The Integration of AI Industry Development and Crypto Assets

The artificial intelligence industry has flourished in recent years and is regarded as an important component of the Fourth Industrial Revolution. The emergence of large language models has significantly improved efficiency across various sectors, with Boston Consulting estimating that GPT has enhanced work efficiency in the United States by about 20%. The generalization ability of large models is seen as a new software design paradigm, where modern software increasingly embeds generalized large model frameworks to achieve better performance and broader modal support compared to traditional precise code design. Deep learning technology has ushered in a new wave of prosperity for the AI industry, and this trend has also extended to the Crypto Assets industry.

Development History of the AI Industry

The AI industry started in the 1950s, and academia and industry have proposed various technical routes for implementing artificial intelligence at different times. Currently, the mainstream methods are based on machine learning, which centers on the idea of allowing machines to improve system performance through iterative processing of large amounts of data. Machine learning is mainly divided into three schools: connectionism, symbolicism, and behaviorism, which respectively mimic the human nervous system, thinking, and behavior.

Currently, connectionism represented by neural networks dominates and is also known as deep learning. Neural networks have input layers, output layers, and multiple hidden layers, fitting complex general tasks through massive parameters and data training. Deep learning technology has undergone several evolutions, from early neural networks, RNNs, and CNNs, to modern Transformers.

The development of AI has gone through three technological waves:

1. In the 1960s, symbolic technology triggered the first wave, addressing the issues of general natural language processing and human-computer dialogue.

2. In the 1990s, IBM's Deep Blue defeated the world chess champion, marking the second peak of AI.

3. Since 2006, the rise of deep learning has triggered the third wave. The three giants of deep learning proposed related concepts, and subsequently, algorithms such as RNN, GAN, and Transformer have continued to evolve.

In recent years, there have been several milestone events in the field of AI:

• In 2011, IBM Watson defeated humans in the Jeopardy! quiz show.
• In 2016, AlphaGo defeated the world champion Go player Lee Sedol.
• In 2017, Google proposed the Transformer algorithm.
• The GPT series models continued to break through from 2018 to 2020.
• ChatGPT launched in 2023 and quickly became popular.

Deep Learning Industry Chain

Current mainstream large language models are based on deep learning methods. Large models represented by GPT have triggered a new wave of AI frenzy, with a large number of players entering this track. We can analyze the industrial chain structure of deep learning from dimensions such as data and computing power.

The training of large models is mainly divided into three steps:

1. Pre-training: Requires massive amounts of data and computing power, which is the most resource-intensive phase.

2. Fine-tuning: Use a small amount of high-quality data to improve model quality.

3. Reinforcement Learning: Continuously iterating and optimizing model outputs through feedback.

The three key factors affecting the performance of large models are the number of parameters, the amount/quality of data, and computing power. Taking GPT-3 as an example, it has 175 billion parameters, approximately 570GB of training data, and requires enormous computing power support.

The deep learning industry chain mainly includes:

• GPU hardware: High-performance GPU chips provided by manufacturers such as Nvidia.
• Cloud Services: AWS, Google Cloud, etc. provide computing power support
• Data: Various data providers and annotation companies
• Database: Vector databases and other storage for massive data
• Edge devices: cooling systems and other supporting devices
• Application: AI applications in various vertical fields

The Combination of Crypto Assets and AI

Blockchain and Crypto Assets technology can bring new value discovery and reconstruction mechanisms to the AI industry chain:

1. Token economics can incentivize more people to participate in various aspects of the AI industry, gaining returns beyond cash flow.

2. Decentralized ledgers can solve the trust issues of data and models, enabling collaboration under data privacy protection.

3. The global value network can activate idle computing power and reduce costs.

4. Smart contracts can enable automated trading and usage of AI models.

The main directions for the combination of Crypto Assets and AI currently include:

• GPU Cloud Computing Market: Projects like Render provide a decentralized computing network.
• Data Market: EpiK Protocol and others provide data services for AI
• ZKML: Implementing model training and inference under privacy-preserving computation
• AI Agent: Projects like Fetch.AI are building intelligent agent networks.
• AI Public Chain: A blockchain network designed specifically for AI

Although the current application of AI + Crypto Assets is still in its early stages, this combination is expected to reshape the AI industry chain and create new value. In the future, as technology advances, the integration of the two fields will become closer.