Synthetic Data Are Better Than Data from Real Life. Does Better Mean Cheaper?

March 22, 2022

I read “When It Comes To AI, Can We Ditch The Datasets?” The answer, as you may have surmised, is, “Absolutely.”

Synthetic data is like polystyrene. Great for building giant garbage islands and not so great for the environment. So trade offs. What’s the big deal?

The write up explains:

To circumvent some of the problems presented by datasets, MIT researchers developed a method for training a machine learning model that, rather than using a dataset, uses a special type of machine-learning model to generate extremely realistic synthetic data that can train another model for downstream vision tasks.

What can one do with made up data about real life? That’s an easy one to answer:

Once a generative model has been trained on real data, it can generate synthetic data that are so realistic they are nearly indistinguishable from the real thing.

What can go wrong? According to the article, nothing.

Well, nothing might be too strong a word. The write up admits:

But he [a fake data wizard] cautions that there are some limitations to using generative models. In some cases, these models can reveal source data, which can pose privacy risks, and they could amplify biases in the datasets they are trained on if they aren’t properly audited.

Yeah, privacy and bias. The write up does not mention incorrect or off base guidance.

But that’s close enough for nothing for an expert hooked up with IBM Watson (yes, that Watson) and MIT (yes, the esteemed institution which was in financial thrall to one alleged human trafficker).

And Dr. Timnit Gebru’s concerns? Not mentioned. And what about the problems identified in Cathy O’Neill’s Weapons of Math Destruction? Not mentioned.

Hey, it was a short article. Synthetic data are a thing and they will help grade your child’s school work, determine who is a top performer, and invest your money so you can retire with no worries.

No worries, right.

Stephen E Arnold, March 22, 2022


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