Initially, we wanted to do the first trial of our “alpha testing” for the PIR: Premier Information Retrieval mobile application…but we have to postpone until next month (February).
However, the world’s premier antiartificial influence, Hexagon Lavish®, a scientific research and development startup, will be conducting the second leg of its “alpha testing” for early users of its informational interpretation technology, PIR: Premier Information Retrieval. The technology will be introduced in the nature of a mobile application that performs immediate detection of potentially harmful substances [in food and drink] via the smartphone camera. Our beta version has been developed for Android® smartphones.
Our aim with “alpha testing” is to test the waters with the beta version of informational interpretation software and also to see how market behavior plays out (measuring the inefficiency). In these days, with the onset of COVID-19 changing the way people see the world around them, they are going to have a growing concern with what’s in their food and having the means to make that determination. Enter the era of PIR.
For the moment, our software is designed to perform immediate detection (< 20 seconds) of foods made with, containing or prepared with tomatoes or black beans. Solid foods, yes, but also liquids…
Our current investor’s wife has a susceptibility to tomatoes [and strawberries], so the personal affection towards PIR was sufficient to have his interest in our product piqued.
The vision is to pursue the implementation and sustainability of practicality as far as scientific application for the “everyday layperson”. The development of informational interpretation technology is not a moonshot ambition. Rather, it’s about getting the alpha mined out, and let us explain what that means. Emphasizing practicality instead of chasing moonshot ambition illustrates a comprehension of the difference between risk and uncertainty. The former entails randomness whose model parameters you are uncertain; the latter entails randomness whose models you are uncertain.
It takes a ton of experimental data to play with here just to retrieve information that resides in 10⁵ dimensions, so for people to see this as trivial, it tells me that they just don’t realize the amount of computational firepower it takes to pull this off.
For instance, in the ImageShot up above featuring a regular hamburger from McDonald’s, there is ketchup on the hamburger. Tomato paste is an ingredient used in making ketchup. Now you can’t see the ketchup in the ImageShot…but what does PIR see?