What is decentralised AI?

What is the problem with traditional centralised AI systems?

From its inception, artificial intelligence (AI) has been centralised. While it was obvious that there are some perks associated with traditional AI models, the strict centralisation of these systems raised several concerns regarding data privacy, accessibility, ethics and their entire decision-making process. 

Centralised AI is plagued by certain challenges. For example, big tech companies and their broad amount of available data, set out a cycle where AI models generate even more data. However, contemporary centralised AI systems that rely on a single source often result in biased information. 

That brings us right to data usage- centralised AI doesn’t provide any transparency. Big tech uses users’ data and doesn’t provide transparency about its usage in return. In a world where data protection is linked to many regulatory moves, centralised AI systems seem to be pushing in another direction. 

As a result, we got new models, such as decentralised AI (DAI), to address the shortcomings of traditional AI systems. 

Are artificial intelligence and crypto the new power couple? Find out by reading this article: 'AI Meets Crypto: Can ChatGPT help crypto?'.

Can decentralised AI be more ethical than centralised AI models?

It is important to understand that AI is not only related to intelligence, but to data as well. For example, extensive data sets utilised by AI are controlled by a small number of big organisations. 

This implies that centralised AI is associated with a concentration of power in the hands of several big players. So, the concern that these players have a big influence on the development and future application of AI technologies is real. As a result, the AI landscape could be shaped according to business interests instead of the public good. 

This brings up several ethical concerns, along with the problem of bias in AI algorithms and data protection infringements. 

These ethical minefields in the development of AI technologies could have an impact on society and economy as well as highlight economic inequalities throughout the years. Putting a lot of control in the hands of a few organisations could also stifle innovation and present a true obstacle to enter the relevant market. 

Taking all of these considerations into account, it seems that a more decentralised AI system could be the answer- a diversified landscape of AI innovation can amount to more balanced development of the technology as well as minimise bias and data privacy risks. 

Decentralised technologies are providing a more transparent and accessible system. Check out how decentralised science (DeSci) helped scientists and improved research by reading this article: 'What is DeSci? Web3 on the path to improve science'.

What is decentralised AI (DAI)?

Decentralised artificial intelligence (AI), the merger of artificial intelligence and blockchain technology, combines two recent technological innovations. In simple terms, DAI refers to an AI system that utilises blockchains to store, process and distribute data, operating in numerous nodes instead of one central authority.

Decentralisation means that there is no main decider, no central authority or big organisation pulling the strings. The main element of decentralisation refers to achieving consensus among the smallest units, and that’s how distributed systems can be built and managed. 

Decentralised AI systems could provide users with pre-trained AI models on their devices that process data locally to maintain privacy. Training AI models with public, verifiable data sources amounts to the respect of data privacy protection and avoids several legal pitfalls typically linked to unauthorised data usage.

DAI contributes to the accessibility of AI systems, making them more adaptable and inclusive. For instance, centralised AI models are being created in isolation, but their decentralised counterparts would enable developers from all around the world to contribute to the project as the result is to create a robust and less biased AI model. 

AI models can be trained collaboratively more efficiently, utilising a variety of data sets to create more applicable models due to its open source type of development linked with a collaborative approach and secure data sharing.

What are the core components of decentralised AI models?

At the moment, DAI is one of the most recent trends in the AI development space. The blockchain technology and artificial intelligence combo seems to be paving the way for a decentralised ecosystem based on collective intelligence. 

If you are new to the crypto space and don't understand these technologies well, think of enrolling in our Learn Crypto Academy.

A few technological innovations can make AI models decentralised. For example, blockchain technology and smart contracts can combine traditional AI features with sophisticated cryptography to optimise the automation process of AI applications.  

Let’s take a look at some techniques that can make decentralised AI possible.

Homomorphic encryption

Homomorphic encryption refers to a type of encryption that enables the execution of specific computations to be done in the ciphertext, along with results also encrypted in ciphertext. Homomorphic encryption can be either partial or full. 

In other words, computations can be executed without the need to decrypt them. It is considered one of the biggest technological innovations in cryptography. 

Adversarial neural cryptography

Adversarial neural cryptography, also known as GAN cryptography, was created by Google and explained in a 2016 research paper. This type of cryptography ensures the confidentiality of data sets and the exchange of data with the purpose of achieving higher degrees of privacy.

Secure multi-party computations

Secured multi-party computations (sMPC) is linked to the development of blockchain protocol. It refers to a security technique that ensures the public function’s computation on private data while keeping all inputs confidential.  

The architecture of secure multi-party computations enables the development of an AI model without the need to reveal the data to third parties.

Benefits of decentralised AI systems

There are several important ways how decentralised AI models could become a tool in benefiting society. Let's check them out.

Transparency and accountability

Transparency is not an upper-hand of centralised AI models; instead, think of them like black boxes, making it hard or even impossible to understand how their inner workings are done or how their outputs are processed and created. 

Because of this black box feature, it is not an easy task to oversee centralised AI models and conclude whether they should be used by the general public.  

On the other hand, DAI relies on blockchains and open-source protocols which adds up to artificial intelligence’s transparency and accountability. Users can test and audit AI processes.

Ethical data usage and enhanced privacy protection

We have already discussed how centralised AI models are less ethical than their decentralised counterparts, especially when it comes to data usage. DAI utilises public on-chain data sets, making sure to avoid pitfalls associated with unauthorised data usage and privacy breaches.  

Since data is processed locally and dispersed throughout decentralised networks, the risks of unauthorised access and single point of failure are decreased. The underlying blockchain technology ensures the accuracy of data.

Reducing bias and false outputs

Due to decentralised decision-making processes and a variety of inputs, decentralised AI decreases the possibility of bias and creates more balanced results. Because of cryptographic verification processes, the artificial intelligence’s outputs are considered more stable. 

The occurrence of false outputs and bias in centralised AI models typically happens because of incomplete or inaccurate data used for training. On the other hand, DAI is built on verifiable data sources lying on the blockchain which reduces the possibility of these issues.  

Putting AI training in a decentralised environment based on verifiable on-chain data, validation and cryptographic processes, along with the use of crypto wallets and smart contracts, can provide transparency and improved learning of AI models.

Decentralised decision making processes

In the blockchain ecosystem, governance is tokenized, enabling token holders to vote and implement changes to the protocol. In simple terms, the entire decision-making process is dispersed among all holders which provides a higher level of collaboration.

Are there any challenges?

The decentralised model also brings to the table several drawbacks. Coordinating decentralised networks can be complex, specifically when it comes to ensuring security across the systems and compatibility with existing platforms. 

At the moment, most DAI models are experimental and still have to prove their efficiency in practice. In comparison to centralised AI models, DAI is still in its infancy. 

Implementing DAI requires the comprehension of new blockchain protocols, technologies and processes which can be challenging for users. The process involves synchronising data across network nodes and maintaining blockchain networks. 

For example, managing trust and achieving consensus could be complex. Making sure that all nodes act honestly and prevent malicious activities requires a strict consensus mechanism which could need a lot of resources. 

Since data needs to be distributed across multiple nodes, scalability issues can emerge. As the network grows, this task can add up to performance matters and latency. 

Artificial intelligence is a new landscape that is just being regulated, but many issues still haven’t been addressed. The entire field could be influenced by regulatory changes. 

Practical implications of decentralised AI

Although the technology still needs to mature and prove its efficiency, a decentralised AI system is forecasted to have several interesting implications. Let's check out AI solutions for particular problems.

Cybersecurity and enhanced network resilience

Since centralised models are black boxes and single points of failure, they are susceptible to cyber criminals and more attractive targets with more and more users making use of AI technology.

Blockchain technology that supports decentralised AI model development can help in monitoring on-chain activities and anomalies which adds up to advanced cybersecurity measures and makes AI systems less vulnerable.

Additionally, the presence of multiple nodes that run on the blockchain network removes the single point of failure feature and provides improved security and resiliency of AI platforms.

Enhancing financial systems

Traditional financial institutions, such as banks, have been getting into decentralised technologies and integrating blockchain networks into their system. Since blockchains record transactions through an immutable ledger, a broad amount of data can be processed by AI agents.

Multiple banks decided to launch crypto trading services in 2024. If you're interested in learning more, why not read this article: 'Banks launching crypto trading services in 2024'.

Additionally, decentralised artificial intelligence can make investment strategies more effective by providing transparent data analysis and market sentiment analytics. The transparency of decentralised AI models creates trust among investors and regulators which could amount to a more secure financial ecosystem and the subsequent creation of other auditable machine learning models.

Healthcare applications

When it comes to healthcare, decentralised AI can enhance diagnostics by providing health data security and sharing among multiple medical institutions while preserving the privacy of patients.

For example, decentralised AI algorithms could analyse broad amounts of anonymised data to spot patterns, predict outbreaks of diseases and enhance treatment plans of patients. The data security and collaborative approach fostered by decentralised AI could lead to more accurate results and reduced healthcare costs.

Efficient supply chain management

Within supply chain management, a decentralised AI system can enhance operations and provide real-time insights. DAI contributes to the efficiency of supply chains by being able to analyse various data, predict fluctuations and recommend optimal solutions.

The transparency of these AI systems can also increase customer satisfaction as well as improve the accountability and resilience of supply chain systems.

Why decentralised AI matters for the future of technology?

DAI is set to change the tech landscape by providing a mix of privacy, security and machine learning developments. Due to its ability to deliver verifiable and real-time data could be an essential driver of future innovations and an ethical use of technologies. 

By providing more secure and real-time processing power and data exchange, DAI could overcome the shortcomings of centralised models such as privacy struggles, transparency and resource allotment by utilising blockchain technology and distributing duties throughout a network of nodes.