Could synthetic data be the solution to rapidly train artificial intelligence algorithms? There are advantages and disadvantages to synthetic data; however, many technology experts believe that synthetic data is the key to democratizing machine learning and to accelerate testing and adoption of artificial intelligence algorithms into our daily lives.
One way to create synthetic data is to use real-world data but strip the identifying aspects such as names, emails, social security numbers and addresses from the data set so that it is anonymized.
Similar to how a research scientist might use synthetic material to complete experiments at low risk, data scientists can leverage synthetic data to minimize time, cost and risk.
Synthetic data allows organizations of every size and resource levels the possibility to also capitalize on learning that is powered by deep data sets which ultimately can democratize machine learning.
Synthetic data can also complement real-world data so that testing can occur for every imaginable variable even there isn’t a good example in the real data set.
If synthetic data isn’t nearly identical to a real-world data set, it can compromise the quality of decision-making that is being done based on the data.
Whenever privacy concerns are an issue such as in the financial and healthcare industries or an enormous data set is required to train machine learning algorithms, synthetic data sets can propel progress.
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