“Three-Point Interrogation: The Hilarious Highs and Lows of Using Synthetic Data in AI”
“3 Questions: The pros and cons of synthetic data in AI”
“If you’re a company that needs to test an AI system, for instance, you can’t wait until you have years of data to learn from because you’d go out of business. So that’s why this field, synthetic data generation, has sprung up,” reiterates Kalyan Veeramachaneni, Principal Research Scientist at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL).
It seems that MIT’s erudite scientists are having a lightbulb moment about synthetic data. It’s an amusing spectacle, one that borders on a comedic sketch. But let’s not digress into stand-up territory. Instead, let’s delve into the deep, thrilling waters of synthetic data generation.
If you’ve had the misfortune of short-changing your AI system due to lack of data, brace yourself for a sweet reprieve. Synthetic data generation, this fast-emerging, white knight of the tech world is riding to your rescue. Evidently, you don’t need to put your ambitious AI project on a painfully slow drip of naturally gathered data. Instead, create your own data synthetically and save your business from sinking into obscurity. Now wouldn’t that be a nice bedtime story for your grandkids?
Veeramachaneni, with all his characteristic modesty, points out the potential pinpricks in this seemingly impenetrable synthetic data bubble. Potential biases, he says, could seep into the intended neutrality of the data.
It’s disconcerting to hear the possibilities of racial or gender bias seeping into our data, subtly manipulating our AI systems’ decisions. And here we were thinking that the only biases in play were those between iOS and Android users. But, sarcasm aside, it’s a valid concern. After all, synthetic data, just like people, is created, not born. And creators inevitably leave imprints of their biases, purposefully or inadvertently.
In summary, the golden age of synthetic data might be upon us. Still, there’s an underground river of bias that could potentially pollute the pristine waters of artificial intelligence. So, maybe tread slowly, or dive in with bias-proof goggles. Remember, even the heady rush of new technology can’t fully drown out the persistent voice of caution.