Google Synthetic Content Scaffolding
September 3, 2024
This essay is the work of a dumb dinobaby. No smart software required.
Google posted what I think is an important technical paper on the arXiv service. The write up is “Towards Realistic Synthetic User-Generated Content: A Scaffolding Approach to Generating Online Discussions.” The paper has six authors and presumably has the grade of “A”, a mark not award to the stochastic parrot write up about Google-type smart software.
For several years, Google has been exploring ways to make software that would produce content suitable for different use cases. One of these has been an effort to use transformer and other technology to produce synthetic data. The idea is that a set of real data is mimicked by AI so that “real” data does not have to be acquired, intercepted, captured, or scraped from systems in the real-time, highly litigious real world. I am not going to slog through the history of smart software and the research and application of synthetic data. If you are curious, check out Snorkel and the work of the Stanford Artificial Intelligence Lab or SAIL.
The paper I referenced above illustrates that Google is “close” to having a system which can generate allegedly realistic and good enough outputs to simulate the interaction of actual human beings in an online discussion group. I urge you to read the paper, not just the abstract.
Consider this diagram (which I know is impossible to read in this blog format so you will need the PDF of the cited write up):
The important point is that the process for creating synthetic “human” online discussions requires a series of steps. Notice that the final step is “fine tuned.” Why is this important? Most smart software is “tuned” or “calibrated” so that the signals generated by a non-synthetic content set are made to be “close enough” to the synthetic content set. In simpler terms, smart software is steered or shaped to match signals. When the match is “good enough,” the smart software is good enough to be deployed either for a test, a research project, or some use case.
Most of the AI write ups employ steering, directing, massaging, or weaponizing (yes, weaponizing) outputs to achieve an objective. Many jobs will be replaced or supplemented with AI. But the jobs for specialists who can curve fit smart software components to produce “good enough” content to achieve a goal or objective will remain in demand for the foreseeable future.
The paper states in its conclusion:
While these results are promising, this work represents an initial attempt at synthetic discussion thread generation, and there remain numerous avenues for future research. This includes potentially identifying other ways to explicitly encode thread structure, which proved particularly valuable in our results, on top of determining optimal approaches for designing prompts and both the number and type of examples used.
The write up is a preliminary report. It takes months to get data and approvals for this type of public document. How far has Google come between the idea to write up results and this document becoming available on August 15, 2024? My hunch is that Google has come a long way.
What’s the use case for this project? I will let younger, more optimistic minds answer this question. I am a dinobaby, and I have been around long enough to know a potent tool when I encounter one.
Stephen E Arnold, September 3, 2024
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