Drinking list
The Neuromation Platform

The Neuromation Platform will use distributed computing along with blockchain proof of work tokens to revolutionize AI model development. It will combine all the components necessary to build deep learning solutions with synthetic data in one place. Platform service providers, commercial or private, will provide specific
resources for the execution and development of synthetic data sets, distributed computing services, and machine learning models, addressing the “three pillars” of AI in the previous slide. Each executed service (data set generation, model computation) or sold data piece (dataset, data generator) is accommodated by a reward, numerated in our currency, Neurotoken (NTK). The Neuromation Platform will be running an auction model that allows customers to negotiate prices directly with service providers.

Platform Price Setting

Platform Price Setting: The price for each service will be determined by the aggregate setting of the Neuromation nodes (price per unit of computation). Each node will have a minimum token price-floor setting. The minimal price floor can also be adjusted dynamically via an algorithm that will maximize the total tokens earned for the node. The
Neuromation platform will determine the resources required for each requested task and select the most efficient node pool (minimizing price for the customer). Between nodes “sniffing” out the market and customers hunting for the most efficient price, the Platform will find equilibrium in supply and demand.

In order to transact on the Neuromation platform, a client will need to buy Neurotokens (NTK). To simplify the purchase mechanics, Neuromation will provide a client portal that will make Neurotoken (NTK) purchase a one-click process.

Neural Networks and Deep Learning

The compositional structure of neural networks allows for parallelization in both the training and inference of such models. Parallel processing of the networks’ layers advanced GPU-based computational paradigms, leading to breakthroughs in high-performance computing. Currently virtually all industrial applications of neural
networks are trained and run on GPUs, and the next jump in efficiency will involve specialized hardware such as Google’s TPU and other embedded hardware platforms.

Synthetic Data

They propose a solution where perfect label quality is guaranteed by construction synthesizing large datasets along with perfectly accurate labels artificially. The benefits of synthetic data are manifold:

  • once the environment is ready, it is fast and cheap to create as much data as needed;
  • the data is perfectly accurate and tailor-made for the task at hand, with labeling that might be impossible to obtain by hand (for example, illumination map of the environment or depth values);
  • it can be modified to improve the model and training, in a constant feedback loop between the model and the synthetic environment.

In solution, synthetic data is used for training, and a small validation set is comprised of real, manually labeled data. In essence, this makes using synthetic data for a transfer learning problem: we need to reuse models trained on one kind of dataset (synthetic) and apply them on another kind of dataset (actual images). Their approach, however, has several important advantages that greatly simplify transfer learning in this case. First, the synthetic training dataset is not given as part of the problem but rather generated by they – can and do try to make it match real data.

Retail Automation Labs

Neuromation has partnered with some of the industry’s leading brands to solve the problem of finding consumer goods on store shelves. Using synthetic data of every product, they are able to create large perfectly labeled datasets for hundreds of thousands of SKUs in the retail industry. Deep learning vision algorithms trained on these massive data sets are able to efficiently analyze and label shelf availability, percentage of the shelf, layout accuracy, and other metrics.


If you want more information, visit the WEBSITE and read the WHITEPAPER.

No comments so far!
Leave a Comment