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Challenges and Opportunities of Decentralized AI

Challenges and Opportunities of Decentralized AI 789 526 Comidor Low-code Automation Platform

Artificial Intelligence (AI) – especially Machine Learning and Deep Learning – has become popular among businesses and many organizations have begun adopting this technology in their operations.
Artificial Intelligence refers to the feature of having the ability to provide computational power for creating cognition in machines. This means that machines can perform human activities such as planning, reasoning, problem-solving, etc.
More and more enterprises have started to identify the need of implementing systems that are powered by AI in their procedures.

What are the Risks of Centralized AI Solutions?

The current AI solutions are completely centralized and involve a lot of risks for the companies that implement any of them. Some of those risks are:
The Rich Get Richer Problem: Many of the AI solutions in the market are provided by large companies that control huge datasets. These datasets keep increasing constantly as the more data they own, the more datasets they produce.
Dataset Owners Influence Populations: Centralized AI systems can influence populations and direct knowledge based on their willingness. For example, imagine the influence that political campaigns launched on Google, Facebook, Twitter, etc. have and how they can impact the outcome of an election.

What is Decentralized Artificial Intelligence (AI)?

Decentralized AI is one of the most promising trends in the AI space. These models provide the opportunity for large companies that control huge datasets to be independent.
Blockchain technology has contributed a lot to the development of this trend. It has paved the way for a decentralized ecosystem where data scientists, data providers, consumers, and all other involved parties collaborate in order to create AI architectures without the need for a centralized control authority.

How Decentralized AI can be Achieved?

Many subsets of cryptography have been already developed, which helps the empowerment of a Decentralized AI environment. These techniques offer ways of distributing datasets among many counterparts securely and ensuring the confidentiality of data.

Homomorphic Encryption

Homomorphism is one of the greatest technological advances in the cryptography space. This type of encryption allows the execution of specific types of computations to be done in the ciphertext and provides results that are also encrypted in the ciphertext. Having said that, this allows parties to execute computations in datasets without any need for decrypting them. The two types of homomorphic encryption algorithms that exist are Partial Homomorphic Encryption (PHE) and Fully Homomorphic Encryption (FHE).

Adversarial Neural Cryptography or GAN Cryptography

GAN cryptography is a model that was pioneered by Google and is explained thoroughly in the “Learning to Protect Communications with Adversarial Neural Cryptography” paper that was published at the end of 2016. With Adversarial Neural Cryptography, the confidentiality of datasets is ensured, and data are exchanged among different parties by maintaining high levels of privacy.
This technique gains great momentum through time and has the potential to be mainstream in decentralized AI applications.

Secured Multi-Party Computations (sMPC)

sMPC is the foundation of the development of new blockchain protocols. This security technique ensures the computation of a public function based on private data while keeping their inputs secret. Owing to that, sMPC architectures enable the creation of AI models without revealing the datasets to any third party.
Creating a decentralized AI environment needs an environment that is emphasized in high levels of security and data integrity protection. There are several techniques that can be implemented for the development of a decentralized model that allows parties to make computations of datasets without any central authorities.

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