The Integration of AI and Blockchain: A Panorama from Infrastructure to Applications

The rapid development of the artificial intelligence industry in recent times is seen by some as the beginning of the Fourth Industrial Revolution. The emergence of large language models has significantly improved efficiency across various industries. According to estimates by the Boston Consulting Group, GPT has brought about a roughly 20% increase in overall work efficiency in the United States. At the same time, the generalization capability of large models is viewed as a new paradigm for software design. Past software design was about precise code, while current software design increasingly involves embedding the strong generalization capabilities of large model frameworks into software, allowing for better performance and support for a wider range of modal inputs and outputs. Deep learning technology has indeed brought a new wave of prosperity to the AI industry, and this trend is gradually spreading to the cryptocurrency industry.

The Development History of the AI Industry

The artificial intelligence industry began to take shape in the 1950s. In order to realize the vision of artificial intelligence, academia and industry have developed various schools of thought to achieve artificial intelligence across different eras and disciplines.

Modern artificial intelligence technology mainly uses the term "machine learning", whose core idea is to allow machines to iterate repeatedly based on data to improve system performance in tasks. The main steps include inputting data into algorithms, training models with this data, testing and deploying models, and finally using the models to complete automated prediction tasks.

Currently, there are three main schools of thought in machine learning: connectionism, symbolicism, and behaviorism, which respectively mimic the human nervous system, thinking, and behavior. Among them, connectionism, represented by neural networks, currently dominates and is also known as deep learning. This architecture consists of an input layer, an output layer, and multiple hidden layers. When the number of layers and neurons is sufficiently large, it can fit complex general tasks. By continuously inputting data and adjusting the parameters of the neurons, the neural network can ultimately reach an optimal state after training on a large amount of data. This is also the origin of the term "deep" - a sufficient number of layers and neurons.

The deep learning technology based on neural networks has also undergone multiple technical iterations and evolutions, from the earliest neural networks to feedforward neural networks, RNNs, CNNs, GANs, and finally developing into modern large models like GPT that use Transformer technology. The Transformer technology is an evolutionary direction of neural networks, which adds a converter ( Transformer ) to encode data from various modalities ( such as audio, video, images, etc. into corresponding numerical representations. These encoded data are then input into the neural network, enabling the neural network to fit any type of data, thereby achieving multimodal processing capabilities.

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The development of AI has gone through three waves of technological advancements. The first wave occurred in the 1960s, a decade after the introduction of AI technology. This wave was mainly driven by the development of symbolic technology, which addressed general natural language processing and human-computer dialogue issues. During the same period, expert systems were born, exemplified by the DENRAL expert system completed with the support of NASA at Stanford University, which possessed extensive chemical knowledge and could generate answers similar to those of a chemistry expert through problem inference.

The second wave of AI technology occurred in 1997, when IBM's "Deep Blue" defeated chess champion Garry Kasparov with a score of 3.5 to 2.5, a victory seen as a milestone in the development of artificial intelligence.

The third wave of AI technology began in 2006. The three giants of deep learning, Yann LeCun, Geoffrey Hinton, and Yoshua Bengio, proposed the concept of deep learning, which is an algorithm that uses artificial neural networks as architecture to perform representation learning on data. Since then, deep learning algorithms have continuously evolved, from RNN, GAN to Transformer and Stable Diffusion; these algorithms have collectively shaped the third wave of technology and marked the peak of connectionism.

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Deep Learning Industry Chain

Current large language models mainly use deep learning methods based on neural networks. The large models represented by GPT have sparked a new wave of artificial intelligence, attracting a large number of players into this field. We find that the market demand for data and computing power has surged significantly, therefore in this part of the report, we mainly discuss the industrial chain of deep learning algorithms, analyzing how the upstream and downstream are composed in the AI industry dominated by deep learning algorithms, as well as the current situation, supply-demand relationship, and future development trends of the upstream and downstream.

The training of large language models such as GPT based on Transformer technology ) LLMs ( is mainly divided into three steps:

1. Pre-training: By providing a large amount of data pairs to the input layer, the optimal parameters for each neuron in the model are sought. This process requires massive data and is the most computation-intensive phase.

2. Fine-tuning: Use a smaller quantity of high-quality data for training to improve the quality of the model output.

3. Reinforcement Learning: Establish a "reward model" to evaluate the output quality of the large model, and improve the parameters of the large model iteratively in this way.

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During the training process of large models, the more parameters there are, the higher the upper limit of their generalization ability. Therefore, the three main factors affecting the performance of large models are: the number of parameters, the amount and quality of data, and computing power. These three elements jointly determine the quality of the results and the generalization ability of large models.

The main links in the industrial chain include:

1. Hardware GPU providers: Currently, Nvidia is in an absolute leading position in the AI chip market. The academic community mainly uses consumer-grade GPUs like the RTX series ), while the industrial sector primarily uses chips such as H100 and A100 for the commercialization of large models.

2. Cloud Service Providers: Provide flexible computing power and hosted training solutions for AI companies with limited funds. Mainly divided into three categories: traditional large cloud vendors ( such as AWS, Google Cloud, Azure ), vertically specialized cloud computing platforms ( such as CoreWeave, Lambda ), and emerging inference-as-a-service providers ( such as Together.ai, Fireworks.ai ).

3. Training data source providers: Provide the model with a large amount of high-quality or domain-specific data. Some companies specialize in data collection and annotation work.

4. Database Providers: Specialized vector database solutions for AI data storage and processing. Major players include Chroma, Zilliz, Pinecone, Weaviate, and others.

5. Edge Devices: Including energy supply and cooling systems to support the operation of large-scale GPU clusters. As the scale of AI models grows, the demand in this field is also rapidly increasing.

6. Application Development: Develop various applications in vertical fields based on large models, such as intelligent assistants, content generation tools, etc. Currently, application development is relatively lagging behind infrastructure construction.

The Combination of Blockchain and AI

The combination of blockchain technology and AI is mainly reflected in the following aspects:

1. Value Restructuring: Token economics can redefine value in various segments of the AI industry chain, incentivizing more participants to delve into niche tracks within the AI sector.

2. Trust Mechanism: The decentralization and immutability of Blockchain can provide a reliable data processing environment for AI applications, addressing data privacy and security issues.

3. Resource Sharing: Through the Blockchain network, global sharing of idle GPU computing power can be achieved, improving resource utilization efficiency.

4. Data Market: Blockchain can build a fair and transparent trading market for AI training data, incentivizing individuals and organizations to contribute high-quality data.

5. Model Verification: By using cryptographic techniques such as zero-knowledge proofs, the correctness of AI inference results can be verified while protecting the privacy of the model.

In the ecosystem where Crypto and AI converge, the following types of projects have mainly emerged:

1. Distributed GPU computing network: such as Render, Akash, etc., aiming to build a decentralized GPU computing market.

2. AI Data Providers: Such as EpiK Protocol, Synesis One, Masa, etc., are committed to establishing a decentralized AI training data market.

3. ZKML( Zero-Knowledge Machine Learning ): Combining Zero-Knowledge Proof technology to achieve AI training and inference under privacy protection.

4. AI Agent (: Such as Fetch.AI, creating a network of AI agents capable of autonomously executing tasks.

5. AI Public Chain: Blockchain networks designed specifically for the development and deployment of AI models, such as Tensor, Allora, etc.

Although the combination of Crypto and AI is still in its early stages and faces challenges such as performance and privacy, this field shows great potential for innovation. With advancements in technology and improvements in the ecosystem, we have reason to expect that the deep integration of AI and Blockchain will bring revolutionary changes to both industries.

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