Development History of the AI Industry and Its Relationship with Crypto Assets

Since the emergence of artificial intelligence technology in the 1950s, it has undergone several waves of development. Currently, mainstream deep learning technology is represented by neural networks, which continuously optimize model parameters through training on large amounts of data to achieve fitting for complex tasks.

The development of deep learning has undergone evolution from the earliest neural networks to structures such as RNN and CNN, eventually evolving into the widely used Transformer architecture today. This technical route has greatly enhanced the generalization ability of AI systems, allowing them to adapt to various modal inputs and outputs.

In terms of the industrial chain, the training and inference of deep learning models require substantial computing power support, with GPUs becoming the primary hardware choice. At the same time, a massive amount of high-quality data is also crucial for ensuring model performance. Therefore, around the two key elements of computing power and data, a complete industrial ecosystem has formed, including GPU suppliers, cloud service providers, data service providers, and more.

The combination of the Crypto Assets industry and AI is mainly reflected in the following aspects:

1. Provide a decentralized GPU computing power sharing network to activate idle computing resources.

2. Promote the collection and sharing of high-quality data through token incentive mechanisms.

3. Utilize privacy computing technologies such as zero-knowledge proofs to achieve secure data usage.

4. Develop blockchain-based AI agent ( Agent ) system to achieve automated on-chain interaction.

5. Build public chain infrastructure specifically for AI applications.

Overall, cryptocurrency technology can provide new value discovery and circulation mechanisms for the AI industry chain, while the trustless nature of blockchain can also address some trust issues in AI applications. However, in the actual implementation process, decentralized systems still have shortcomings in performance and development convenience, which need further optimization.