AI will change everything. This is the great world event not just of our generation, but our lifespans. We must not build AIs solely through cloud-based environments, but instead unlock Edge AI through local computing. We must empower developers to break their dependency on the cloud and create sovereign AI systems that are better, faster and stronger than their cloud-based equivalents. AIs are built on data. If we can distribute data environments, we can distribute AI - and truly deliver on their promise.  

Concerns Over Centralized AI

Right now, trillions of dollars of investment in compute clusters, data centers, and bespoke energy grids are being poured in by the world’s biggest tech companies to create the models that will take over many of the systemic architectures of our digital society.

If it’s not built in the right way, it’ll change everything for the worse. Creeping at first, and then so fast social structure will change before you can say the word ‘Singularity’. We must begin the process of distributing AI training data and model storage to avoid the greatest inequality of power seen in human history since the Age of Kings. 

Currently, AI models must use the cloud for training, but local deployment is already within reach. Next gen AI models will be able to use distributed training data and be trained in a continuous manner rather than being snapshots like current LLMs are - and distributed data management like that offered by Source Network’s stack will be key for their functionality.

In the Information Age, AI models will be the new means of production, and if they’re controlled by centralized powers, the rest of us will be at their mercy. We must avoid the most powerful AI models being solely at the discretion of modern day corporate zaibatsus.

Pragmatism Over Ideology

That’s the ideological pitch out of the way - but it’s the pragmatic pitch that is far more compelling. Distributed and collective creation and management of AI models through Local Edge is important not only because it countermands power accumulation, but because it quite simply makes them work better. If AI is not deployed through Local Edge, it will always be lacking in the capabilities we require - and may rapidly onset newly destructive social paradigms. We need distributed data management to effectively build these models in the first place and help them reach their true potential. 

Let’s be clear, though - despite ideological concerns - large companies are not against Edge AI. Quite the opposite, they are actively developing them. Apple’s “Apple Intelligence” in its latest OS utilizes on-device processing. Gemini Nano by Google is built specifically for edge device functionality. Samsung integrates on-device processing with its cloud models. These large companies understand the restrictions of pure cloud-based environments for their AI models. They are just as keen as anyone to use Local Edge to create scalable AIs through their device fleets to deliver better performance and utility to their end users and better flexibility to developers working in their environments - even if they do have a tendency to silo their users and data while restricting collaboration between devices. Still, Source Network’s tools help any Local Edge software development startup deploy their software to all environments, be it iOS or Android, be it cars, smart watches, planes, trains or drones. On anything with a computer chip, developers can manage their software, user and AI data as effectively as Apple or Google does.

Why Cloud-Based AI Has Limitations

This is because cloud-based AIs that are operated through centralized providers are ultimately and innately unscalable. They are not suitable for variegated Edge Device networks trying to harness user-generated and user-owned data for their use cases. There are latency issues, bandwidth constraints, scalability bottlenecks that all limit performance. Crucially, as more enterprise and individual users demand their AI always works, online or offline, it's critical that they deploy through Local Edge. The operational costs in processing large datasets in real time through the cloud are prohibitively expensive and energy demanding. There is reliance on cloud providers to provide the uptime required, and limited customization in development environments that are needed as AI advances continue at pace. And, of course, there are the classic data sovereignty problems and the vulnerability of data, its need for regulatory compliance, and the trust issue for end users or end point services on how their application data is being managed by providers. 

The distributed data management and decentralized architectures that Source Network provides let us build Edge AIs that overcome these limitations and can scale better, maintain sovereignty, and create collaborative data conditions that developers can use to boost AI functionalities that can make use of the pertinent data stored on end user devices (with the user’s permission of course), opening up the possibility of AIs that can train, function and operate solely within Local Edge device networks. 

The Power (and Problems) of Edge AI

Personal AIs on personal computers that don’t send every single query directly back to their corporate handlers. AIs that have the low-latency to deliver real time operational capabilities to the frontline of where they need to be. AIs that fight to preserve privacy, not seek to invade it. Cheap, functional, flexible AIs that are a product of their computing environments, not foisted upon them. AIs that don’t instigate the next Ice Age through their outrageous power demands. AIs for smart cars, industrial manufacturing, smart energy grids, satellites, smart city utilities, intelligent automated farming - the list goes on. Distribute data in every sector - distribute AI in every sector.

Edge AI, of course, has its own set of practical problems that need to be solved to be able to assume the functions we need it to. Edge devices have naturally constrained computational resources in and of themselves, with limited storage capacity that can reduce the complexity of the applications that run on them. Data on edge devices can be, if not properly protected, more vulnerable to data breaches than cloud environments. If data sharing and collaboration between edge devices is both hyper-efficient and secure it enables AI models to unleash their endgame potential. It lets AI models running on devices get access to the local data they need (and nothing else) to operate effectively. Picture a smart car entering a new location and gathering information from other cars familiar with the local area and then using that data to update its model and deliver the best information on the ground to the user, without sacrificing privacy and faster than it could ever get that information from a permissioned server. Developers have to date made a faustian bargain with cloud-based development to power their models in this manner, and inherit all the drawbacks of those environments as a result.

How Source Network Enables Developers Build AIs on the Edge

Source Network’s distributed data management stack is built to solve those issues and make Edge AI a reality. Our tools - DefraDB, SourceHub, LensVM and Orbis - combine to create sovereign data management, trust-based collaboration, verifiable data integrity, flexible access control, self-verifying data models and data transformation that developers need to build Edge AI models through local compute. The goal is to invert several key relationships in current computing architectures that then make this possible.

Ultimately, it’s about putting data at the center of the computing universe. Services come to data, instead of data going to services. Data residing on edge devices can be processed locally by models that can act upon it without having its sovereignty surrendered. All types of data can have its own set of permissions intrinsically attached to it by default, rather than externally managed and services that access it individually. And the trust - the important part - that underpins this data originating on the Edge device or with the end user, not a centralized service dictating who or what can access it through a hierarchical system. The trust is established directly between participants (data owners, users and AI agents) who can collaborate to power applications - including Edge AI. 

Instead of using cloud federated learning architecture like that provided by AWS, developers can use Source Network instead to create private, secure and resilient AI through Local Edge devices. Instead of a centralized workflow, developers can use dynamic asynchronous workflow with a seed node providing initial weight values and configurations and to act as a vehicle for developers to share metadata like policies. Rather than receiving global model updates from a central server delivering operational data to perform functions, peers instead share updates directly with one another through DefraDB peer-to-peer capabilities. This fuels local and appropriate data sharing and syncing and doesn’t require cloud servers. 

With data and model weights all stored in DefraDB, local model training can be triggered either through timers or by thresholds such as large changes to the input dataset - perhaps originating from multiple sensor and/or edge devices channeling data into it. The outputs can be stored as versioned data on DefraDB with complete weight traceability. The local model updates can then be published, again all handled transparently through DefraDB. Finally, these published model updates will be aggregated to dynamically refine the Edge AI model being trained. Thanks to LensVM, required data transformation and variegated data types that are feeding the model can be unified holistically whatever their origin edge device and the type of data it's producing.

This means that rather than giant unwieldy datasets being harvested, indexed, then fed into models and discrete model versions happening slowly over time (and flagrantly betraying all privacy and data laws while they’re at it), models can continuously train and develop through decentralized architectures and Local Edge device networks that are always adjusting based on the data they are receiving, emitting, or observing. Dynamic Edge AIs that can work with low-latency real time improvements and adaptations to the environment they are operating in, and which can be trained in a pluralistic, collaborative environment.

We’ve gone over the many potential industries Source Network can affect in previous blogs, and next time, we will establish in detail how these Edge AIs can radically transform those industries due to their powerful real time capabilities and distributed model weight training our decentralized architecture provides - as well as go over some of our long term goals in helping the AI revolution reach its true apotheosis. Distributed, local, user-centric, secure, private - and for everyone.