Creating synthetic data from real-world data is a powerful way to train self-driving cars, train AI models, and simulate “black swan” situations. This article will discuss the different ways that artificial intelligence can be trained and used for this purpose. Regardless of industry, synthetic data can benefit businesses and individuals in a variety of ways. For instance, it can help companies develop better AI models by training them with real-world data.
Synthetic data is based on real-world data
While artificial intelligence benefits from the ability to simulate real data, a synthetic version of the same data is also valuable. It can be used in marketing, software testing, creating digital twins, and even to simulate alternate futures, the metaverse, and other situations where real-world data is not available. Many industries use synthetic data, from banks to financial institutions, to better understand market behaviors and combat financial fraud. Retailers rely on this technology to create automated checkout systems and cashier-less stores, to analyze customer demographics.
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The idea for synthetic data generation dates back to the early 1990s, when Harvard statistics professor Donald B. Rubin was helping the U.S. Census with data. Rubin first described the concept in a 1993 paper that referred to multiple simulated datasets. But his method was not perfect. It was still able to generate data that matched the real-world data. Consequently, a synthetic data generation algorithm can reproduce a variety of statistical properties, such as the correlation between the variables in the data set.
It can be generated by a computer simulation
This artificially generated data is ideal for predictive analytics because it is anonymous, protecting customer privacy. Furthermore, artificially generated data points are less costly than their statistical counterparts. Such data are also useful in areas such as finance and healthcare, where customer interactions are rare. In fact, many companies already use synthetic data for this purpose. This article will discuss the benefits and use cases of synthetic data for predictive analytics. Further, you will discover why synthetic data is important for businesses.
Aside from its application in generating accurate predictions, synthetic data can also be used to train AI systems to deal with edge cases. While these cases are rare, they are nonetheless crucial to the success of any AI system. These edge cases are similar to the main target, but differ in a few key aspects. For example, an image classifier might consider partially visible objects as an edge case, and vice versa.
It can be used to train self-driving cars
Unlike real-world data, synthetic data can be changed to improve a model and train self-driving cars. These data can vary by lighting, weather, traffic density, and number of pedestrians. Currently, synthetic datasets are produced using the CARLA open-source gaming engine. While this isn’t quite as photo-realistic as Grand Theft Auto, the datasets are more diverse and photo-realistic than those produced by other methods.
As artificial intelligence and machine learning techniques become more advanced, companies are turning to synthetic data generation as a way to level the playing field. This can be particularly useful for smaller upstarts, which may not have the capital to invest in millions of real-world driving data. Google has invested billions in Waymo and has collected millions of miles of real-world data to train its self-driving cars.
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