Artificial Intelligence Development History and Pre‑training Model Trends

The China Academy of Information and Communications Technology recently released the Artificial Intelligence White Paper (2022) . The paper states that AI has entered a new development stage, defined by a three‑dimensional coordinate of technology innovation, engineering practice, and trustworthy security. Algorithms, computing power, and data are regarded as the three driving forces of AI. In the algorithm domain, ultra‑large‑scale pre‑training models have become a hot focus, with Baidu’s ERNIE 3.0 achieving a GLUE score above 90%, ranking first worldwide.

01 Artificial Intelligence Development History

In 1941 the world’s first computer was created; fifteen years later, the Dartmouth Conference (1956) introduced the term “Artificial Intelligence,” marking its formal birth. The first AI wave aimed to write great algorithms to simulate human thought, but limited computing power prevented progress. A second wave emerged in the 1980s when Japan and the United States invested heavily in fifth‑generation computers, yet even Moore’s‑law‑scaled chips could not meet the required compute, and the lack of data caused this wave to stall as well.

Thanks to breakthroughs in deep‑learning algorithms, continuous improvements in compute, and massive data accumulation, AI moved from laboratories to industrial practice. In 2016, AlphaGo defeated world Go champion Lee Sedol, showcasing a new generation of AI that can master tasks through machine learning and even create novel strategies unseen in human experience.

02 What Is Pre‑training

If AI model capability is likened to education levels, training a domain‑specific model previously required starting from kindergarten and progressing to the target level (e.g., university), which is time‑consuming and costly. Pre‑training aggregates large‑scale, low‑cost data to learn general knowledge—equivalent to a high‑school level model. For domain‑specific, higher‑level performance, the model is fine‑tuned with specialized labeled data, producing a specialized model while the high‑school‑level model serves as a “large model.”

Training a large model demands not only advanced algorithms but also massive data and compute, incurring huge expenses that only large enterprises can typically afford.

03 Pre‑training Significantly Lowers the Threshold for AI Applications

Deep‑learning algorithms have sparked the AI wave, achieving or surpassing human performance in computer vision, speech, and natural language processing. Before pre‑training, applying large‑scale deep‑learning models in NLP required substantial expertise and resources. Pre‑training models dramatically reduce cost and entry barriers by enabling model reuse; a general large model can be cheaply adapted to specific domains (e.g., finance) through fine‑tuning. This transfer‑learning approach captures contextual semantics implicitly, achieving strong results across virtually all NLP tasks while remaining highly extensible.

For those interested in large‑model practice, the official Baidu Wenxin platform provides learning materials and tools: https://wenxin.baidu.com/ .

04 Why Pre‑training Large Models Enable Rapid Application

Overall, large models have developed rapidly in the past two years and gained swift industrial adoption. However, AI models still face challenges, chiefly limited generality—most models are trained for specific domains and perform poorly elsewhere.

1. Model fragmentation: Large models offer a universal solution via “pre‑training + downstream fine‑tuning,” capturing knowledge from massive labeled and unlabeled data and expanding generalization. In NLP, shared pre‑training tasks and parameters allow the same model to serve translation, QA, text generation, etc.

2. Self‑supervised learning reduces annotation costs: By minimizing the need for manual labeling, small‑sample learning can achieve strong performance, and larger parameter scales amplify these advantages, lowering development costs.

3. Potential to break current accuracy limits: Historically, accuracy gains stem from architectural innovations. As data and model scales continue to grow, larger models can surpass existing precision ceilings.

05 Three Development Trends of Pre‑training

Pre‑training models are evolving along three major trends: (1) Models become increasingly large, with deeper Transformer layers and higher capability, albeit at rising training costs; (2) Training methods diversify, including various auto‑encoding and multitask strategies; (3) Multimodal expansion, moving from text‑only to joint text‑image‑speech learning, paving the way toward more general AI.

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