Understanding Synthetic Data in the Modern World
The human body needs food to sustain itself, and the human mind relies on knowledge, experience, and perception to sharpen its thinking. In a similar vein, artificial intelligence needs data, tons, and tons of raw data, to drive its mechanism and be able to function according to the required expectations.
Data, therefore, has become a central currency in the modern economy that it has become the fuel that allows AI to produce credible outputs. Thus, AI technologies are only able to undertake predictive analysis, generate texts, or even parse medical images to detect illnesses. Because data fuels so much of the work in our world today, it has become a precious commodity, with the demand for high-quality, real data growing exponentially, to allow organizations to undertake data-driven steps for optimum outcomes through using machine learning and predictive modeling.
Such a demand for increasingly copious amounts of data has to be balanced against grounds of privacy, as users do not want their data harvested and collected, and for good reasons too. Most tech companies have for the longest time exploited user data for their own benefits, without letting users know the extent to which their digital footprint is being tracked.
In this context, synthetic data becomes extremely important. Synthetic data refers to computer-generated data, which is cheap to produce, and is automatically labeled. The USP of synthetic data is that it looks and feels like real data, thereby allowing one to train AIs further. Even as synthetic data is not a new idea, it is changing the entire value chain of data production and organization, as synthetic data allows practitioners to digitally generate the right kind of data they require, tailored specifically according to their stringent demands. A popular study conducted by Gartner talks about how 60% of all data used in the development of AI will be synthetic by the year 2024.
Synthetic data can be employed for just all the same tasks as real data, such as text, augmentation, sentiment analysis, and language translation, all of which are crucial processes in natural language processing that allow artificial intelligence to augment its robustness and diversity, as synthetic data can be specified to be more diverse. Therefore synthetic data will overhaul the realm of language. An example of this is how Anthem, one of the foremost health insurance companies, has announced its planned collaboration with Google Cloud to generate a colossal amount of synthetic data to train AI to parse synthetic medical histories and insurance claims to allow it to detect fraud and offer personalized patient care.
Therefore, synthetic data is a powerful solution to the problem of data poverty that we face in the real world. It is, however, not without its problems, as we shall explore in the next blog.
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