In this article we will learn how artificial intelligence creates synthetic data for machine learning.<\/p>\n\n
Today, almost every industry uses AI in one form or the other to harness the advantages it offers. This has piqued interest in AI and many of its related sub-domains. There is an ever-increasing demand for Artificial Intelligence \/Machine Learning based applications.<\/span><\/p>\n Machine Learning-based applications heavily depend on data to train the machines with a training data set. This data set contains everything from independent or predictor variables to the dependent or predicted outcomes for the device to learn from. These data sets are typically massive. The availability of such data is not as simple as it sounds. This data comes with breach of privacy concerns and sometimes even risks of data theft.\u00a0<\/span><\/p>\n One way you can sidestep this problem is by using artificial data or what is called Synthetic data. AI classes <\/span>are offered online, from where you can study about AI and ML in detail. <\/span>In the following sections, we shall explore synthetic data and some techniques used in the industry to generate synthetic data.<\/span><\/p>\n Synthetic data is artificially created data and not organically collected data from genuine sources. Often created using purpose-built algorithms, this kind of data has several uses, including product testing, data model validation and Machine Learning\/Deep Learning model training. <\/span>One reason synthetic data is increasing in popularity is the various issues and hurdles in securing genuine data. Issues like privacy and data theft concerns crop up when sourcing data from sources.<\/span><\/p>\n Some benefits of using synthetic data in a machine learning setup are:<\/span><\/p>\n To give you a perspective, one use case for increasing adoption of synthetic data in Machine Learning and Deep Learning setup is that of Self-driving simulations.<\/span><\/p>\n As autonomous driving technology development company Waymo is finding out, real-life experiments are expensive, with it having to create an entire mockup of a city for its self-driving simulations. <\/span>Another example is self-driving Uber cars causing deadly crashes, dealing with a crippling setback to their operations in Arizona. Some start-ups and businesses are trying to solve this problem by helping with creating synthetic data for their customers using original data. This synthetic data is privacy compliant.<\/span><\/p>\nSynthetic data- What is it?<\/b><\/h2>\n
\n