Artificial intelligence is currently emerging as the key factor propelling mankind into the age of intelligence, much as steam engines ushered in the age of steam, electric generators ushered in the age of electricity, and computers and the internet ushered in the age of information. The world's industries are evolving and changing as they compete to create creative ecosystems in the field of artificial intelligence, fully realizing the significance of artificial intelligence technology in kicking off a new round of industrial revolution. In an effort to obtain an advantage in the global technology race, developed nations throughout the globe have identified the development of artificial intelligence as a key strategy to boost national competitiveness and maintain national security. Accelerating the development of the new generation of artificial intelligence is a strategic issue regarding whether China can seize the opportunities of the new round of scientific and technological revolution and industrial transformation, as President Xi Jinping pointed out in great detail at the ninth collective study of the Central Politburo of the 19th CPC Central Committee. Missing a chance might mean passing up an entire period. We must take the chance, step up, and work to excel in this big game that defines our future and destiny as the next phase of technological revolution and industrial transformation is already in view.
Concept and Background
We must first comprehend the origins of artificial intelligence in order to comprehend where it is headed. McCarthy, Minsky, and other researchers first proposed the idea of "artificial intelligence (AI)" during a gathering at Dartmouth College in the summer of 1956 to discuss "how to simulate human intelligence with machines," which launched the field of artificial intelligence.
An emerging field of technology called artificial intelligence researches and creates theories, processes, tools, and application systems that can mimic, supplement, and even surpass human intelligence. Its research aims to make it possible for intelligent machines to listen (through speech recognition and machine translation, for example), see (through image and text recognition), speak (through speech synthesis and human-machine dialogue), think (through human-machine chess and theorem proving, for example), learn (through machine learning and knowledge representation), and act (through robots and self-driving cars, for example).
The path to understanding artificial intelligence is paved with detours. There are many ways to view the evolution of artificial intelligence over the past 60 years, starting in 1956. The six stages of artificial intelligence development are as follows:
The first is the first development period, from 1956 to the beginning of the 1960s. After the idea of artificial intelligence was put forth, a number of outstanding research breakthroughs were made, including the development of chess programs and machine theorem proving, which led to the first peak in the field's development.
The second spans the 1960s to the early 1970s, a time of introspection and growth. People's expectations of artificial intelligence have significantly increased as a result of the ground-breaking advancements in its early development. People started taking on increasingly difficult jobs and putting forth some irrational research objectives. The progress of artificial intelligence, however, reached a low point as a result of repeated failures and unmet expectations (such as the failure of machines to demonstrate that the sum of two continuous functions remains a continuous function and the machine translation fiasco).
The application development phase of artificial intelligence, which lasted from the early 1970s through the middle of the 1980s, was the third stage. Expert systems were created at this time to tackle particular problems by replicating the knowledge and experience of human specialists. This was a significant advancement for artificial intelligence as it moved from theoretical research to actual use in practical applications utilizing specialized knowledge. Expert systems' success in disciplines like medicine, chemistry, and geology propelled the development of applications for artificial intelligence to new heights.
The fourth era, which spanned the middle of the 1980s to the middle of the 1990s, was the slump development period. Expert system issues such as limited application domains, a lack of common sense knowledge, challenges in knowledge acquisition, solitary reasoning methods, a lack of distributed functions, and compatibility problems with existing databases started to surface as the application scale of artificial intelligence continued to grow.
From the middle of the 1990s to 2010, the fifth phase was known as the steady development period. Innovative research into artificial intelligence was expedited by the development of network technology, particularly internet technology, which encouraged more practical use. Garry Kasparov was vanquished by IBM's Deep Blue supercomputer in 1997, and in 2008, IBM put up the idea of a "smart earth." Both of these significant occasions occurred during this time.
The booming development period, which runs from 2011 to the present, is the sixth time frame. Big data, cloud computing, the internet, and the internet of things, along with pervasive perception data and computing platforms like graphic processors, have accelerated the development of artificial intelligence technology, particularly deep neural networks, bridging the "technical divide" between science and application. Images classification, speech recognition, knowledge retrieval, man-machine chess, and unmanned driving are just a few examples of artificial intelligence technologies that have transformed from "unusable" to "usable," ushering in a new era of accelerated growth.
There is some "hype" in society about the current state and impact of artificial intelligence. For instance, some people think that in 30 years, robots will rule the world and that humans would be forced to serve artificial intelligence. Others think that artificial intelligence will soon surpass human intelligence. The advancement of artificial intelligence will suffer from this "hype" and misconceptions, whether they are deliberate or not. Therefore, it is crucial to have a thorough understanding of the technology and market conditions when developing the strategy, policy, and guidelines for the growth of artificial intelligence.
Specialized artificial intelligence has seen significant advancements. Depending on the context, artificial intelligence can be broadly split into two categories: specialized artificial intelligence and generic artificial intelligence. Due to their single job, clear requirements, identifiable application boundaries, substantial domain knowledge, and relatively simple modeling, specialized artificial intelligence systems for certain activities (such as Go) have single-point breakthroughs in the field of artificial intelligence. Specialized intelligence fields have seen the most recent advancements in artificial intelligence. For instance, AlphaGo outplayed a human champion in a game of Go; artificial intelligence algorithms have surpassed human capabilities in large-scale picture and face identification; and professional-grade AI systems have detected skin cancer.
Artificial intelligence is experiencing a boom in innovation and entrepreneurship. The global industrial community has adjusted its development strategies in light of the crucial role that artificial intelligence technology will play in bringing about a new wave of industrial change. Google, for instance, made it plain at its annual developer conference in 2017 that its development strategy will change from "mobile first" to "AI first," and Microsoft's fiscal year 2017 report for the first time identified artificial intelligence as the company's development ambition. The vanguard of innovation and entrepreneurship is the realm of artificial intelligence. A McKinsey & Company analysis claims that the global investment in artificial intelligence research and development exceeded $30 billion in 2016 and is currently in a high-growth stage. According to CB Insights, a renowned venture capital research organization, 1,100 new artificial intelligence start-ups were founded globally in 2017. The artificial intelligence sector received a total investment of 15.2 billion US dollars in 2017, a 141% increase from the previous year.
The inventive ecosystem's design has evolved into a key stronghold for the growth of the artificial intelligence sector. The evolution of information technology and industry may be traced to the succession of both new and established information industry titans who raced to build an inventive ecosystem for the sector. In the age of the internet and mobile internet, representative firms in the information industry include Google, Apple, Facebook, Amazon, Alibaba, Tencent, and Baidu, as opposed to conventional information industry representative corporations like Microsoft, Intel, IBM, and Oracle. The ecosystem for artificial intelligence innovation includes horizontal business and application ecosystems like intelligent manufacturing, intelligent healthcare, intelligent security, intelligent retail, and intelligent homes as well as vertical data platforms, open-source algorithms, computing chips, fundamental software, graphics processors, and other technology ecosystems. Since the information industry structure in the age of intelligent technology has not yet established a monopoly, major international technology companies are actively encouraging the development of artificial intelligence technology ecosystems. They also aim to take control of the relevant artificial intelligence industries.
It is increasingly clear how artificial intelligence will affect society. On the one hand, artificial intelligence is driving the upgrading and replacement of traditional industries as well as the quick growth of the "unmanned economy," producing favorable effects in the areas of intelligent transportation, intelligent homes, intelligent healthcare, and other areas of people's livelihood. Artificial intelligence is also driving the rapid development of the "unmanned economy." In contrast, urgent solutions are needed for problems like the protection of personal data and privacy, intellectual property rights of AI-generated content, potential bias and discrimination in AI systems, traffic laws for unmanned driving systems, and ethical concerns about brain-machine interfaces and human-machine symbiosis.
Trends and Prospects: After more than 60 years of research and development, artificial intelligence, or "artificial intelligence," has made great strides in the fields of algorithms, processing power, and data, commonly referred to as the "three calculations." Technology has reached a point where it has changed from being "unusable" to "usable." Before it can be termed "very useful," there are still a lot of obstacles to be removed. What are the anticipated patterns and traits of artificial intelligence's future evolution, then?
general intelligence progressing from specialized intelligence. In addition to being a necessary trend for the next generation of artificial intelligence development, achieving a breakthrough in the switch from specialized to general artificial intelligence is also a key issue in the research and application fields. The "National Strategic Plan for Research and Development of Artificial Intelligence," published by the US National Science and Technology Council in October 2016, underlined the need of concentrating on research on generic artificial intelligence. The AlphaGo system development team's founder, Demis Hassabis, suggested that we work toward the objective of "creating general artificial intelligence that can solve all of the world's problems." Microsoft set up a general AI lab in 2017, and several researchers with expertise in perception, learning, reasoning, natural language understanding, and other areas took part.
artificial intelligence to hybrid intelligence between humans and machines. An important research area in artificial intelligence involves utilizing findings from the fields of neurology and cognitive science. In order to enhance the performance of artificial intelligence systems, human-machine hybrid intelligence attempts to incorporate human roles or cognitive models. It makes it possible for artificial intelligence to develop into a natural addition to and expansion of human intelligence and to more effectively use human-machine interaction to tackle complicated challenges. In both China's next generation of artificial intelligence plans and the United States' Brain Initiative, human-machine hybrid intelligence is a significant research and development direction.
The transition from "artificial + intelligence" to autonomous intelligent systems. Deep learning is currently the subject of a lot of artificial intelligence research. Deep learning has drawbacks, though, and is heavily dependent on human input. It takes a lot of time and effort, for instance, to construct deep neural network models, set application scenarios, gather and label enormous amounts of training data, and require user adaptation to the intelligent system. As a result, scientists are now concentrating on autonomous intelligent techniques that minimize human involvement and enhance the machine's capacity for autonomously learning from its surroundings. For instance, AlphaZero, the AlphaGo system's successor, used self-play and reinforcement learning to attain "universal chess artificial intelligence" in the games of Go, Chess, and Shogi. In terms of the autonomous design of AI systems, Google presented an automatic machine learning system (AutoML) in 2017. This system aims to cut labor costs by automatically developing machine learning systems.
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