Decentralized AI Is A Model That Allows For The Isolation Of Processing Without The Downside Of Aggregate Knowledge Sharing
AI today is fairly centralized and is limited to the ownership of a single entity, such as Facebook or Google. This presents a unique set of challenges that don’t actually further additional advancements for the betterment of society. More importantly, there’s no collaboration when things are centralized. The future of artificial intelligence (AI) will be determined by how much weight we put into collaboration. Fundamentally, collaboration relies on a group of people (or machines) who share their individual knowledge to solve a problem. Decentralized AI is the path forward. When you talk about decentralized AI, what you’re really talking about is a way to bring different ideas and data sets based on intelligence, or pseudo intelligence, together to solve a set of complex challenges.
When you combine a decentralized computing model, such as blockchain with an intelligence system (AI), you can leverage the best of both worlds for a scale of resources. Decentralized AI is a model that allows for the isolation of processing without the downside of aggregate knowledge sharing. By virtue, it enables you to process information independently, among varying computing apparatus or devices. In doing so, you 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. From the beginning, the role of science was used to take the discoveries and lessons of the past and find out how they can be used to accurately explain the world around us. Decentralized AI takes a similar path as it interacts and changes the environments around it, starting with our devices. This type of AI will explain how our devices can operate in a more efficient way and how we can better interact with our world.
Decentralized AI has incredible potential across businesses, science, and us, as a 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, we’ll have a definitive and continual structure in place that explains how things work. But knowing just how important decentralized AI is to our society isn’t enough. We need to understand the steps that are needed to realize this potential. There’s a real need for platforms to contain the computing power required, with storage and (very) high-speed communications. After that, you need to ensure that the appropriate security framework is set up to protect these assets and the data surrounding it.
Right now, 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 though 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.
This news was originally published at Inside Big Data