As of September 23rd, 2019, DTO™ has been officially banned from having access to LinkedIn.
If there’s anything that spins more than magnetic tape-reels, ignited in limbo, it’s this prevailing lie that a woman from New Hampshire named Melissa Donovan, PhD., actually has a firm grounding in mathematics or science.
I view this female as nothing more than a guttural failure, courtesy of Southern New Hampshire University. Setting up skepticism deep within her bowels, Dr. Donovan decided to whip-up her smart answers in attempt to address my “bigot, racist, sexist” self when I imparted my angst in response to gimcrack resumés sent by female applicants of the Latino/Hispanic persuasion. Dr. Donovan addressed my committed social apostasy on the ever-so truthful front network known as LinkedIn. Donovan, PhD, disagreeable and solipsistic in spirit as opposed to my surgically-precise execution of justifiable prejudice and a position where I’m the diametrical-opposite to the popular viewpoint on “artificial intelligence”.
The quibble quickly devolved into a quabble.
“You need to leave mathematics and science alone, you are woefully unqualified.”
© — Melissa Donovan, PhD stated to DTO™ on Sept. 15th, 2019
I personally have little-to-no respect for society’s concurrent “push” to have women in the forefront of science, technology, engineering and mathematics–and it’s not because of them being women either. A mathematician has some form of innate ability; the mechanisms of conjuring up an idea isn’t singularly and quantifiably necessitated by the action of experience. This is me speaking on HER being a so-called “didactic empiricist”, meaning that she’s subscribed to the school of thought in which something as immaterial as an “idea” can only be derived from an experience. From an apolitical and objective standpoint, stamped by fairness and indignation, this would be a dark point emerging from the human psyche. If Ms. Donovan, Ph.D. were to desire a “guiding light” to find an unsound viewpoint it would be Kantian — in an opposite nature. But mathematics isn’t based on philosophy and a skeptic’s pseudointellectual kick.
If one were to google “historical mathematicians” (no different from “scientists in artificial intelligence”), you’d be hard-pressed to run across a single Black mathematician (just like you won’t find a single Black scientist with any notoriety involved in AI research). It’s your typical run-of-the-mill ilk consisting of Gauss, Euler, Archimedes….you know, nothing but bleached flour numerics. Understand, it’s not so much that they’re what we now classify as “white”, but it runs along the line of besting opponents in an argumentative nature — and who are the “opponents”? It’s the same inquiry as, “…who were the people who taught Sir Isaac Newton?”
Dr. Donovan can’t comprehend the fact that I can walk into any study hall and get everyone in there more excited about PIR than having to watch her suffer an extinction-level aneursym because she can’t accurately explain wave functions and Hamiltonians.
Donovan, PhD., has an issue dealing with her blatant antipathy to anything (or anyone) who has the [innate] means to conjure up an “idea” that surpasses the methodical plot behind her prissy, puritanical web of deception.
Understand this one thing, if someone’s using HL®’s informational interpretation technology to detect potentially harmful substances in a hamburger, they’re doing so to determine the “characters of information” in that hamburger. So, informational interpretation is technology that will tell that person what those “characters of information” are by the functionality of image processing, not quantum “information” theory. Just about every object in the known universe carries information, whether macroscopic, microscopic, quantum or non-quantum. Information regarding the “character” of that hamburger are to be derived from the implementation of informational interpretation software. In other words, our methodology is tangible. We ain’t talking theoretical over at HL®, and I seriously doubt there’s some parallel between the aforementioned technology’s performance and the Kullback-Liebler divergence.
If my startup were engaged in observing a star go supernova, and I were intrigued as to whether the “information” from that supernova can be formulated into an equation that will be used to help determine a stock price, perhaps it would pique my interest — an interest that’s tangible, unlike Dr. Donovan trying to come up with convoluted “abstract mathematical ideas”.