Synthetic Datasets: Reality Bytes
February 5, 2017
Years ago I did a project for an outfit specializing in an esoteric math space based on mereology. No, I won’t define it. You can check out the explanation in the Stanford Encyclopedia of Philosophy. The idea is that sparse information can yield useful insights. Even better, if mathematical methods were use to populate missing cells in a data system, one could analyze the data as if it were more than probability generated items. Then when real time data arrived to populate the sparse cells, the probability component would generate revised data for the cells without data. Nifty idea, just tough to explain to outfits struggling to move freight or sell off lease autos.
I thought of this company’s software system when I read “Synthetic Datasets Are a Game Changer.” Once again youthful wizards happily invent the future even though some of the systems and methods have been around for decades. For more information about the approach, the journal articles and books of Dr. Zbigniew Michaelewicz may be helpful.
The “Synthetic Databases…” write up triggered some yellow highlighter activity. I found this statement interesting:
Google researchers went as far as to say that even mediocre algorithms received state-of-the-art results given enough data.
The idea that algorithms can output “good enough” results when volumes of data are available to the number munching algorithms.
I also noted:
there are recent successes using a new technique called ‘synthetic datasets’ that could see us overcome those limitations. This new type of dataset consists of images and videos that are solely rendered by computers based on various parameters or scenarios. The process through which those datasets are created fall into 2 categories: Photo realistic rendering and Scenario rendering for lack of better description.
The focus here is not on figuring out how to move nuclear fuel rods around a reactor core or adjusting coal fired power plant outputs to minimize air pollution. The synthetic databases have an application in image related disciplines.
The idea of using rendering engines to create images for facial recognition or for video games is interesting. The write up mentions a number of companies pushing forward in this field; for example, Cvedia.
However, the use of NuTech’s methods populated databases of fact. I think the use of synthetic methods has a bright future. Oh, NuTech was acquired by Netezza. Guess what company owns the prescient NuTech Solutions’ technology? Give up? IBM, a company which has potent capabilities but does the most unusual things with those important systems and methods.
I suppose that is one reason why old wine looks like new IBM Holiday Spirit rum.
Stephen E Arnold, February 5, 2017
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