Some Fun with Synthetic Data: Includes a T Shirt
August 12, 2024
This essay is the work of a dumb dinobaby. No smart software required.
Academics and researchers often produce bogus results, fiddle images (remember the former president of Stanford University), or just make up stuff. Despite my misgivings, I want to highlight what appear to be semi-interesting assertions about synthetic data. For those not following the nuances of using real data, doing some mathematical cartwheels, and producing made-up data which are just as good as “real” data, synthetic data for me is associated with Dr. Chris Ré, the Stanford Artificial Intelligence Laboratory (remember the ex president of Stanford U., please). The term or code word for this approach to information suitable for training smart software is Snorkel. Snorkel became as company. Google embraced Snorkel. The looming litigation and big dollar settlements may make synthetic data a semi big thing in a tech dust devil called artificial intelligence. The T Shirt should read, “Synthetic data are write” like this:
I asked an AI system provided by the global leaders in computer security (yep, that’s Microsoft) to produce a T shirt for a synthetic data team. Great work and clever spelling to boot.
The “research” report appeared in Live Science. “AI Models Trained on Synthetic Data Could Break Down and Regurgitate Unintelligible Nonsense, Scientists Warn” asserts:
If left unchecked,”model collapse” could make AI systems less useful, and fill the internet with incomprehensible babble.
The unchecked term is a nice way of saying that synthetic data are cheap and less likely to become a target for copyright cops.
The article continues:
AI models such as GPT-4, which powers ChatGPT, or Claude 3 Opus rely on the many trillions of words shared online to get smarter, but as they gradually colonize the internet with their own output they may create self-damaging feedback loops. The end result, called “model collapse” by a team of researchers that investigated the phenomenon, could leave the internet filled with unintelligible gibberish if left unchecked.
People who think alike and create synthetic data will prove that “fake” data are as good as or better than “real” data. Why would anyone doubt such glib, well-educated people. Not me! Thanks, MSFT Copilot. Have you noticed similar outputs from your multitudinous AI systems?
In my opinion, the Internet when compared to commercial databases produced with actual editorial policies has been filled with “unintelligible gibberish” from the days I showed up at conferences to lecture about how hypertext was different from Gopher and Archie. When Mosaic sort of worked, I included that and left my Next computer at the office.
The write up continues:
As the generations of self-produced content accumulated, the researchers watched their model’s responses degrade into delirious ramblings.
After the data were fed into the system a number of time, the output presented was like this example from the researchers’ tests:
“architecture. In addition to being home to some of the world’s largest populations of black @-@ tailed jackrabbits, white @-@ tailed jackrabbits, blue @-@ tailed jackrabbits, red @-@ tailed jackrabbits, yellow @-.”
The output might be helpful to those interested in church architecture.
Here’s the wrap up to the research report:
This doesn’t mean doing away with synthetic data entirely, Shumailov said, but it does mean it will need to be better designed if models built on it are to work as intended. [Note: Ilia Shumailov, a computer scientist at the University of Oxford, worked on this study.]
I must admit that the write up does not make clear what data were “real” and what data were “synthetic.” I am not sure how the test moved from Wikipedia to synthetic data. I have no idea where the headline originated? Was it synthetic?
Nevertheless, I think one might conclude that using fancy math to make up data that’s as good as real life data might produce some interesting outputs.
Stephen E Arnold, August 12, 2024
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