What Does True Decentralisation Entail?

True decentralisation does not happen until the training and implementation of AI models are affordable and efficient for everyone.

The biggest struggle for developers and engineers in developing efficient automation of systems is the expense and time required to train deep learning ML models successfully. For instance, to train a model on ImageNet which has a dataset of 1 million images, each of size 256*256, it takes months to get completed when using just one GPU server. The time required would be reduced to 30–40 hours, when the dataset is split across mini-batches when instead of one, tens of several powerful GPUs are made available.

More than half of the organisations worldwide have confirmed use of or plan to use AI at the core of their strategies in the coming 5 years. These organisations want to make their user experience personalized or seeks to optimize their operations to accelerating their profits. In the effort to maximise the efficiency of the resulting models, more and more datasets are fed in, which incidentally has increased the compute power requirements around the world.

And that is exactly what Raven has found a solution for. Successful trials in using blockchain to facilitate sharing of resources has prompted the creation of a more down-to-earth platform where the compute power of even a normal laptop or smartphone device can be a contributor to training a model anywhere in the world.

Distribution of Deep Learning Training Models and Sharing of latent Compute Resources through the Blockchain

What was once thought to be an unchanging process of strenuous uploading and processing of algorithms that is dependent on considerable availability of resources, is now set to change with blockchain integrated platform at Raven Protocol. The protocol allows anyone, anywhere to participate in contributing the compute power as low as that on their smartphone devices. What makes the system sustainable is the distribution of monetary value getting generated in the process. This is not only for the companies, but also for their consumers.

Giving entrepreneurs, startups, and the companies struggling to compete in the current economy the ability to train their ML models inside the protocol by inputting/sharing the algorithms, datasets and compute power, is structurally what forms Raven’s platform. That people who are not implicitly part of a training programme can also share their resources inside the protocol, which catapults the availability of necessary compute power.

Taking A Nuanced Approach to Decentralisation

Inclusivity has been the target of all other institutions that have tried to bring about a decentralised approach to Machine Learning. The blockchain and AI are a perfect combination and multiple efforts have been made to enhance the experience of adapting AI into all spheres of practical implementation.

Some of their applications are still limited and thus, providing solutions to those limitations can create a more sustainable ecosystem. Raven is, therefore, expanding the scope of the ecosystem to better further the cause of decentralising AI.

Browser Based Application

It brings great convenience for anyone to be able to hop-on / hop-off from the network whenever they feel like contributing.

No Additional Dependency at the Contribution End

Additional dependencies on any set of softwares from the contributor’s can delay support to the community, which instigated the elimination of any such dependencies.

Speedy Training

The use of the above two features in addition to inclusion of increased amounts of nodes, together doles out fast training of deep neural networks.

Dynamic Computational Graph

Dynamic allocation of nodes facilitates ad-hoc hopping on and off of contributor nodes. In short, the network is robust enough to handle any related aberrations.

Javascript Based DL Framework

All of this made possible by a new deep learning framework built on Javascript.