80 Years of AI Development: Review and Outlook

In the 80 years of development in the field of artificial intelligence, we have witnessed the ups and downs of funding, the diversification of research methods, and the fluctuations of public sentiment. This history provides us with valuable lessons that are worth deep reflection.

In December 1943, neurophysiologists McCulloch and logician Pitts published a groundbreaking paper proposing the concept of an idealized neural network. Although this paper had limited impact in the field of neuroscience, it laid the foundation for future research in artificial intelligence. However, we need to be cautious in distinguishing engineering, science, and speculation, avoiding the erroneous perception of equating humans with machines.

In the past few decades, the anticipation of the imminent realization of General Artificial Intelligence (AGI) has repeatedly sparked excitement. From the 1950s to the 1980s, several AI pioneers optimistically predicted the arrival of AGI. These predictions even influenced government investment decisions. However, reality often diverges significantly from expectations. We should approach new technologies rationally and avoid falling into the trap of excessive optimism.

A common misconception in the development of AI is the "first step fallacy," which assumes that once a preliminary breakthrough is achieved, a perfect solution is just around the corner. However, the gap between being unable to complete a task and barely completing it is often much larger than the gap between barely completing it and achieving perfection. We need to objectively assess the current state and potential of AI technology.

The development of AI also tells us that early success and widespread application do not guarantee long-term sustainability. The expert systems that emerged in the 1980s are a typical example. Although they were widely adopted for a time, they eventually declined due to difficulties in knowledge acquisition and updates. This reminds us to carefully assess the long-term development prospects of new technologies.

The competition between symbolic and connectionist schools of thought has long existed in AI research. In recent years, connectionism has dominated, but we should not overlook the potential of other research directions. A diversified research strategy may be more beneficial for the long-term development of AI.

Finally, the success of Nvidia gives us the insight that we must remain vigilant and be ready to respond to market changes at any time. At the same time, we should draw lessons from the history of AI development and view the evolution of AI technology with a more rational and long-term perspective.

The future of the AI field is full of opportunities and challenges. We need to find a balance between enthusiasm and rationality, actively exploring the potential of AI while objectively recognizing its limitations. Only in this way can we truly promote the healthy development of AI technology and create greater value for human society.