Global adoption of artificial intelligence (AI) is rising dramatically. AI is present in finance, healthcare, and many of the technologies we use on a daily basis. In a world where automation is king, AI presents an abundance of opportunities for businesses to save time and resources and improve user experiences. However, as AI becomes more expensive, only a small group of organizations can afford to compete at the highest level, posing the risk of centralization. Decentralized AI uses public blockchains for immutable data storage and economic incentives. The coming together of blockchain and AI promotes collaboration and innovation without trust. But, what is decentralized AI, and why is it so important?

In this “What is decentralized AI?” article, we’ll explore the different ways blockchain and AI work together. We’ll discuss some of the benefits and drawbacks of AI and some of the common classifications. Plus, we’ll explore how blockchains and cryptocurrencies help to alleviate many of the concerns around AI and its various subsets.

What is AI?

Before we consider the question, “what is decentralized AI?” let’s take a look at the field of AI itself. Artificial intelligence (AI) is an interdisciplinary field within computer science that enables humans to build machines that can complete tasks or learn to perform specific functions more efficiently than a human can. AI practitioners often model the human brain to develop novel mechanisms for solving everyday problems. Furthermore, AI is becoming increasingly popular sector-wide and plays a prominent role in things like customer service chatbots, self-driving cars, and apps like Alexa and Siri.

Furthermore, various subsets of AI exist, including deep learning and machine learning. Accordingly, there is no single definition that satisfies the entire AI community, as the term “artificial intelligence” means different things depending on the context.

A Brief History of AI

Renowned cryptographer and mathematician Alan Turing is widely recognized as a pioneer of AI. In his seminal 1950 paper titled “Computing Machinery and Intelligence,” Turing seeks to establish whether computers are capable of thought. This paper laid the foundations for what would become the field of AI. Later, the Turing test was established to further understand how machines can think. The Turing test is a framework that measures the “intelligence” of computers by analyzing the likeness of their responses to questions compared to those of humans. 

How Does AI Work?

Typically, AI works by emulating human thought and behavior patterns. AI models are often based on human reason and behavior and allow machines to quickly learn how to act in a certain manner or achieve a particular outcome. A common thread among AI models is the ability for machines to be “trained” on data sets. However, different types of AI aim to achieve different outcomes with varying levels of complexity. While new subsets of AI emerge frequently, four prominent categories define most AI models.

Firstly, “reactive machines” is the simplest form of AI that reacts to data inputs but cannot act on learned data. Secondly, “limited memory” models can analyze data to forecast future outcomes. Subsets of AI that use limited memory models include machine learning (ML) and recurrent neural networks (RNN). Furthermore, AI models that fall under the “theory of mind” category can make decisions and adjust their behaviors according to human emotions. Finally, “self-awareness” AI models enable computers to display human-level “intelligence,” including emotions and consciousness. Moreover, AI systems perform tasks that typically require human intelligence, despite the various iterations available, 

Classes of AI

AI can be further classified into three distinct groups; narrow AI, artificial general intelligence (AGI), and superintelligence. Narrow AI incorporates the simplest forms of AI, also known as “weak AI.” Weak AI emulates basic facets of human intelligence and usually specializes in a specific task. Also, weak AI relies on human operation and cannot act autonomously. Examples of weak AI include smart assistants like Siri and Aleksa, spam filters, traffic reports, and chatbots. The research that facilitates weak AI often comes from innovations in machine learning (ML) and deep learning (DL).

On the other hand, AGI, or “strong AI,” can be applied to any complex issue or problem-solving scenario. Furthermore, AI systems that fall into the “superintelligence” classification are currently unavailable, but these systems are being developed to accurately portray complex human emotions and solve nuanced problems autonomously. 

Machine Learning and Deep Learning

Machine learning (ML) is one of the most popular AI algorithms for emulating human intelligence. It works by feeding data into an AI machine that uses statistics to learn how to perform specific operations. Over time, these machines become more efficient at completing the tasks set out by AI practitioners. 

Furthermore, ML incorporates elements of supervised and unsupervised learning using both labeled and unlabeled data sets. Machines are supervised by AI practitioners who feed and interpret data to and from machines. However, ML systems can learn to become better at tasks autonomously without programming. Moreover, ML allows users to generate predictable and unpredictable outputs, making it a versatile tool with many use cases.

Furthermore, deep learning (DL) is a subset of ML that uses neural networks that mimic those of the human brain using a deep, layered data structure. The use of DL is prominent in healthcare, disease prevention, and optimizing self-driving vehicles.


Artificial general intelligence (AGI) is what many consider to be the pinnacle of AI research; a universal algorithm that can undertake all aspects of AI, learning how to accomplish any task with the enhanced cognitive functions of a human. Although AGI is several years from becoming a reality, developments such as OpenAI’s GPT-3 language model and DeepMind’s MuZerio are edging us closer to it. Superintelligence is a term used to describe a hypothetical AGI model that can replicate human behavior and cognition in every area possible.

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Blockchain and AI

Blockchains are immutable databases that use distributed networks of nodes instead of centralized server farms. Public blockchains like Bitcoin and Ethereum are decentralized. They are community-governed and have no single authority. Whenever transactional data is broadcast onto a public blockchain, each node in the network must reach a consensus about the validity of the corresponding transaction.

Furthermore, blockchains use a data structure that interconnects each transaction. Accordingly, any attempt to manipulate a transaction on a public blockchain requires significant computing power for success. Also, anyone can view the history of transactions on a blockchain using a block explorer, so they create a transparent environment for data sharing.

Financial incentives prevent bad actors from adjusting transactional records or validating fraudulent transactions. Using cryptocurrencies in decentralized AI systems encourages high-quality contributions in a sustainable, scalable fashion.

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What is Decentralized AI?

So, what is decentralized aI? Decentralized AI is an emerging field of AI that incorporates blockchain and other distributed ledger technologies (DLTs). Many leading AI practitioners agree that decentralization is vital for ensuring AI is equitable and safe. Successful AI models rely on complex and increasingly-large data sets. While it’s possible to create AI models using modest data sets, advancements in the field rely on state-of-the-art computers and experts to interpret and analyze data. 

As the resource demands for competitive AI models increase, the number of competitors naturally decreases. The result is a small group of dominant players that have the ability to shape the future of the industry and train AI models that prioritize revenue over ethics and safety. Decentralized AI aims to solve these issues using distributed networks of nodes that prevent a concentration of power and provide more value for users and society than centralized AI models. It operates on the principles that federated knowledge provides a superior learning environment than centralized intelligence and that collective learning provides greater scope for human betterment than AI models that rely on centralized mechanisms.

Furthermore, decentralized AI models help practitioners to control some of the most powerful AI systems and prevent hyper-intelligent machines from becoming corrupted. If these models relied on siloed servers, they could easily be hacked or fall into the wrong hands. Blockchains allow AI teams to safely develop intelligence models with the ability to significantly impact society. Also, decentralized AI increases transparency and promotes collaboration.

What Problems Does Decentralized AI Address?

Centralization is not inherently bad. However, centralized AI systems pose several problems. For example, closed AI projects often fail to incentivize contributions that make AI models of high quality. Also, closed-source AI often serves corporate agendas and lacks the innovation achieved via collective learning models. Decentralized AI solves this by creating a robust framework for a new privacy-preserving, open-source standard.

Decentralized AI models often use homomorphic encryption to facilitate collaboration in a trustless environment. This allows multiple parties to collaborate on a data set without disclosing sensitive data. Also, smart contracts and decentralized applications (dapps) allow AI practitioners to automate various aspects of the AI modeling process in a transparent and trustless environment.

Furthermore, cryptocurrencies create financial incentives for data scientists and AI practitioners that reward collaborators and promote community governance. Moreover, decentralized AI facilitates collaboration without the need for trust. It encourages data providers to vet data sets and raises the bar for emerging AI developments, and increases the quality of the outputs AI models generate. As the use of AI becomes increasingly prominent, decentralization acts as a safeguard to ensure superintelligence does not lead to a dystopian future where humans are indecipherable from robots. 

What is Decentralized AI? The Relationship Between Blockchain and AI: Summary 

The field of AI is accelerating rapidly. However, its growth is limited by the ever-increasing computation costs of vast data sets. Teaching machines how to solve problems is an expensive endeavor. As the industry grows, the number of businesses that can afford to compete begins to decrease, leaving only a handful of powerful companies to dominate the space. 

As AI becomes increasingly intertwined with our everyday lives, development teams are keen to ensure that this powerful technology serves to benefit society at large rather than corporate interests. Decentralized AI mechanisms use distributed ledgers like blockchains to prevent data manipulation, increase transparency, and prevent the centralization of power that could prevent AI from realizing its potential. Blockchain and AI work together to advance the AI space, expedite innovation, and create incentives for building autonomous, intelligent technologies.

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