The Path of Artificial Intelligence Development: From Logical Intelligence, Computational Intelligence to Parallel Intelligence The development process of artificial intelligence (AI) is an epic full of exploration and debate. From the initial pursuit of logical reasoning to the current attempts at complex computing and simulating human intelligence, the development path of AI has been tortuous yet full of hope. This article will outline the development of AI, explore the essence of intelligence, and elaborate on the importance of parallel intelligence as the future direction of AI development. 1. Tracing back intelligence: Hilbert's dream and Turing's computationalism The roots of AI can be traced back to the dream of mathematician David Hilbert. Hilbert proposed the "Hilbert Program" in an attempt to establish a complete, consistent, and decidable formal system for mathematics, achieving the mechanization of mathematical reasoning. This program has stimulated people's imagination of machine intelligence. Alan Turing is a key figure in the history of AI development. His concept of Turing machines laid the theoretical foundation for computationalist AI. The Turing machine is an abstract computational model that proves that any computable problem can be solved through a mechanical process. The Turing test provides a method for determining whether a machine has intelligence. Turing's work, as well as von Neumann's contributions to computer architecture, pointed the way for the development of AI and gradually formed the "Turing school" in AI development. 2. The Essence of Intelligence: G ö del's Challenge and Intuitionism However, the essence of intelligence is far more complex than computation. Kurt G ö del's incompleteness theorem poses a challenge to computationalist AI. G ö del's theorem states that any sufficiently complex axiomatic system has inherent limitations, namely the existence of propositions that cannot be proven true or false. This implies that human intelligence may surpass the capabilities of any form of system, and also forms the "G ö del school" in the development of AI. G ö del's work focused more on revealing the limitations of systems, and his ideas were closer to intuitionism, emphasizing that human intuition and cognitive abilities may surpass the limitations of computation. If Turing's work provided a "method" for the development of AI, then G ö del's work revealed the "boundaries" of AI development. 3. Classification of Intelligence: Algorithm Intelligence, Language Intelligence, and Imagination Intelligence In order to better understand the essence of intelligence, Professor Wang Feiyue categorizes intelligence into three types: algorithmic intelligence, linguistic intelligence, and imaginative intelligence. Algorithmic intelligence: This is the most fundamental form of intelligence, referring to the ability to perform specific tasks such as computation, search, sorting, etc. Algorithmic intelligence relies on clear rules and steps, and computers can efficiently execute algorithms. Language intelligence: Language intelligence refers to the ability to understand and use language, including natural language processing, language generation, dialogue interaction, and so on. Language intelligence enables us to communicate, exchange ideas, and transfer knowledge. Imagining Intelligence: This is the highest form of intelligence, referring to creative thinking, innovative ability, and the ability to solve complex problems. Imagining intelligence surpasses known rules and patterns, capable of generating new ideas and concepts. There is a hierarchical relationship between these three types of intelligence: algorithmic intelligence is the foundation, linguistic intelligence is built on top of algorithmic intelligence, and imaginative intelligence is built on top of linguistic intelligence. Higher level forms of intelligence rely on lower level forms of intelligence. As Einstein once said, "The true mark of intelligence is not knowledge, but imagination." This sentence emphasizes the importance of imagination in intelligence and also points out the higher goals of AI development. 4. The Path of Artificial Intelligence Development: Logical Intelligence, Computational Intelligence, and Parallel Intelligence The development process of AI can be summarized into three stages: logical intelligence, computational intelligence, and parallel intelligence. Logical Intelligence: This is the early stage of AI development, mainly focusing on symbolic reasoning and logical operations. Expert systems are representatives of logical intelligence, attempting to solve problems by simulating the knowledge and reasoning processes of human experts. Computational Intelligence: With the improvement of computing power and the increase of data volume, AI has entered the stage of computational intelligence. Machine learning, neural networks, and deep learning technologies have become mainstream, allowing AI to learn from data and perform pattern recognition and prediction. Parallel Intelligence: Traditional computational intelligence faces challenges in the face of complex systems and uncertain environments. Parallel intelligence has emerged, emphasizing the effective management and control of complex systems through the construction of interaction and feedback between artificial and real systems. 5. Parallel Intelligence: The Fusion of Turing and G ö del The design methodology of parallel intelligence can be seen as a combination of Turing's computationalist AI theory and G ö del's intuitionist design methodology beyond computation. It attempts to balance the accuracy of calculations with the flexibility of intuition, similar to the combination of traditional Chinese and Western medicine in treatment. Turing's contribution: Turing machines provided the computational foundation for parallel intelligence, enabling efficient computation and simulation of parallel systems. G ö del's revelation: G ö del's theorem reminds us that any model is a simplification of reality, and parallel systems need to constantly interact and feedback with real systems in order to continuously improve and adapt. 6. Bitcoin: The Practice of Parallel Intelligence Satoshi Nakamoto's Bitcoin can be seen as a practice of parallel intelligence. The Bitcoin system is a distributed and decentralized system that utilizes blockchain technology to transfer and store value. Parallel system: The Bitcoin system can be seen as a parallel system to the traditional financial system, which maintains its operation through consensus mechanisms and cryptographic techniques. Interaction and feedback: The operation of the Bitcoin system relies on the interaction and feedback of participants, for example, miners maintain the security of the system through mining, and users use the system through transactions. Self organization: The Bitcoin system has strong self-organization and can operate and develop without a central authority. The success of Bitcoin provides useful reference for the application of parallel intelligence. Conclusion: Future oriented AI development The development of AI is a continuous process of exploration and innovation. From logical intelligence to computational intelligence, and then to parallel intelligence, the development path of AI is full of challenges and opportunities. Beyond Calculation: The development of AI requires going beyond simple computation and placing greater emphasis on understanding the essence of human intelligence, including intuition, emotions, and consciousness. Human machine integration: The development of AI requires the integration of humans and machines, fully leveraging human creativity and machine computing capabilities to jointly solve complex problems. Ethical considerations: The development of AI requires attention to ethical issues and ensuring that its applications conform to human values and moral standards. Parallel intelligence, as a new paradigm of AI development, provides us with a broader perspective. It emphasizes the openness, interactivity, and self-organization of the system, providing new ideas for solving complex problems and achieving sustainable development. In the future development, we need to constantly explore and innovate in order to better understand the essence of intelligence and build more powerful and beneficial artificial intelligence systems.