The current artificial intelligence market is heavily controlled by global tech giants, such as Microsoft, Google, Tesla, and Meta.
If it stays this way, we can see a monopoly in the AI field in the long run. As a result, it would cause unfair pricing a lack of transparency, and we will probably have no say on how things work.
This is where decentralized artificial intelligence comes into the picture. Decentralized artificial intelligence refers to the model that enables the isolation of data processing without the disadvantage of aggregate knowledge sharing. In other words, it allows you to process the data independently. As a result, you can generate better, unique results and create solutions that were previously not possible with a centralized AI system.
If you’re not sure what a centralized AI system is or how it works, you can go through machine learning and artificial intelligence tutorials by Jennifer Kwentoh.
That said, let’s understand decentralized artificial intelligence in detail.
What is Decentralized Artificial Intelligence?
Decentralized artificial intelligence is all about separating learning and intelligence into two different devices and organizations. And then training machine learning models on locally available data.
This approach allows you to combine knowledge from numerous local datasets without compromising the actual raw data between devices, locations, and organizations.
Decentral AI is getting more popular because it offers better privacy and efficiency than centralized artificial intelligence.
If you’re not sure where it will be applied, it will play a critical role in hospitals and autonomous cars (allowing them to take advantage of knowledge centrally while keeping sensitive data at local servers).
Role of Blockchains in Decentralizing AI
Blockchain provides a much-needed foundation for decentralizing artificial intelligence. The first generation of decentralized artificial intelligence apps is taking advantage of smart contracts and DApps to define how the endpoints in an AI app will interact with each other.
Similarly, digital tokens are still relevant in the decentralized AI world as they offer the mechanism to compensate data scientists for their contributions. They also influence how the model will benefit all the parties engaged.
Potential of Decentralized AI In Real World
As we have iterated above, decentralized AI is very beneficial and has lots of applications in the real world. Industry experts predict that in the next ten years, devices and applications that use decentralized AI networks will most likely benefit from those that have come before them and all other devices currently connected to the network.
They will also be able to gather, convert, and take advantage of all the data in the existing system. Also, people will have an easy-to-understand, definitive structure that explains how things work.
Federated Learning
Federated learning refers to a learning architecture for artificial intelligence systems that run in distributed topologies, such as smartphones. Started by Google, federated learning is an alternative to centralized AI training, where different devices can contribute to training the model while keeping the data in the device secure.
This means that applications can use federated learning to independently train or optimize AI without the need for trusting a centralized authority.
Homomorphic Encryption
Homomorphism is the process of mapping a mathematical set onto another set or within itself to generate an outcome that ensures the elements of the first set are mapped onto the another.
Homomorphic encryption ensures these computations are done using ciphertext to generate an encrypted result. This is considered one of the biggest inventions in the cryptographic industry in the last decade.
Because of homomorphic encryption, users of decentralized AI can contribute to training a model in a way that remains encrypted to other parties, thereby ensuring privacy and secrecy.
Conclusion
The future of AI is decentralized AI. Although it is in early-stage, businesses leveraging decentralized AI will soon outperform those that don’t. If you think artificial intelligence can be beneficial for your business, it’s time to test decentralized AI.
Still have doubts? Shoot in the comments, and we will address all your concerns ASAP!
Read more:
How to decentralize Artificial Intelligence Education?
