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Babies learn to talk from hearing other humans mostly their parents repeatedly produce sounds.
Slowly, through repetition and discovering patterns, infants start connecting those sounds to meaning.
Through a lot of practice, they eventually manage to produce similar sounds that humans around them can understand.
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Take fraud detection in insurance claims.
Thousands upon thousands of both.
You get where this is going, because the same applies to healthcare records and financial data.
More esoteric but just as worrying are all the algorithms trained on text, pictures, and videos.
Also, what if theres simply not enough data available to train an AI on all eventualities?
500 years and 11 billion miles.
You dont have to be a super-brained genius to figure out that the current process is not ideal.
So what can we do?
How can we create enough, privacy-respecting, non-problematic, all-eventuality-covering, accurately-labeled data?
You guessed it: more AI.
Take Waymo, Alphabets autonomous driving company.
More methods for producing synthetic data are gaining ground.
Thescalability of this pop in of modelmakes collecting data less time consuming and less expensive for data hungry businesses.
In a GAN, two AIs are pitted against each other.
Synthetic data can give smaller players the opportunity to turn the tables.
Lately, more methods for producing synthetic data have been gaining ground.
Third, it can protect privacy and copyright, as the data is, well, synthetic.
And finally, and perhaps most importantly, it can reduce biased outcomes.
With AI playing an increasingly larger role in technology and society, expectations around synthetic data are pretty optimistic.
Gartner has famously estimated that60% of training data will be synthetic data by 2024.
Data has been called the most valuable commodity in the digital age.
Synthetic data can give smaller players the opportunity to turn the tables.
