely used in the future, it is an inevitable trend that various specific intelligent technologies are integrated into it.
In today's information technology industry, compared with big data and artificial intelligence, because its professional blockchain is a relatively lonely technology, it is it that opens a unique observational information technology industry. The windows of future development provide a unique perspective. The potentials revealed by it may have more essential and important significance for us to effectively grasp the future development of the information technology industry and the entire human civilization.
The current information technology application system has increasingly become the design of software, and as we pointed out in the first section of Chapter 2 of "Turning to the Peak of IT", 'Compared with the design and development of physical products, software development It is almost free to play in the virtual space of an infinite resource. So software design development is even considered to be a purely spiritual art creation process. 'It is this freedom of free creation that has a blockchain Miracles will also spawn more disruptive system-level innovations of varying system sizes beyond our most romantic imagination today.
The functions that many systems will achieve will far exceed our expectations for artificial intelligence as a whole. They will not have the subjective status of people, but they will greatly surpass the human beings in intelligence as an individual. The height and breadth of the body - just like the mechanical tools of auxiliary physical energy achieve a comprehensive transcendence of human beings in all aspects of physical fitness. The 'intelligence' of these systems will be significantly different in nature from human intelligence, just as The blockchain system is shown to us. These systems are being gradually created and will form an unprecedentedly complex virtual world.
While paying full attention to the progress and impact of specific application technologies such as artificial intelligence, we should expand our imagination and pay more attention to the innovation of basic system level principles and related technologies. The future of information technology industry development may be more This level of subversive innovation is determined. And specific technologies such as artificial intelligence will find better and bigger stage in these innovations.
The intelligent system with the main virtual image as the core in the book “Turning – Looking at IT Peak” is the basic system principle and technology level in the information technology industry. It is an important innovation with systemic intelligence meaning, representing information. An extremely important direction for the application of the class is a great advancement in human intelligence pursuit, and a qualitative leap in human-assisted intelligence tools.
About the author: Dr. Xie Wei, a leading talent in the capital science and technology department, Ph.D., Department of Electronic Engineering, Tsinghua University. Author of "My Workplace Ten Years", "Growth - From Campus to Workplace", "Turning - Looking at IT Summit".