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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.
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.
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: email@example.com
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!
Neuromation sells synthetic datasets for AI training. We create a sandbox environment that will generate virtually unlimited set of well-labeled examples for you.
We charge per image / data point.The generation tool can be deployed into a web service API.In general you only pay after some agreed degree of validation has been achieved. The model will train and be cross-validated on real-world data. Partnership opportunities are also available and are evaluated on a case by case basis.
Neuromation can build your back-end AI engine. We will develop a model from scratch taking care of data generation, model training and tuning for you. Neuromation can also do retraining and maintenance as required. Neuromation can also help if you need to improve the existing model with additional data points (synthetically generated).
The result can be deployed as an API on the cloud or embedded into a device. Our preferred model is to charge per processed request. However, we can also hand the model off to you and deploy on your infrastructure with full release of the IP.
Neuromation is engaged in basic research on the role of synthetic data in Deep Learning. We would love to share our findings and also collaborate with other teams.
If you think synthetic data can benefit your research effort Neuromation can engage our staff scientists to help you realize your goals. We can organize research teams for both short and long term projects. There is no specific business model to follow for this type of service. If the research subject is interesting enough Neuromation can sponsor the effort.
NEUROMATION IS PROUD TO ANNOUNCE
AI STARTUP COMPETITION
We’ve got great news: the very first paper with official Neuromation affiliation has appeared!
In the fourth installment of the NeuroNuggets series, we continue our study of basic problems in computer vision. We remind that in the NeuroNuggets series, we discuss the demos available on the recently released NeuroPlatform, concentrating not so much on the demos themselves but rather on the ideas behind each deep learning model.
As China’s fast-Moving AI Industry Prepares to Kick into Overdrive, Neuromation Hopes to Pave the Road with a Blockchain platform and Synthetic Data
With venture capital pouring into AI startups throughout China and a new poll revealing that 36 percent of Chinese entrepreneurs surveyed believe AI is the most promising industry, tech companies from other countries have flocked to Shanghai and other cities, hoping to be part of the AI boom with complementary technologies, including robotics, cloud computing, and Blockchain.