The Potential of Decentralized Artificial Intelligence in the Future

Decentralized artificial intelligence has a lot of potential in the tech industry in the future.

When a decentralized computing model, like blockchain, is combined with artificial intelligence, the best of both worlds can be leveraged for a scale of resources. Decentralized Artificial intelligence is a model that allows for the isolation of processing without the downside of aggregate knowledge sharing. By virtue, it enables the user to process information independently, among varying computing apparatuses or devices. In doing so, one can achieve different results and then analyze the knowledge, creating new solutions to a problem which a centralized AI system would not be able to.

Potential of Decentralized Artificial Intelligence

Decentralized AI has incredible potential across businesses, science, and collective people. Altogether, it will allow devices to overcome adversity through real-world challenges, by reasoning, and through trial and error, while having the results recorded. Rather than slow methods of testing that traditional science has brought, there will be a priority towards speed with exponential points of testing. Ideally, through several evolutions of life experiences through these challenges, the optimal results and total knowledge gained can be shared across devices. Over the next ten years, devices that are learning through a decentralized AI network would benefit from those that have come before them and all of the other devices currently existing in the network. They will be able to leverage, that domain knowledge gathered and convert that data into knowledge. Through decentralized AI, people will have a definitive and continual structure in place that explains how things work.

To clearly understand the potential of decentralized AI, there's a real need for platforms to contain the computing power required, with storage and (very) high-speed communications. After that, people need to ensure that the appropriate security framework is set up to protect these assets and the data surrounding them. Currently, there are vehicles that can hold or harness this power in development. In the near future, when it is available, those early adopters will have an extremely capable computing device that will not only serve practical needs but also allow them to participate in Decentralized AI Volunteer efforts and potentially even fund the purchase itself through cryptocurrency mining or through fractional computing time. Ultimately, decentralized AI will provide a hierarchy of achievement that is created through the advancement of knowledge. At the root of this achievement is individualized collaboration. When disparate parts come together to share their findings, new or consistent understandings can be presented to help overcome challenges and, thus, unlocks decentralized AI's true potential.

Composition of Decentralized AI

Many subsets of cryptography have been already developed, which helps the empowerment of a Decentralized AI environment. The following 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.
• 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.
• 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 architects enable the creation of AI models without revealing the datasets to any third party.