Journal
AIHH public writing archive
The original AIHH blog now lives here. We keep the essays, metadata, and visuals intact while giving them a cleaner editorial home.
III. The Open-Source Ecosystem: The Ultimate Force Reshaping AI Development
Over the past decade, artificial intelligence (AI) has experienced remarkable growth, fundamentally transforming various industries. In the previous two articles, we explored (1) how market and societal demands serve as external drivers accelerating AI's rapid evolution, and (2) how data and computing power act as internal foundations that determine AI’s ultimate potential. However, beyond these two core factors, AI's rapid advancement has also been fueled by a critical catalyst—open source. Open source has not only accelerated AI innovation and adoption but has also lowered technical barriers, enabling researchers, enterprises, and developers worldwide to efficiently share knowledge, improve algorithms, and apply AI across diverse scenarios. This article reviews the development of AI from 2015 to the present, illustrating how open source has become **the ultimate force reshaping the AI landscape**.
II. The Evolution of Artificial Intelligence: A Paradigm Shift Driven by Computing and Data
In the late 1980s, artificial intelligence (AI) research underwent a significant paradigm shift, transitioning from rule-based systems to data-driven approaches. Early expert systems relied on meticulously constructed knowledge bases and inference rules, holding great promise. However, as their application scale expanded, challenges such as knowledge acquisition bottlenecks, rule explosion, poor adaptability, and computational resource limitations became increasingly evident, making it difficult for expert systems to handle complex and dynamic real-world environments.\n\nAt the same time, data-driven machine learning began to emerge. In 1986, the introduction of the **backpropagation algorithm** enabled **multi-layer perceptrons (MLPs)** to learn complex data patterns, reigniting academic interest in neural networks. With advancements in both data availability and computational power, AI research moved away from expert systems dependent on manually designed rules and shifted toward machine learning, with statistical learning and neural networks at its core. From this point onward, **data and computation became the driving forces behind AI’s progress**.
I. The Spiral Evolution of Artificial Intelligence: Lessons from Dartmouth to the Rise and Fall of Expert Systems
The explosion of ChatGPT in 2022 ignited a global AI race. Over the past two years, the frenzy of technological advancements and capital investment has propelled generative AI to an almost mythic status. However, as the limits of technological capabilities become increasingly apparent, expectations for **Artificial General Intelligence (AGI)** have begun to return to a more rational perspective. At the end of last year, **OpenAI experienced internal turmoil**, with both Chief Scientist **Ilya Sutskever** and Chief Technology Officer **Mira Murati** departing, further adding uncertainty to the AGI pathway centered around large language models. Meanwhile, China’s AI startup **DeepSeek** has rapidly risen, achieving performance breakthroughs with open-source models, though its advancements remain confined to the realm of engineering optimization. **As the technological hype gradually subsides, people are starting to seriously contemplate an important question: In an era where computing power and data have become fundamental infrastructure, how can we ensure that artificial intelligence truly serves all members of society?**