Deep Learning Networks (DNNs) are some of the most powerful deep learning algorithms constructed from multiple layers of linear and non-linear processing units. Neural Networks interpret sensory data through machine perception, labeling and clustering raw input.

The advent of synthetic data will overturn the competitive advantages of machine learning that powerful tech companies get by amassing visual data sets of images and videos. Synthetic data is computer generated data that burlesque real data; in other words data that is created by a computer. Many initial startups face the “cold start” problem where lack of quality labels make it difficult to train computer algorithms. To resolve this problem, data simulators, which are highly flexible and versatile, are being used to generate contextually relevant data in order to train algorithms. However since, big companies exponentially expand their initiatives to gather relevant data, they do not face the same challenge.

Data simulation will bring parity between big technology companies and startups. The major challenge facing the startups is to leverage the best visual data with correct labels to train computers accurately so that they can compete against functionaries with inherent data advantage.

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