Blockchain-Enhanced Artificial Intelligence: The Future of Reliable and Ethical Compound AI Systems

What is Generative AI?

Generative AI are AI models that can create new and original content based on data or input. These models can generate different types of content such as images, text, or audio. Generative AI is used in many fields like creative work, automatic content production, and personalized recommendations. These systems learn from large datasets and provide creative solutions tailored to users' needs.

What is an AI Agent?

An AI Agent is a software designed to perform specific tasks and learn by interacting with its environment. By giving a large language model (LLM) a role, a goal, and a background story, an AI Agent structure can be created. AI Agents collect data from their surroundings, analyze it, and take actions according to specific objectives.

For example, a chatbot used in customer service or software working as a financial advisor can be defined as an AI Agent. Thanks to their learning and adaptation skills, AI Agents can offer solutions to complex problems.

What are Composite AI Systems?

Composite AI systems combine multiple AI models to perform more complex and comprehensive tasks. These systems merge the strengths of different AI models to provide more efficient and effective solutions. Composite AI systems work collaboratively, exceeding the limitations of individual models and solving advanced problems effectively. Tests have shown that composite AI systems built with ChatGPT 3.5 models can produce better results than a single ChatGPT 4 model. This approach provides significant advantages, especially when working with large datasets and analyzing various types of data.

Ethical Discussions in Artificial Intelligence

The neutrality, privacy, data security, and accountability of AI systems are at the center of ethical discussions. At the Generative AI Summit supported by Superteam Turkey on February 17, 2024, we discussed the following ethical concerns:

1. Neutrality and Avoiding Bias: Ensuring neutrality in AI decisions and preventing discrimination is a top ethical concern. Biased datasets used in AI training can lead to unfair results. Therefore, it is important to carefully select datasets and make them unbiased.

2. Data Privacy: Protecting user data and ensuring its proper use are critical for the ethical use of AI. Personal data must be secured, and its sharing without user consent should be avoided.

3. Data Security: Ensuring data security in AI systems builds user trust. If data security is not guaranteed, malicious actors might exploit this data for harmful activities. Therefore, data security must be ensured during the training and use of AI models.

4. Responsibility and Accountability: Who is responsible for decisions made by AI systems? Who will bear the consequences of incorrect decisions? These questions raise important ethical issues about accountability in AI systems. Transparency and traceability are essential to address these concerns.

5. Transparency and Explainability: Users must understand how AI systems make decisions and the data they rely on. If decision-making processes are not transparent, it might lead to distrust among users. AI should be explainable so that users can understand how these systems work.

6. Manipulation and Misinformation: AI systems can be used to spread false or misleading information, leading to unethical scenarios. For instance, technologies like deepfake pose a significant threat to information manipulation. Preventing such ethical issues requires developing and monitoring AI systems under strict ethical standards.

Developing AI within an ethical framework is essential to building trustworthy and fair systems.

What Does the New EU AI Regulation Say?

The European Union has introduced new regulations to ensure the ethical and legal use of AI technologies. The EU AI Act aims to develop and use AI systems that are safe, transparent, and aligned with ethical principles. This regulation classifies AI systems based on their risk levels and subjects high-risk systems to stricter controls.

Additionally, it mandates tracking mechanisms to record the data entering and leaving AI models. This ensures that the source of training data can be verified, and decision-making processes are more transparent. With these legal arrangements, the EU aims to ensure that AI aligns with human rights and ethical values.

How Does Blockchain Solve Ethical Concerns?

Blockchain technology helps address ethical issues that may arise in AI systems. Its decentralized structure enables data to be tracked transparently and reduces manipulation risks. Storing data on the blockchain guarantees its accuracy and immutability, ensuring the security and integrity of training data for AI models. However, it’s important to note that not all training data should be stored on the blockchain. Instead, hash values of the data can be recorded and verified.

Smart Contracts and Traceability: Blockchain-based structures enable transparency in AI processes through smart contracts. Smart contracts are programs that automatically execute when certain conditions are met, making AI systems’ data management and decision-making processes traceable. This allows the sources and usage of data in AI systems to be verified.

Data Security and Transparency: Data stored on the blockchain is immutable and traceable, ensuring the security of the datasets used by AI systems. This helps guarantee the data's accuracy and prevents manipulation. Storing the hash values of training data on the blockchain ensures transparency and accountability throughout the training process.

Accountability and Responsibility: Blockchain increases the traceability of data entering and exiting AI models by recording it. Decisions made by AI models and the data they rely on can be verified with blockchain records. This improves accountability in AI systems and helps prevent ethical violations.

The Impact of Blockchain in Applying EU Regulations

Blockchain technology plays a critical role in meeting the ethical requirements set by EU regulations, both directly and as an upper-layer solution.

Frameworks and Libraries for Building AI Agents

LangChain: LangChain is a framework designed to make large language models (LLMs) more effective. It integrates LLMs with data sources and user applications to perform chain-based operations. LangChain provides tools for developers to manage tasks, create context, and handle more complex processes for AI Agents. Its flexible modular structure accelerates the process of developing custom agents and enables comprehensive solutions by combining different data sources.

CrewAI: CrewAI is a platform that simplifies the creation of collaborative AI models. It is ideal for building composite AI systems by combining different AI models. CrewAI is designed for developers working on task-based AI applications, offering protocols for inter-model communication and optimized collaboration strategies. Its features for data and model management ensure that AI Agents work more efficiently and cohesively. CrewAI is a powerful tool for teams looking to integrate multiple AI models into complex workflows.

A New Economic Model: AI Agent Trading

The blockchain ecosystem, essential to the crypto industry, seems to have created a new opportunity. Just like NFTs, the process of turning AI Agents into a tradeable product has already started. However, the alignment of this idea with the fundamental logic and its correctness should be questioned.

Firstly, it is crucial to analyze the potential user base for AI Agents. As discussed earlier, AI Agents are essentially software that automates specific tasks and replaces human labor. Like any software, AI Agents may need to be sold, supported, or leased. However, the key point here is that AI Agents must serve a purpose and be genuinely useful.

The best way to maintain this "usefulness" is to understand the usage areas of AI Agents in the industries where they are developed and transfer this knowledge to the web3 space instead of treating them as a mere phenomenon in the web3 community. Transferring industrial know-how to the web3 ecosystem can ensure that AI Agents create value and gain broader acceptance. This approach can establish a more solid foundation for AI Agent trading, helping create a sustainable economic model.

At this point, ensuring the accountability of AI Agents moved to web3 and addressing ethical concerns with blockchain technology is not just essential but also a vital element in building trust and sustainability for users and investors alike.