Without abundant data captured from diverse data streams by means of products/services, it can be difficult for companies to create a conducive environment for data scientists to train their machine learning algorithms. While giants like Google and Amazon don’t face issues when it comes to gathering data, other companies often have limited access to the datasets they need.
Acquiring data is often an expensive endeavor which most companies cannot afford. High costs of acquiring third-party data cripples companies from taking AI initiatives. This is why companies and researchers are now relying on synthetic data to train their algorithms.
Synthetic data is lab-generated, artificially manufactured information which is neither obtained by direct measurement nor captured through any means. According to research conducted by Massachusetts Institute of Technology, artificial data gives the same results as real data — without compromising privacy. This article talks about the importance of synthetic data for AI initiatives.
Machine learning (ML) algorithms do not make a distinction between real data and synthesized data. ML algorithms can produce undiminished results with the use of synthetic datasets that closely resemble the properties of real data. The gap between synthetic data and real data is shrinking with the advancement of technology. Not only is creating synthetic data more cost and time efficient than collecting real-world data, it also adds security by eliminating the use of personal and sensitive information.
It has an immense importance when access to real-world data is forbidden for testing, training, or quality-assurance of AI processes due to privacy concerns owing to sensitivity of data and regulations from the industry. It makes it possible for organisations of every size and resource to capitalise on deep learning, where algorithms are capable of unsupervised learning from unstructured data, ultimately democratising AI and machine learning.
Synthetic data is particularly vital in testing algorithms and producing proofs-of-concept with a judicious use of resources in AI initiatives. It is employed to corroborate the potential efficacy of algorithms and provide confidence in making investments to move forward with a full-scale development of those algorithms.
Synthetic data can be used to make reliable predictions on high-risk, low occurrence events (also known as black swan events) like equipment malfunctions, vehicle accidents, and rare weather calamities. Training AI systems to perform well in every eventuality needs an enormous amount of data and use of this data can help achieve it. It finds its application in healthcare to create models of rare disease symptoms. By combining simulated and actual X-rays, AI algorithms are trained to identify conditions of illness.
Using synthetic data systems for detecting fraudulent activity can be tested and trained without exposing sensitive financial records. Waymo, the self-driving unit of Alphabet (Google’s parent company), tested its autonomous vehicles by driving 8 million miles on real roads and another 5 billion miles on simulated roadways which were generated using synthetic data.
Using synthetic data, third-party data providers can monetize data sharing directly or via data marketplaces without privacy risks. It can be leveraged to deliver greater value whilst offering great detail compared to any other data anonymization techniques. Use of synthetic data can hasten the development of data-driven products and services with realistic data at company’s disposal.
An interesting application of synthetic data is in nuclear science. Prior to building actual nuclear facilities, simulations are created to study the chemical reactions, analyze results, and devise safety measures. Scientists employ agents in these simulations, which are created using synthetic data that accurately represent the chemical and physical properties of elemental particles to understand interactions between the particles and their external environment. Simulations of nuclear reactions are represented by trillions of calculations and scientists leverage some of the world’s fastest supercomputers to run these models.
Objective: Our client, a Fortune 100 Oil and Gas company, wanted to automate and streamline the process of knowledge capturing from geo-scientists who are either leaving the company or moving to a new role.
Solution: Acuvate helped the client deploy a voice-based virtual assistant which can ask a predefined set of questions and capture scientists’ responses.
Microsoft Azure Speech Service is used to train a custom speech model for the virtual assistant. The model is trained using a combination of Acoustic data (audio), Language data (Text) and Phonetic data (Word Sounds).
In order to improve the transcribe and understanding accuracy of the virtual assistant, the speech model needs to be trained with synthetic data in addition to data from real sources (interview recordings, publicly available geology language documents etc.)
Synthesized speech from Google Wavenet, IBM Watson Speech, and Microsoft TTS was leveraged to train and improve the effectiveness of the speech model.
Result: The solution achieved a transcribe and understanding accuracy of more than 80%.
Creating it comes with its own challenges. Despite the potential value, it can be challenging to create high-quality data, especially if the system is complex. In addition, the generative models (which in turn create synthetic data) themselves have to be of a great accuracy to synthesize reliable data. The inaccuracy of generative models compound the errors in synthetic data and result in inferior data quality. It can have inherent biases and validating them against real world data is a challenging process.
Inconsistencies can arise while replicating complexities using the original datasets. Owing to the complexity of generative models, there are difficulties in tracking all necessary features required to accurately replicate the real-world data. It is sometimes possible to simplify representations within datasets while synthesizing data. This can hinder the performance of an algorithm when used in a real world setting.
While there is a great demand for high-quality data to propel artificial intelligence and train machine learning models, its supply however remains scarce. Today, many companies are employing lab-generated synthetic data to support their AI initiatives. This data is particularly valuable when availability of data is meagre and expensive to acquire. It will also be the best alternative to overcome the challenges of gathering real data in the machine learning process. Not only will there be advancements in the processes that generate synthetic data, the quality of data itself will appreciate to an extent that it will showcase highly accurate representations of the real world.
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