Synthetic Data- A Solution to the data moat Problem for Machine Learning

Bulk data is needed in training machine learning algorithms and synthetic data generation is a surrogate technique to tackle the problem of collecting comprehensive data in real time. In computer vision, researchers are using synthetic data to bridge the data gap for the field of deep learning. In a fully-supervised learning problem, lack of availability of training data is a tremendous problem, but the University of Barcelona talks about Synthetic Data Generation Model that could assist in tackling the problem by synthetic image generation algorithm. To mimic real-world data, anonymised data is used that is called synthetic data. To get perfectly labelled data for recognition in a more efficient way, synthetic data is immeasurable according to chief research officer at Neuromtion Sergey Nikolenko.

What is synthetic data?

Synthetic data may not be found in the original, real data and it is created on certain conditions to meet specific tasks. It is used as a theoretical value, simulation or situation when designing a system. This data is used to set a baseline and represent the authentic data. The most extensive use of synthetic data is to protect the confidentiality and privacy of the original data. By striping recognising aspects such as addresses, names, social security numbers, emails an organization can anonymize and use this original data to create synthetic data that closely resembles the properties of authentic data. The gap between synthetic and real data is diminishing with the advancements in technology.

A secret to Artificial Intelligence:

Every advancement comes up with advantages and disadvantages at the same pace. But many researchers and technology experts believe that the adoption of synthetic data in artificial intelligence (AI) for machine learning (ML) in our daily lives is key to success and synthetic data can accelerate testing in artificial intelligence (AI) by providing robust data for algorithms.

Importance of synthetic data for deep learning:

Machine learning is vital and deep learning has become the number one field of machine learning. A broad spectrum of disciplines has been covered by deep learning, that was considered impossible due to traditional approaches of collecting data and combining big data with supervised learning to perform artificial intelligence tasks. Algorithms of machine learning are calibrated or trained with the amounts of big data that was a gap in the implementation of machine learning algorithms, but synthetic data filled this gap.

Advantages of synthetic data:

Deep learning machines and artificial intelligence algorithms are solving challenging issues and reducing the workload but what powers them? Huge data sets. Biggies of techs Amazon, Facebook and Google had a competitive advantage for their business due to data they create daily. Synthetic data can ultimately democratize machine learning for organizations of every size. While creating and using synthetic data organizations should use the best KYC compliance solution that should be more efficient and cost-effective in many cases. On-demand based specifications, it can also be created rather than collecting data once it occurs in reality. Even if there isn’t a good real data set, testing can occur for every imaginable variable because synthetic data can complement real-world data. This approach can accelerate training of new systems and testing of system performance for organizations. Fabricated data sets are very useful as they reduced they cost of employees associated with the collection of data, creating models and it also reduced time by creating data synthetically instead of collecting it, it also reduced limitations of using real-world data for testing and learning. An organization can determine the value of synthetic data as recent researchers suggest that synthetic data can create the same results that an organization would generate by using authentic data sets.

Conclusion:

Due to the rising cost of data sets, it is difficult to deep dive into the inner working of statistical modelling, while the paucity of authentic data also limits one’s ability of machine learning and leaves the understanding superficial. This increased the immense need for synthetic data to advance machine learning in time-reducing and cost-effective approach. An organization can take services of companies and can provide them with specific requirements to generate data for machine learning.