Why Synthetic Data?
The Neuromation Platform will use distributed computing along with blockchain proof of work tokens to revolutionize AI model development.
"This project is unique, revolutionary, and cheaper/faster than every competitor. The science they are accelerating with these large data mines could change the world in a lot of ways. I feel very comfortable recommending this ICO. I wouldn’t sell it anytime soon either. 7.8 out 10"
Joshua Larson, Hacked.com.
A very new Blockchain use case. That blockchain project can also play a major role in automation and I am open to new ideas.
Platform
Neuromation is creating a distributed platform to service all aspects of future synthetic data ecosystem. The platform will allow users to create dataset generators, generate massive datasets, train deep learning models. Users will also be able to trade datasets and models in the platform marketplace.
We plan to take advantage of the latest trends in blockchain cryptocurrency. All transactions in the Neuromation will be done using our Ethereum extended ECR-20 compliant token. We will engage crypto-currency miners in computationally intensive tasks of data generation and model training. They will be mining our Tokens by performing these tasks instead of mining cryptocurrency.
The enormous computing capacity that will become available on the platform will be game-changing for wide AI adoption by the Enterprise.
They have reached a very good pre sale result.
They have three good topics, to decentralize prozesses.
Retail Automatization
Neuromation has partnered with the industry’s leading brands to solve the problem of making store shelves mart. Using synthetic data we are able to create large well-labeled datasets for all the SKUs in the retail industry. Algorithms taught on these massive data sets are able to efficiently analyze and label shelf availability, percentage of the shelf, accuracy of ayout and other metrics. Furthermore with our proprietary dynamic modeling platform we are able to create the datasets that mimic human interaction with the shelf. We are able to track customer flow and intent!
Our Retail technology lab is first to go live in the field with select partners. Our vision is to completely change retail technology. Every shelf should should feed a constant stream of analytic data to our retail partners. The key aspect of synthetic data is its ability to model massive datasets that change constantly. Because of our synthetic data platform, we are able to retrain our models quickly and with greater accuracy, producing significant improvements in capital spending and ROI for our partner brands.
Industrial automation
Neuromation plans to open an industrial automation lab where synthetic data will be applied to various problems. Typical difficulty with industrial environment is highly dynamic nature of the performed tasks. The model will need to analyze real time data and produce highly sophisticated predictions and calculations on the fly. It is very difficult to properly teach models to operate with dynamic data. Sometimes it is impractical to construct even the smallest dataset from real data. Neuromation will be using our proprietary Sensor Emulation Sandbox to procedurally create virtual environments where the models would be trained. Areas we are planning to tackle are: drone autopilots, industrial process monitoring, object manipulation, and others.
Our operating procedure is to focus on finding an industry partner who would take advantage of the capabilities Neuromation will provide and spearhead adoption of our methods. The goal then is to open up our sandboxes to 3rd parties to allow them to buy our data and train their models. Operating procedure is to focus on finding an industry partner who would take advantage of the capabilities Neuromation will provide and spearhead adoption of our methods.
Biotech and Pharma
There are a lot of interesting targets for the Neuromation approach in Pharma / Biotech. In general any problem where construction is easier than recognition (output is easy to calculate from input, but the reverse is hard; like computer science hash function) will yield well to our synthetic data approach. We are currently looking for partners in diagnostics and drug discovery to start a pilot project and open up a lab. We estimate that a lot of hypothesis building in drug discovery and biological system dynamics can be handled with Deep Learning models. If you work in the field and have good ideas please write to us: partnerships@neuromation.io
Our ideal pilot partner will bring a concrete problem that can be readily deployed in the industry. The most interesting case being where Neuromation would need to model not only the output of the system for proper classification, but also some underlying processes to create a full scale simulation that would extend to other problems and domains. We look forward to your suggestions!
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