The development of artificial intelligence in the next 20 years
He recommended a few novels. "Fang Wei" was a Qin and Han dynasty novel written by Qingmo Nongyu. The male protagonist, Cao Fang, said that as long as he was around, there would be no chaos. Military affairs were his romance. Urging group number 1073023738. "The Masked Rider's Celebration Can Make You Stronger" was a light novel written by Chu Ge in Cold Clothes. It had the story of a fake transmigrator and a young girl who wanted to be a demon king. The male and female leads were Makeshi Tsunebana and Woz. There were also many supporting roles. The character information was extremely detailed. Overall, this Masked Doujin was well written. 'The Crown Prince' was a historical novel written in February. The male protagonist, Zhu Changluo, was a fan of Erguotou, while the Wanli Emperor was the main character. He had many unique hobbies. However, this book started out high and went down low. After it was put on the shelves, there was too much water. The protagonist had hundreds of chapters, but he still thought that the prince did not make any big moves. 492635934. 'The more they object, the more it proves that I did the right thing.' It was a light novel written by Metal Raindrop. The novel way the male protagonist, An Su, became the pope was super interesting, and the female protagonist, Luojia, and other characters also had their own characteristics. It had a funny style and a lot of memes. Although it was not enough, it was worth watching. "Gang Zong: Captured by Brother Kun for Filming" was a heavenly novel written by Tai Chi. The male lead, Du Sheng, was captured by Jing Kun to film and could still draw skills. The story was very special. "A Short History of the Future: Legends of the Intelligent Era" was equally exciting. Everyone was welcome to click and read it!
Blueprint for the development of artificial intelligence in the next five years
There was currently not enough information to accurately describe the development blueprint of artificial intelligence in the next five years, but some development trends could be inferred from the available information. From the perspective of education, artificial intelligence would continue to be deeply integrated. For example, under the scenario of " artificial intelligence + education," a more personal and intelligent learning environment might be further constructed. Such innovative application scenarios as the AI learning room may continue to develop and improve, providing more diverse services to meet the needs of students 'independent learning and creative ability cultivation. At the same time, it will lower the threshold of use and improve the quality of service. In the process of digital transformation of education, methods such as MOOCs, microclasses, virtual reality, game learning, etc. will be more fully utilized to promote the digitizing of resources and innovative teaching and learning norms. At the enterprise level, the artificial intelligence model would develop in the direction of solving practical problems. For example, the Pangu model had already demonstrated how to solve industrial problems. In the future, more companies might apply artificial intelligence to difficult professional fields such as weather forecast, and they would further get rid of the constraints of foreign technology to solve the practical difficulties faced by enterprises. With the development of technology, the capabilities of AI itself might also gradually improve. From the basic stage of building the ability to talk, to the stage of solving complex problems, taking action independently, assisting innovation, and even leading innovation, and finally reaching the stage of efficiently organizing complex work and coordinating multiple resources. In terms of application fields, it could be seen from the world's first artificial intelligence application conference that artificial intelligence had great potential in cutting-edge fields such as "AI+ English","AI+ Health","AI+ Speech", as well as finance, culture, and cars. In the next five years, the application of artificial intelligence in these fields may continue to expand and deepen, thereby promoting global industrial upgrading and transformation. At the same time, the China government had incorporated " artificial intelligence +" into its national strategy, which would encourage more R & D resources to lean toward artificial intelligence. More results could be achieved in the application and optimization of technology in the market and the rapid transformation of technology into products. The construction of digital infrastructure would also be further developed and improved. For example, there might be more results in the integration of cloud and network, cloud and intelligence, and heaven and earth. This would provide better basic conditions for the development of artificial intelligence. " A Short History of the Future: Legends of the Intelligent Era " was equally exciting. Everyone was welcome to click and read it!
The development of artificial intelligence in five years
According to the available information, artificial intelligence could develop in the following areas by 2029: - ** In terms of market value **: Artificial intelligence in the education market is expected to grow at a compound annual growth rate of 33.51%, and the market value will reach 43.467 billion USD in 2029. Other areas are not clear but may also show growth trends. - ** In terms of technical capabilities **: There are currently different opinions on whether artificial intelligence can reach the level of general artificial intelligence (AGI) by 2029. Futurist Ray Kurzweil predicted that artificial intelligence would achieve human-level intelligence by 2029; Musk believes that by 2029, artificial intelligence may be smarter than all humans combined; Hasabis, CEO of Google DeepMind, believes that AGI may arrive as soon as 2030; Huang Renxun believes that general artificial intelligence may be realized in just five years (by 2029), but it may still be far away from other definition; Meta's chief scientist, Yang Likun, believed that it would take many years for artificial intelligence to reach the level of human intelligence, and it would take decades for it to reach a certain human perception. The predictions of both Open AI and Microsoftwere more conservative. Brad Smith, the president of Microsoftsaid that artificial intelligence with superintelligence level was unlikely to be realized in one or two years, and it would take years or even decades. Jason Kwon, the chief strategy officer of Open AI, also pointed out that GPL- 4 was not AGI, and the proportion of work done by humans with economic value was still much higher than that of GPL- 4. - ** In terms of application scope **: At present, artificial intelligence has penetrated into various industries such as news, education, and e-commerce. Five years later, it may be further developed and bring about changes in more industries. For example, applications in the field of education, from transmitting knowledge to custom-made learning experiences for students, may be more mature and perfect. - ** Global landscape **: From a global perspective, it is expected that North America will occupy an important share of the educational artificial intelligence market. The governments and enterprises of various countries are actively investing in artificial intelligence. China will play an important role in technological innovation, policies and regulations, and international cooperation in promoting the development of artificial intelligence security. In the next five years, the development of artificial intelligence in various countries will continue to compete and cooperate. "A Short History of the Future: Legends of the Intelligent Era" was equally exciting. Everyone was welcome to click and read it!
The Future of Artificial Intelligence in the Next Five Years
The following are the possible development prospects of artificial intelligence in the next five years: ** 1. Technology ** 1. ** Model structure and scale ** - The big model and the small model developed together. On the one hand, large models continued to evolve in the direction of multi-modes, becoming the standard for understanding language, pictures, videos, and other information. They continued to improve the comprehensive processing ability of various complex information. On the other hand, due to the advantages of low cost and high operational efficiency in specific scenarios, small models would emerge and run on more devices with excess computing power such as mobile phones. Moreover, small models would play an important role in the vertical field, becoming a tool to improve the productivity of specific business scenarios. - The open source model continued to develop. The situation of open source large models and closed source large models competing would further develop. More companies and research institutions would actively participate in the open source community. By sharing knowledge, ideas, and technology, they would promote the continuous emergence of large model related results. This would help the country learn from foreign achievements, avoid repeated research and development, and accelerate the development of artificial intelligence. 2. ** Data ** - The importance of small data and high-quality data was highlighted. With the resource consumption and training challenges brought by a large amount of invalid data, the value of small data (focusing on accuracy and relativity) and high-quality data (strictly screened, cleaned, and labeled) will continue to increase in the next five years. This will help reduce the dependence and uncertainty of artificial intelligence algorithms on data, enhance network reliability, and may also provide new ways to solve the bottleneck of general artificial intelligence. 3. ** Explanation and reliability ** - The explainable model would receive more attention. In order to make AI more transparent and credible, so that humans can understand, evaluate, supervise, and interfere with model behavior, explainable methods will continue to develop. This will help AI in key areas such as healthcare and finance, reducing the consumption of public resources and enhancing user trust. - Building a reliable AI system became the key. By transforming human values and ethics into reinforcement learning reward functions, human-machine alignment was achieved to ensure that the AI output results were consistent with human values and improve the reliability of the AI system, so that it not only performed well in terms of task efficiency, but also met human ethical standards. ** 2. The application level ** 1. ** Enterprise application ** - The rise of the enterprise market. By 2024, about 85% of companies will expand their AI capabilities through open source models, and companies will shift AI technology from research and development to production applications. Large models would be developed in the direction of industrialization and vertical development on the enterprise side. More enterprises would use large models to solve vertical scene problems. By integrating the unique knowledge (dark knowledge) within the enterprise with the depth of the business, productivity would be improved. There was no need to pursue a full-featured large model, which would solve the problems of enterprises in terms of computing power. - Business processes were automated. Some companies will use artificial intelligence to achieve business process automations in the fields of logistics, customer support, and marketing. They will use algorithms to make real-time automatic decisions, improve corporate efficiency, and enhance their ability to respond to market fluctuations. 2. ** Social life application ** - The smart devices were all upgraded. There would be more cases where Huawei promoted the comprehensive upgrade of smart devices through AI chips and algorithms. AI would continue to develop in smart homes, autonomous driving, smart customer service, medical diagnosis, and other fields, changing people's lifestyle. - A new generation of voice assistants and video functions were popularized. With the development trend of Sora, the vincendiary video model of Open AI, and Google integrating chatbots into mobile devices, vincendiary video and a new generation of voice assistant functions will appear in more devices. ** 3. Social Impact and Supervision ** 1. ** AI Constitution ** - Establishing an AI monitoring model framework similar to the constitution's superior law would be an inevitable trend. Through the development of clear standards and specifications, it ensured the compliance and safety of the AI system during the development and use process, reduced the risks that may be brought about by the monitoring, value guidance, and overuse of AI in the military field. It protected user privacy during the training phase and avoided unfair results. During the deployment phase, it continuously monitored the operational status. 2. ** Perfection of laws and regulations ** - With the introduction of laws and regulations related to artificial intelligence in China and the European Union, it is expected that in the next five years, more countries will implement artificial intelligence governance laws to regulate the development and application of artificial intelligence, ensure that it is ethical and respects intellectual property rights, and avoid the adverse effects of companies taking shortcuts or ignoring ethics. 3. ** Dealing with the Challenge of False Information ** - In the "post-truth" era, due to the challenge of artificial intelligence that may bring about the spread of false information, governments will speed up the formulation of laws and at the same time improve the public's ability to identify through education. "A Short History of the Future: Legends of the Intelligent Era" was equally exciting. Everyone was welcome to click and read it!
The development of artificial intelligence
The development of artificial intelligence has experienced ups and downs. The following are some of the iconic achievements and stages in its development process: In 2015, Academician Zhang Bo proposed the prototype of the third-generation artificial intelligence system. By the end of 2018, he officially proposed the theoretical framework of the third-generation artificial intelligence system, which marked the development of artificial intelligence into the third generation. In 2019, the artificial intelligence industry had completely bid farewell to the era of " shouting slogans " and " packaging concepts " and entered the track of steady development. During this period, artificial intelligence technology and applications began to land in various industries, and the results and scenarios were endless. For example, LVDIA's open-source StyleGAN, Google's quantum hegemony paper was officially published in Nature, Boston Dynamic's robot dog Spot was about to be put into commercial use, Ali launched the world's strongest AI chip, Hanguang 800, AI face swapping and AI " Face Recognition " to assist the police, and so on. In 2020, in the context of the global fight against the epidemic, artificial intelligence was given more expectations and responsibilities, showing its talents in information collection, data summary and real-time updates, epidemic investigation, vaccine drug research and development, new infrastructure construction and other fields. Nowadays, artificial intelligence was widely used in many fields such as autonomous driving, voice recognition, smart home, financial risk assessment, and so on. It was still developing and was expected to play an important role in more fields in the future, promoting the intelligent process of human society. However, as the discipline was still expanding, it was difficult to make a completely accurate and detailed development map that covered all the details. " A Short History of the Future: Legends of the Intelligent Era " was equally exciting. Everyone was welcome to click and read it!
The development of artificial intelligence
The development of artificial intelligence (AI) was as follows: 1. ** Initial Stage (1943 - 1956)**: Early theories and concepts begin to develop. In 1943, Warren McCulloch and Walter Pitts proposed the basic model of artificial neural networks, and then Turing proposed the Turing test to determine whether a machine had true intelligence. 2. ** Golden Age (1956 - 1974)**: The term "artificial intelligence" was first proposed at the Dartmouth Conference in 1956, marking the emergence of artificial intelligence as an independent research field. During this period, advances in computer technology and large amounts of research funding allowed artificial intelligence to make significant progress. 3. ** Winter period (1974 - 1980)**: Due to high research costs, lack of practical applications, and disappointment caused by high expectations, artificial intelligence research entered a state of stagnation, known as the "AI winter." 4. ** Expert System Era (1980 - 1987)**: Artificial intelligence expert systems were widely used. These systems simulated the decision-making process of human experts and provided advice for specific tasks. 5. ** Second Winter (1987 - 1993)**: Due to economic and technological reasons, artificial intelligence once again fell into a low point. 6. ** Machine learning era (1993 - 2011)**: The improvement of computer processing power and the emergence of big data made machine learning, especially neural networks, receive renewed attention. 7. ** Deep learning era (2011-present)**: In 2012, AlexNet achieved a breakthrough in the image classification competition, Imagenet, marking the arrival of the deep learning era. Today, AI has been widely used in speech recognition, natural language processing, image recognition, and other fields. In addition, the development of AI was divided into the following stages: the first stage was chatbots, conversational language AI; the second stage was reasoners, solving human-level AI; the third stage was intelligent entities, AI that could take action on behalf of users (such as AI advertising, AI education, AI video, AI media, AI games, etc.); the fourth stage was the inventor, AI that could help invent; and the fifth stage was the organizer, AI that could complete organizational work. "A Short History of the Future: Legends of the Intelligent Era" was equally exciting. Everyone was welcome to click and read it!
Blueprint for the development of artificial intelligence in five years
The following are some speculations about the development blueprint of artificial intelligence in the next five years based on the current development trend of artificial intelligence: ** 1. Technology ** 1. ** Arithmetic optimization and innovation ** - Deep learning will continue to evolve and hopefully become more efficient and accurate when dealing with complex tasks. For example, significant progress had been made in the fields of image recognition and natural language processing, but there was still room for improvement. In the future, new neural network architecture or optimization algorithms might appear to reduce training time and improve the model's generalization ability. - Quantum machine learning could move from theoretical research to practical applications. With the development of quantum computing technology, quantum algorithms are expected to play a unique role in machine learning tasks such as data mining and prediction analysis. For example, quantum support matrix machines may be more widely used in large-scale data classification tasks, and quantum cluster algorithms may also show higher efficiency and accuracy in data clusters. 2. ** Increase in calculation ability ** - With the development of hardware technology, such as more powerful GPUs, TPUs, and quantum computing hardware, the training and reasoning speed of artificial intelligence models would be greatly improved. The development trend of cloud-intelligence integration would encourage communications companies to further improve the full-stack intelligent computing service system for large models to meet the needs of multiple large models being trained at the same time. This would help the rapid development and widespread application of artificial intelligence in various fields. 3. ** Data Management and Usage ** - Building more open and shared data standards and large data centers might become a trend. Breaking the data barrier and integrating multi-source data could provide AI with richer and more comprehensive data resources, thereby improving the accuracy and reliability of the model. For example, in the medical field, integrating medical data from different hospitals and research institutions could train more accurate disease diagnosis and treatment recommendation models. ** 2. Field of application ** 1. ** Education ** - The application of artificial intelligence in educational scenarios would be more in-depth. It was expected that innovative application scenarios such as the AI learning room would be further promoted and improved to provide a more customized learning experience. For example, by constructing a creative question-and-answer learning model, students 'questioning ability, demand definition ability, and human-computer cooperation ability were cultivated. The learning agent developed by the expert teaching and research team was used to stimulate students' interest in learning and improve their learning ability. - Human-computer collaboration would become the new norm in education. The dual-teacher model of " AIGC teacher + expert teacher " would be adopted, combining the intelligent tutoring of artificial intelligence and the professional knowledge of expert teachers to provide students with a richer and deeper learning experience to meet the learning needs of different students. 2. ** Industry ** - Industrial applications similar to the Pangu model would continue to expand. Artificial intelligence would play a greater role in solving industrial problems in thousands of industries. For example, in the manufacturing industry, the production process would be optimized, the efficiency of product quality control would be improved, and the accuracy and speed of prediction would be further improved in complex industrial fields such as weather forecast, thus promoting the intelligent transformation of the industrial field. 3. ** Agriculture ** - Artificial intelligence technology would further integrate technologies such as intelligent perception, intelligent equipment, expert systems, and the Internet of Things to achieve more accurate intelligent monitoring and regulation of the agricultural product planting environment. For example, through more accurate environmental monitoring and automated regulation equipment, the yield and quality of crops could be improved and the risks of agricultural production could be reduced. 4. ** Modern Service Industry ** - In the financial sector, artificial intelligence would provide smarter risk management, such as more accurate credit evaluation and market risk prediction. In the medical field, in addition to customized medical services, it could also play a greater role in disease prevention, remote medicine, and so on. In terms of public services, artificial intelligence will help to improve service processes and improve service efficiency and quality, such as achieving smarter traffic flow regulation in urban traffic management. 5. ** Government governance ** - The new generation of artificial intelligence would further reshape the interaction model between the government and the people and improve the digital government service system. For example, through intelligent customer service and automated approval processes, the efficiency of government governance could be improved and the satisfaction of the people could be increased. 6. ** Green Industry ** - In the power system, artificial intelligence technology will continuously improve the real-time monitoring and dispatching capabilities of the power system to improve energy efficiency. In the monitoring and treatment of industrial pollution, artificial intelligence would enable more accurate pollution monitoring and more effective treatment solutions. ** 3. Industrial ecology and social aspects ** 1. ** Comprehensive governance and policy support ** - The government would continue to strengthen the top-level design, build a new digital China system, formulate laws and regulations, standard systems, and ethical norms to ensure the orderly and controllable development of artificial intelligence. Set up a special fund, continue to increase financial support for artificial intelligence, and promote the upgrading of the industrial chain. - Co-innovation and cross-field cooperation would become more common. By building a joint research base, promoting the integration mechanism of production, education, and research, promoting resource mobilization and cooperative innovation, cultivating innovative talents, and building intellectual support for the intelligent era, the development of multi-scenario applications could be realized. 2. ** Social impact and employment structure adjustment ** - With the widespread application of artificial intelligence in various fields, the social employment structure would undergo a certain degree of adjustment. Some repetitive and regular jobs might be replaced by artificial intelligence, but at the same time, it would also create new employment opportunities, such as artificial intelligence research and development, maintenance, data annotation, and human-computer collaboration. Society needed to pay attention to how to retrain and transform the labor force to adapt to the employment needs of the artificial intelligence era. " A Short History of the Future: Legends of the Intelligent Era " was equally exciting. Everyone was welcome to click and read it!
Future Development of Artificial Intelligence
The future development of artificial intelligence presented many prospects: 1. ** Continuous development of large models **: With the further improvement of computing power and the continuous increase of data volume, large models will make more progress in natural language processing, computer vision, and other fields. More powerful large models will appear and provide support for various application scenarios. 2. ** Self-adaptation and Personalized Service **: Artificial intelligence will pay more attention to self-adaptation and personalization requirements. With the help of deep learning and big data technology, they could deeply understand user preferences and needs, so as to customize customized services for users. 3. ** Deep cross-disciplinary integration **: To integrate more closely with biology, physics, chemistry, and other fields to promote the development of cross-disciplinary research and provide more innovative solutions to the many challenges facing mankind. 4. ** Enhanced autonomous decision-making and learning ability **: Future artificial intelligence systems will have higher autonomous decision-making ability and be able to analyze and make decisions in real-time in complex environments. At the same time, it would also have the ability to learn independently and continuously improve its performance through interaction with the environment. 5. ** Perfection of ethics and regulations **: With the development of technology, the ethical and legal issues related to artificial intelligence have become more prominent. In the future, a more complete ethical and legal system was needed to ensure the safe, reliable, and sustainable development of artificial intelligence technology. 6. ** Closer human-machine cooperation **: The future artificial intelligence will pay more attention to human-machine cooperation, fully realize the complementary advantages of humans and artificial intelligence, and then efficiently solve complex problems and promote social progress. 7. ** Development in the global competitive landscape **: From the perspective of the global competitive landscape, the United States currently leads the global artificial intelligence development in terms of the number of enterprises, the amount of funding, technological innovation, etc.; emerging markets such as Mainland China are rapidly emerging as important forces; the Middle East, Southeast Asia and other regions are full of enthusiasm for the development of artificial intelligence and have great potential, which is expected to become a new hot spot in global competition. 8. ** Continuous Empowerment and Transformation of Various Industries **: For example, in the search industry, the concentrated outbreak of AI technology and applications has pushed the AI search industry into a period of rapid development. As new products, new models, and new scenarios continue to emerge, there will be a new round of changes. In terms of international trade, while the application of artificial intelligence technology has a positive impact on it, its industrial chain is also affected by international trade. In the future, international cooperation needs to be strengthened to play a positive role in artificial intelligence in trade and ensure that it benefits all the economy. " A Short History of the Future: Legends of the Intelligent Era " was equally exciting. Everyone was welcome to click and read it!
Changes and Development of Artificial Intelligence
Artificial intelligence had undergone many major developments since its birth in the 1950s. The Dartmouth Conference in 1956 was a landmark event for the birth of the discipline. Since then, the development of AI has experienced a cycle of alternating between "AI Winter" and "AI Spring." There were four major development waves in its development process: 1. ** Early Symbolism AI (1956 - 1974)**: This was the dominant model of early AI research, also known as "classic AI" or "symbolic manipulation method." Its core was logical reasoning. Based on the assumption that human intelligence was essentially the operation of symbols, it simulated human intelligence by designing complex symbolic operating systems, using formal logic rules to represent knowledge, and drawing conclusions through reasoning mechanisms. 2. ** The rise of expert systems (1980 - 1987)**: Its core is rule-based reasoning. It codes expert knowledge into "if-then" rules, and then uses inference engines to operate on these rules to solve problems. 3. ** The rise of machine learning and statistical learning (1990s-2010s)**: Machine learning is a core sub-field of AI. Its core is the theory of statistical learning. By learning the rules of statistics from data, it constructs a prediction model to predict unknown data. Machine learning included many methods, such as supervised learning, unsupervised learning, and reinforcement learning. 4. ** Deep learning and large-scale neural network era (2012 -present)**: Deep learning is a branch of machine learning. It uses multi-layered artificial neural networks to learn the representation of data and has made breakthroughs in image recognition, natural language processing, and other fields. The rapid development of big data and computing power provided key support for the rise of deep learning. Massive amounts of data provided material for model training, and powerful computing power made it possible to train complex models. From the concepts involved in its development, there were also Connectionist AI, Actor AI, and so on. Connectionist AI attempted to simulate the structure of biological neural networks to achieve artificial intelligence. It believed that intelligent behavior was the result of a large number of simple units (similar to neurons) connecting and interacting with each other. The behavior AI emphasized the interaction between the agent and the environment. It believed that intelligent behavior was gradually formed through the perception-action cycle. It was widely used in robotic science and reinforcement learning. China had also made significant progress in the development of artificial intelligence, ranking second in the World Internet Development Index and seventh in information infrastructure. By the end of 2023, the total size of the data center racks in use in China exceeded 8.1 million standard racks, and the total computing power reached 230Eflops (230 quadrillion floating point operations per second), ranking second in the world. Moreover, the smart computing power reached 70Eflops, with a growth rate of more than 70%. A total of 14 national supercomputing centers were built, providing strong support for the development of artificial intelligence. In addition, China's Pangu model and other achievements played an important role in solving industrial problems and weather forecast, changing the direction of artificial intelligence development. "A Short History of the Future: Legends of the Intelligent Era" was equally exciting. Everyone was welcome to click and read it!
The speed of artificial intelligence development
The speed of development of artificial intelligence showed the characteristics of stages. In the initial stage, after the Dartmouth Conference in 1956 to 1965, artificial intelligence entered a period of rapid development. It made many achievements in the fields of machine learning and pattern recognition. For example, the " checkers program " defeated its designer in 1959, defeated the state checkers champion in 1962, the first character recognition program appeared in 1956, and the symbolic integral program was invented in 1963. In 1967, its upgraded version reached the expert level. However, due to technological limitations, the 1970s experienced a decade of slow development. The 1980s entered the second development climax. The expert rule system designed by the University of California at Yale achieved significant economic benefits, but then it entered the second winter due to the shortcomings of the expert system. In the 1990s, with the continuous breakthrough of computer computing power under Moore's Law, artificial intelligence ushered in an opportunity for development. For example, in 1989, handwritten text code digital image recognition was realized through the use of the Intranet, a voice assistant was designed in 1992, and the chess robot Deep Blue defeated the chess champion in 1997. Since 2006, with the establishment of a new architecture for contemporary neural networks by Jeffrey Sinton and Li Feifei's launch of the Imagenet project to open source large-scale image recognition data sets, the troika of computing power, algorithms, and data gathered. Artificial intelligence entered the fast lane and made breakthroughs in many fields. It was widely used in image recognition, natural language processing, and many other fields. Overall, its development speed had experienced rapid development in the early stages, fluctuations in the middle, and was currently in the stage of rapid development and application expansion. " A Short History of the Future: Legends of the Intelligent Era " was equally exciting. Everyone was welcome to click and read it!