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What is Decentralized AI?

What is Decentralized AI? The Relationship Between Blockchain and AI

Artificial intelligence (AI) is becoming increasingly important in business, but decentralization is critical for its success and ethical application. Blockchain and AI have a good relationship, but what is decentralized AI and why do AI and Web3 go together?

Artificial intelligence (AI) is becoming more popular in many industries, but as it becomes more expensive, only a small number of organizations can compete at the highest level. Decentralized artificial intelligence makes use of public blockchains for immutable data storage and economic incentives, promoting collaboration and innovation without relying on trust. This article investigates the various ways in which blockchain and AI interact, the benefits and drawbacks of AI, and how blockchains and cryptocurrencies can help to alleviate many of the concerns surrounding AI and its various subsets.


What is AI?

Artificial intelligence (AI) is an interdisciplinary field of computer science that allows humans to create machines that can complete tasks or learn to perform specific functions more efficiently than humans. AI practitioners frequently model the human brain in order to create novel mechanisms for resolving everyday problems. AI is becoming increasingly popular across industries, with applications including customer service chatbots, self-driving cars, and apps like Alexa and Siri. Because the term “artificial intelligence” means different things depending on the context, there is no single definition that satisfies the entire AI community.


A Brief History of AI


Alan Turing is widely regarded as an AI pioneer. His 1950 paper “Computing Machinery and Intelligence” established the field. The Turing test was developed to assess the “intelligence” of computers by comparing their responses to questions to those of humans.


How Does AI Work?

AI models are based on human reasoning and behavior, and they enable machines to quickly learn how to act in a specific way or achieve a specific outcome. Different types of AI aim to achieve different outcomes with varying levels of complexity, and most AI models fall into four major categories.

– Reactive machines: they respond to data inputs but cannot act on previously learned data.

– Limited memory: capable of analyzing data and forecasting future outcomes.

– Theory of mind: the ability to make decisions and adjust behaviors in response to human emotions.

– Self-awareness: the ability to demonstrate human-level intelligence. Despite the various iterations available, AI systems perform tasks that normally require human intelligence.


Classes of AI

AI is classified into three categories: narrow AI, AGI (artificial general intelligence), and superintelligence.

Narrow AI is the most basic type of AI and typically specializes in a single task. Smart assistants, spam filters, traffic reports, and chatbots are all examples of poor AI. Innovations in machine learning (ML) and deep learning are frequently used to facilitate weak AI research (DL).

While superintelligence systems are currently unavailable, AGI, or “strong AI,” can be applied to any complex issue or problem-solving scenario. AI systems are being developed to accurately depict human emotions and solve complex problems on their own.


Machine Learning and Deep Learning

Machine learning (ML) is a popular AI algorithm for mimicking human intelligence. It works by feeding data into an AI machine that learns how to perform specific operations using statistics. ML combines supervised and unsupervised learning elements, allowing users to generate predictable and unpredictable results.

Deep learning (DL) is a subset of ML that employs neural networks that use a deep, layered data structure to mimic those of the human brain. It is used in healthcare, disease prevention, and self-driving vehicle optimization.



AGI is the pinnacle of AI research, a universal algorithm that can learn how to perform any task with human-like cognitive functions. OpenAI’s GPT-3 language model and DeepMind’s MuZerio are bringing us closer to that goal. Superintelligence is a fictitious AGI model that can mimic human behavior and cognition in every possible way.


Blockchain and AI

Blockchains are immutable databases 
that use node-based distributed networksrather than centralized server farms. They are self-governed and have no central authority. Every node in the network must agree on the validity of the corresponding transaction. Blockchains employ a data structure that connects each transaction, necessitating significant computing power.Furthermore, anyone can use a block explorer to view the history of transactions on a blockchain, resulting in a transparent environment for data sharing.

Bad actors are discouraged from adjusting transactional records or validating fraudulent transactions due to financial incentives. The use of cryptocurrencies in decentralized AI systems promotes high-quality contributions in a sustainable, scalable manner.


What is Decentralized AI?

Decentralized AI is a new field of artificial intelligence that incorporates blockchain and other distributed ledger technologies (DLTs). It seeks to ensure equitable and safe AI models through the use of distributed networks of nodes, which prevent power concentration and provide more value to users and society than centralized AI models. It is based on the principles that federated knowledge provides a better learning environment than centralized intelligence and that collective learning offers more opportunities for human advancement than AI models that rely on centralized mechanisms. Blockchains enable AI teams to safely develop intelligence models with the potential to significantly impact society, and decentralized AI promotes transparency and collaboration.


What Problems Does Decentralized AI Address?

Centralization is not inherently bad, but it can lead to open-source standards that protect privacy. Decentralized AI models employ homomorphic encryption to enable trustless collaboration, smart contracts and decentralized applications (dapps) to automate various aspects of the AI modeling process, cryptocurrencies to reward collaborators and promote community governance, and data providers to vet data sets and raise the bar for emerging AI developments. As the use of AI grows in popularity, decentralization serves as a safeguard to ensure that superintelligence does not lead to a dystopian future in which humans are indistinguishable from robots.



The field of AI is rapidly expanding, but its expansion is hampered by the rising computation costs of large data sets. As the industry expands, the number of companies that can afford to compete shrinks, leaving only a few powerful companies to dominate the space. Decentralized AI mechanisms employ distributed ledgers such as blockchains to prevent data manipulation, increase transparency, and avoid the centralization of power that could prevent AI from reaching its full potential. Blockchain and AI collaborate to advance the AI space, accelerate innovation, and create incentives for developing self-driving, intelligent technologies.


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