Decentralized Artificial Intelligence (DeAI): Use Cases, Catalysts, and Key Questions

Author: Darshan Gandhi, Founder of FutureX Labs; Translation: Jinse Finance xiaozou

In this article, let's explore the world of decentralized artificial intelligence (DeAI). We will learn about the following:

• The development life cycle of AI

• The need for decentralized AI

• Practical applications of DeAI

• Growth catalysts in the field of Crypto x AI

• Visionaries at the forefront

• What are the shortcomings of DeAI

• The future of Crypto x AI

1. Introduction to AI

I think it is safe to say that artificial intelligence (AI) will change the world. Imagine a child who recognizes animals by looking at pictures and remembering the names and characteristics of animals. Over time, the child's ability to recognize animals will improve.

AI works in a similar way, using data to learn and improve performance over time.

Many groundbreaking applications are built using AI, including:

• ChatGPT: Enables human-like conversations.

• Perplexity AI: Improves search accuracy.

• Jasper AI: Your writing assistant.

• DALL-E: Generates images from text descriptions.

• Pika Art: Makes HD videos from text.

There will be more and more applications like this. These tools are becoming part of our daily lives, making work easier and more efficient. Artificial intelligence is not just a futuristic concept, it is also actively solving major problems we face today.

The development of artificial intelligence is affecting and changing many industries, for example:

• Helping doctors diagnose diseases faster

• Enabling self-driving cars to drive safely

• Providing users with a personalized online shopping experience

  
  

In principle, artificial intelligence methods can be divided into three major categories:

• Centralized artificial intelligence: controlled by a single entity or enterprise.

• Decentralized AI: Focus on distributed control, transparency, and incentives.

• Open Source AI: Emphasis on promoting collaboration and transparency.

In this article, we will specifically explore "decentralized AI".

2. AI Development Lifecycle

Before we delve into the details, let's understand the components that shape the AI ​​development lifecycle. This will make it easier for us to understand the contribution of decentralization to each link.

Innovation in AI requires years of continuous progress, continuous feedback, training, and participation.

Developing an AI model involves several key stages to ensure a strong end-to-end operational flow. Here is a detailed look at the key phases of the AI ​​development lifecycle:

• Problem Statement, Identification, and Design

It all starts with identifying the business problem and defining the goals to be achieved.

Data collection is one of the most critical steps to ensure that the model uses accurate and relevant data.

• Data Collection and Exploration

This phase involves aggregating data from different sources and assessing the quality of that data.

Initial data analysis helps understand patterns and trends to develop a plan for data preprocessing and feature engineering (data improvement).

• Data Wrangling and Preparation

Data preprocessing cleans and transforms raw data into rich, usable datasets.

Use feature engineering to create new features from existing data to enhance the performance of the model.

• Model development

This phase selects the most appropriate machine learning practices based on the problem statement and the data collected.

The next step is to train and test the model to ensure that it can make accurate predictions.

The final step is optimization, which is to improve the efficiency of the model.

• Model deployment

Deploying the model to a live environment allows the model to start making predictions, recommendations, or whatever task it was trained on. To put it into production requires a computing power provider.

Continuous monitoring to ensure that the model remains accurate and effective.

Bias detection to ensure fairness in decisions.

• Model Maintenance and Retraining

Maintaining models requires regular updates and retraining with new data.

The focus is to collect as much feedback as possible and send it back to the model for adjustment and enhancement.

Today, most of these models come from research institutions, private companies or some open source organizations. Companies such as Google, OpenAI, IBM, AWS and Microsoft are some of the major players.

Below is a market map of GenAI models by different players in various verticals.

Let’s take a quick look at how AI technology has evolved over the years.

3. The need for decentralized AI

Centralized AI has its own problems. Think about it: a single point of failure could jeopardize everything.

Decentralized Artificial Intelligence (DeAI), on the other hand, changes the game by distributing data across multiple nodes, making the system more secure. If one node is compromised, the others will continue to operate normally. This setup also gives users more control over their data and reduces privacy risks, especially when using technologies such as Fully Homomorphic Encryption (FHE) and Zero-Knowledge Machine Learning (ZKML).

Censorship is another big problem with centralized systems. A single entity can control and manipulate information. Decentralized AI, on the other hand, disperses control, making it difficult for any single entity to dominate the narrative. This ensures that information is accessible and less susceptible to undue influence.

Transparency is a key factor in my opinion. The open source model, incentive mechanisms, and collaborative workflow management mean that anyone can check and verify decisions at any time. This level of openness addresses concerns about hidden biases and opaque processes in centralized systems. In addition, it allows more people to join and contribute. For example, people with idle computing space can now rent it out through decentralized computing providers like Akash and Render.

The decentralized model also limits the power of central entities, preventing AI from being abused for unfair purposes. By promoting collaboration and knowledge sharing, it can leverage collective wisdom and larger-scale governance, resulting in more reliable, open, and accurate systems.

Cryptocurrency acts as this enabler, combining the best of both worlds. It provides access to top services, computing, models, and data, while also providing incentive loops, security, and privacy protections for all stakeholders. This synergy ensures that DeAI is not only effective, but also fair and safe.

4. Practical Applications of DeAI

The following are some of the main applications of DeAI:

(1) By field

• Healthcare

DeAI improves healthcare by enabling secure private data sharing between medical institutions.

AI algorithms can analyze anonymous data to identify patterns, predict disease outbreaks, and develop personalized treatment plans. For example, patients can privately share their data with hospitals and ensure that only they own the data.

• Finance

Decentralized Finance (DeFi) is one of the largest sub-ecosystems of web3. AI can help enhance risk management and trading.

These protocols use AI to assess risk, predict asset prices, and optimize trading strategies. For example, many projects are developing tools for efficient asset management, AI-driven automated market makers (AMMs), and more.

• Security and Fraud Detection

AI algorithms can help the system detect and prevent fraud by analyzing patterns and anomalies in transaction data.

This increases the security of web3 protocols. For example, in the NFT ecosystem, AI can help identify fake assets and ensure integrity.

• Content/Sentiment Generation

AI can be used to create storylines, plots, game mechanics, and more.

For example, web3 games can use AI to generate game content from text descriptions and use smart contracts to manage ownership of assets such as characters and props.

Also, understanding how users feel about a category, problem, or market is invaluable. Tools like Kaito and Nansen aim to provide this capability.

• AI Agents and Automation

There are projects building autonomous AI agents for tasks in a variety of areas, from customer service to supply chain management.

These agents can be created by anyone or collaboratively, and all stakeholders can be rewarded automatically and seamlessly.

• User Experience

Web3’s user experience is not the best, but models can help enhance the user experience through personalized recommendations and behavior prediction.

Decentralized social networks are a good example, allowing users to choose content recommendation algorithms or manage their information flow according to their preferences.

（2）Division by the degree of ecosystem management

• Incentive Mechanism

Stakeholders can get rewards (earn tokens) by providing data, computing power or developing algorithms

There is a strong demand driving people to do this, everyone cooperates on difficult problems, and their time and effort will be reasonably rewarded.

• Cost Efficiency

DeAI platforms can help significantly reduce costs by leveraging unused resources in distributed networks. They eliminate the need for expensive data centers and ensure that resources are used to the maximum extent.

For example, projects such as Akash Network, Aethir, and Render allow users to rent out unused computing power for AI tasks, thereby improving efficiency.

• Governance

DeAI can also be used to improve governance processes, especially for protocols and DAOs.

AI can automate reputation management and rewards, for example, ensuring that contributions are treated fairly in DAOs.

5. Growth catalysts in the Crypto x AI field

There are some powerful catalysts driving the convergence of Crypto and AI. Let's take a look at a few of them.

First, financing in the ecosystem has been increasing. In the past year, there have been 136 rounds of financing of US$1.02 billion, with an average of US$7.5 million per round. Well-known investment companies such as Hack VC, Variant, Paradigm and Polychain have been making large-scale investments. The influx of capital has accelerated research and innovation in the field.

Second, the technology aims to provide a cost-effective alternative to centralized systems. It can reduce potential operating costs by nearly 50% and efficiently process large data volumes while also providing security and privacy protection. For example, compared with AWS, GCP and Azure, Akash claims to offer an 85% discount on computing power supply.

Third, by market capitalization, leading projects in the field, such as Bittensor, Akash, Render, and Worldcoin, have performed exceptionally well in the secondary market over the past year. These projects are the best performing assets in web3. According to Coinbase's report, the Crypto x AI category also performed well in each category.

Fourth, NVIDIA's performance in April this year was very good. Let's look at some numbers in the news reports:

• Their revenue in the first quarter of 2024 was US$26 billion, an increase of 18% from the fourth quarter of 2023 and 262% from the same period last year.

• In the first quarter of this year, GAAP diluted earnings per share were US$5.98, an increase of 21% from the previous quarter and 629% from the same period last year.

Fifth, all centralized services, including Google.com, Chatgpt, and Perplexity, have recently gone down together, while all web3 services are intact and running well. The founder of Akash Network posted the following tweets before and after the incident.

Due to these and many other similar initiatives, events and innovations, the field is developing rapidly.

6. Visionaries at the forefront

The ecosystem is gathering increasing momentum due to the support and participation of some key industry figures.

• Erik vooorhees

Erik Voorhees, the founder of ShapeShift and a hugely influential Twitter personality, launched Venice AI to create a permissionless alternative to popular web2 LLMs such as ChatGPT.

Venice focuses on user privacy and censorship resistance, using open source technology to provide uncensored and unbiased information.

• Emad Mostaque

is the founder and former CEO of Stability AI, who has now left to focus on the field of DeAI - developing Schelling AI.

He believes that as artificial intelligence becomes more and more important, transparent and distributed governance will become extremely important.

• Niraj Pant

Formerly a partner at Polychain Capital, he is now developing Ritual.net.

The platform aims to build a sovereign execution layer for AI, enabling the open-source, permissionless creation, distribution, and improvement of AI models.

The first phase of Ritual.net (Infernet) allows developers to access models on and off the chain through smart contracts.

7. Shortcomings

While decentralized AI has many benefits, it has also encountered significant challenges that deserve attention. Here are the key issues it currently faces:

• Initial setup costs and challenges

There are considerable difficulties in establishing a DeAI network. It takes a lot of time and resources to build the necessary infrastructure and attract participants. This cold start problem highlights the need for strong incentives to attract early adopters. However, without reaching a large enough scale, the network has had difficulty gaining traction.

• Increased Coordination Needs

Managing a decentralized network is complex. It takes a lot of work to synchronize multiple nodes and stakeholders, ensure data consistency, maintain network security, and run the network cost-effectively. While this coordination embodies the essence of Crypto x AI, it can sometimes become inefficient and cumbersome.

• Scaling Challenges

The network faces scaling issues. Handling the ever-increasing amount of data and transactions without degrading performance is a major challenge today. Due to varying node uptime, decentralized networks may experience latency and bandwidth issues, affecting overall efficiency. Solutions like sharding are still under development and may not fully alleviate these issues.

• Access to Resources

Enterprises often face barriers when it comes to accessing cutting-edge resources. Major centralized vendors can invest heavily in the latest hardware and software, giving them a competitive advantage. DeAI projects, however, are constrained by limited funding and may fall behind, affecting their performance and capabilities. For example, NVIDIA tends to prioritize resources to hyperscale servers such as GCP, Azure, and AWS due to higher demand. However, for web3 vendors, supply currently outstrips demand, or they may still be in the early stages of development.

• Regulatory and Legal Challenges

Crypto largely operates in a regulatory gray area. The lack of a clear regulatory framework can create legal risks and uncertainty. In a decentralized environment, complying with regulations such as GDPR becomes more challenging, exacerbating an ongoing global struggle.

8. The Future of Crypto x AI

The convergence of crypto and AI is expected to foster the development of innovative projects and applications dedicated to solving real-world challenges.

In our later articles, we will delve into several key subcategories in the crypto space. We will explore zero-knowledge machine learning (zkML) through projects such as Modulus Labs and Giza, which are developing products centered around model reasoning. In addition, we will examine decentralized cloud computing vendors such as Render, Akash Network, and Aethir, highlighting their role in providing scalable and cost-effective alternatives to traditional cloud services.

• Bittensor:The project is developing a decentralized network that incentivizes participants to share AI models and datasets through a blockchain and uses “subnets” to reward contributions.

• Fetch:Fetch focuses on the autonomous AI agent market, providing integrations with top services such as ChatGPT and Slack, facilitating alignment through simple API integrations.

• Akash Network:Focusing on building a decentralized marketplace for providing cloud computing resources, Akash Network utilizes its AKT token for governance, security, and in-network transactions.

9. Conclusion

I firmly believe that decentralized artificial intelligence (DeAI) will be a game changer, and we are just beginning to see its development in the ecosystem.

DeAI embodies the principles of transparency, collaboration, and global impact. As we have discussed, it is reshaping various key sectors.

Projects like Render, Akash, and Worldcoin, with their outstanding traction and funding, not only highlight the huge potential of this field, but also indicate that it may experience substantial growth in the coming years.

Looking forward, we will delve deeper into the various subcategories of Crypto x AI and continue to explore this dynamic vertical.

The future is bright, and we are just getting started.