The Neuromation Platform will use distributed computing along with blockchain proof of work tokens to revolutionize AI model development. The revolution is long overdue: deep learning employs artificial neural networks of extremely large capacitance and, therefore, requires highly accurate labeling. Collecting large datasets of images, text and sound is easy, but describing and annotating data to make it usable has traditionally been challenging and costly. Crowdsourcing was applied to the problem of dataset creation and labeling a few years ago, employing large numbers of humans to correct mistakes and improve accuracy. It proved slow, expensive and introduced human bias. Besides, there were tasks that humans simply could not do well, such as estimating distances between objects, quantifying lighting in a scene, accurately translating text, and so on.
The Neuromation Platform will use distributed computing along with blockchain proof of work tokens to revolutionize AI model development.
The revolution is long overdue: deep learning employs artificial neural networks of extremely large capacitance and, therefore, requires highly accurate labeling. Collecting large datasets of images, text and sound is easy, but describing and annotating data to make it usable has traditionally been challenging and costly. Crowdsourcing was applied to the problem of dataset creation and labeling a few years ago, employing large numbers of humans to correct mistakes and improve accuracy. It proved slow, expensive and introduced human bias. Besides, there were tasks that humans simply could not do well, such as estimating distances between objects, quantifying lighting in a scene, accurately translating text, and so on.
We propose a solution whose accuracy is guaranteed by construction: synthesizing large datasets along with perfectly accurate labels. The benefits of synthetic data are manifold. It is fast to synthesize and render, perfectly accurate, tailored for the task at hand, and can be modified to improve the model and training itself. It is important to note that real data with accurate labels is still required for evaluating models trained on synthetic data, in order to guarantee acceptable performance at inference time. However, the amount of validation data required is orders of magnitude smaller than training data!
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Launch of R&D in Retail Lab test synthetic data hypothesis
4
Q3 2017
Proof of hypothesis on synthetic data is finished. Retail synthetic data generator proven. Model development complete (95% of accuracy reached.). Marketing and first sales to retail clients. ICO preparation.
5
Q4 2017
Neuromation ICO. Neuromation platform v1. Launched with 1,000 GPU capacity deployed. Medical devices Lab started.
6
Q1 2018
Active sales and marketing of Neuromation platform. Neuro Tokens are tradable on exchanges. Industrial automation Lab is started.
7
Q2 2018
Neuromation platform v2. Launched with 10,000 GPU capacity deployed.
8
Q3 2018
Neuro Token will migrate to its own blockchain. Neuromation platform v3. Launched with 100,000 GPU capacity deployed.
