The history of computer science is a magnificent journey of humanity’s continuous exploration of the essence of computation and the simulation and transcendence of our own intelligence. From the early mechanical calculations to today’s artificial intelligence, five milestone papers have not only laid the foundation of modern computer science but also guided us toward the future.

Stage One: Mechanized Human Thinking

In the first half of the 20th century, three pioneers, with their remarkable insights, abstracted mathematics into logical operations and, based on “bits,” initiated the era of mechanized simulation of human thought.‍

• ‍Alan Turing’s “On Computable Numbers, with an Application to the Entscheidungsproblem” (1936) introduced the concept of the “Turing Machine,” an abstract computational model capable of simulating any computable process. The Turing Machine not only defined the boundaries of “computability” but also foreshadowed the possibility of universal computers, laying the mathematical foundation for the birth of computers.‍
• Claude Shannon’s “A Mathematical Theory of Communication” (1948) established the foundation of information theory, defining the concept of information with mathematical formulas and quantifying the rules of information transmission. Shannon’s work had a profound impact on the field of communications and provided the theoretical basis for data compression, encryption, and the development of the internet.
• ‍Warren McCulloch and Walter Pitts’ “A Logical Calculus of the Ideas Immanent in Nervous Activity” (1943) was the first attempt to describe neural activity in the brain using mathematical models. Their simplified model of neurons demonstrated that neural networks could perform any logical function, establishing the theoretical foundation for the development of neural networks and deep learning, and sowing the seeds for the rise of artificial intelligence.

The commonality among these three papers is that they transformed complex mathematics and logical operations into simple binary operations—bits (0 and 1). The Turing Machine used tape to read and write “0” and “1”; Shannon’s information theory used bits as the unit of information; McCulloch-Pitts’ neuron model used “activated” and “inactive” states to simulate neural behavior. In this way, human thought processes were translated into mechanized logical operations, forming the theoretical foundation for the birth of computers and the development of artificial intelligence.

Stage Two: Exploring Emergent Intelligence and Value Networks

With the continuous development of computer science, attention began to shift to more complex problems, such as solving NP-complete problems and building decentralized value networks. During this stage, two groundbreaking papers pointed the way forward.

• ‍Richard Karp’s “Reducibility Among Combinatorial Problems” (1972) proved that 21 classic combinatorial optimization problems are NP-complete, revealing the nature of computational complexity and providing theoretical guidance for solving practical problems. His research not only advanced algorithm design and complexity theory but also offered critical insights for fields such as cryptography and artificial intelligence.‍
• Satoshi Nakamoto’s “Bitcoin: A Peer-to-Peer Electronic Cash System” (2008) introduced the concept of blockchain and cryptocurrencies and designed a decentralized electronic cash system that does not rely on any central authority but instead uses cryptography and consensus mechanisms to ensure system security and stability. Bitcoin’s emergence not only changed the landscape of finance but also opened up new directions for the development of the internet.

Both of these papers delve into the study of complex systems, which possess the characteristic of “emergence”—the behavior of the whole is not a simple sum of the individual parts but results in new, unpredictable intelligence. Karp’s theory of NP-completeness uncovered the essence of complex problems, while Nakamoto’s Bitcoin used blockchain technology to create a decentralized complex system.

Wiener’s “Meaningful Internet”

The founder of cybernetics, Norbert Wiener, proposed the concept of a “meaningful internet.” He argued that the development of the internet should not be limited to just the transmission of information but should focus on how to imbue information with “meaning” so it can create value. Nakamoto’s Bitcoin is a prime example of putting this concept into practice, as it uses blockchain technology to build a decentralized value network, making the transmission and creation of value independent of centralized institutions.

Looking Ahead

From mechanical thinking to emergent intelligence, the development of computer science has been filled with challenges and opportunities. We believe that under the guidance of these great pioneers, the future of computer science will continue to explore the mysteries of complex systems, build smarter and more valuable networks, and ultimately realize the beautiful vision of harmonious coexistence between humans and machines.