How can Web3 and AI help humans solve complex problems?
Web3 and AI aiding human problem-solving
Article Author: Illia Polosukhin
Article Translation: Block unicorn
People have concerns about artificial intelligence (AI), but combining it with blockchain technology can be an excellent complement to human intelligence.
Cultural and regulatory dialogues worldwide indicate that people have many concerns about artificial intelligence. AI will take over the world, take away everyone’s jobs, and destroy Hollywood: you can choose your own dystopian adventure. I don’t want these things to happen either, but deep down, I am an optimist about artificial intelligence. Humans have done quite poorly in many ways, and we should hope that AI can help us address these problems and work with us to solve them, especially when dealing with complex problems.
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This is exactly what the Web3 space has been exploring since the birth of Bitcoin: how to coordinate large-scale, decentralized, and complex populations under a common goal. The point I want to make is that in some areas, the combination of artificial intelligence and Web3 can truly help society solve its most complex problems. The following is a perspective on how this combination could manifest, based on the current state of technology, and it is entirely feasible.
Some things that AI is good at: collecting and integrating large amounts of information, even globally. Assessing results based on a set of parameters and conducting research and tasks based on explicit prompts from experts.
Some things that blockchain is good at: deploying governance frameworks for global pools of funds across multiple jurisdictions to help expand networks of participants globally around shared protocols.
Some things that humans are good at: making wise and nuanced decisions based on deep holistic expertise built on organic experience, and sharing knowledge and passion on topics they care about with communities.
What happens when we mix all these elements together to address a complex problem? I’m talking about a truly complex, global problem that has plagued generations or a “wicked problem” like curing cancer. This requires coordinating the actions and desires of thousands of people, making high-risk decisions on strategy and resource allocation, and managing across many industries and jurisdictions.
How it can work
So let’s imagine a “Cure Cancer” DAO (decentralized autonomous organization). It is founded by a group of scientific research labs, academic departments, and disease communities, with an initial total of 1,000 people. They identify a common mission and appoint a small group of experts as the governing team responsible for strategic decision-making. They launch a DAO, issuing member NFTs with different governance responsibilities to each participant and allocating a fund pool as startup capital, and then they establish an AI agent to manage the project’s scaling process.
The governance committee has designated a series of key performance indicators (KPIs) for the artificial intelligence agent, requiring it to complete a series of community management tasks. Assuming these tasks are initially: managing donations to the funding pool, tracking new members, and paying for work completed by representatives of the DAO based on explicit delivery criteria. This will save a significant amount of time and expenses, which would typically consume a large amount of resources for non-profit organizations or DAOs.
More importantly, the committee also requires the AI agent to assess the needs for advancing cancer treatment and develop a proposal, including a work roadmap, sub-projects, potential participants, and institutions suitable for global participation, as well as specific tasks to be completed. The agent formulates a long-term plan and proposes a series of execution steps, which are then submitted to the expert committee for review.
The committee adjusts and prioritizes the roadmap proposed by the AI for the first six months of work to cover the cure cancer DAO. They authorize the AI agent to recruit personnel to complete these tasks, assign work (regardless of size), and evaluate the completion of work, with compensation paid from the funding pool.
The AI agent regularly updates the roadmap and reports progress to all stakeholders in the DAO, in a way that manages the global view, allowing local contributions to be more effectively conducted in real-time. Over time, it can expand the scope of the project by proposing sub-communities, managing experiments, and helping coordinate collaboration among growing members, even interacting with multiple committees in different professional fields of management.
The DAO committee can veto any proposals put forward by the agent or suggest improvements to make things more efficient as progress is made. Over time, if the governance committee observes that the AI agent is performing poorly in a certain aspect, they can commission the collection of new training data in that specific area to improve the model and fine-tune it to fit their needs. This can even be crowdsourced from the expert community on the blockchain, with these experts reviewing the work and evaluating the improvements made by the AI agent.
The Role of AI and Web3
In fact, this vision requires the dual participation of AI and Web3 to be realized. We need Web3 for governance, financial support, and coordination tools. All AI actions take place on the blockchain, with team members and donations managed through blockchain tools, allowing for direct interaction with the DAO’s funding pool and enabling fully transparent transactions. With collaboration from professionals and assistance under the supervision and transparency of the blockchain, AI can optimize every aspect of operating the “cure cancer” DAO. If all processes are completed on the blockchain, we can even better monitor risks and manage trust than current major social systems.
This is just a very high-level example, but I hope it can guide us to think in an optimistic direction, that is, how we can more effectively solve problems through creative application of AI and Web3. We will be able to expand many things that were previously too complex to be managed solely through social means, or solely through top-down commands and controls, or solely through blockchain. This decentralized scientific community building example is also applicable to any globally coordinated issue or research effort.
These technologies are far less interesting in isolation than when they are combined: it’s not about artificial intelligence alone completing the entire work, but about its role in areas where we are not good at, helping us coordinate better and complete work faster. If we focus on efficient construction and proactive risk management, establish a balancing mechanism that maximizes the advantages of human and technological participants, and work together towards a shared mission, then we will see some powerful experiments gradually emerge in the coming years.