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The subject of the last Meetup was applying Machine Learning to mtDNA, & in this event, I'll show you how to apply Machine Learning to a wider set of problems in the sciences, including physics, & reconstructing order, when no time information is available.
Though the subject is very different from mtDNA, the ideas are relatively similar, in that Machine Learning is much more than prediction, & is instead a fundamental tool of the scientific method.
This will allow us to understand how people are creating "generative A.I." videos & images, but we'll be taking a rigorous, mathematical approach, to what is a very serious topic, rather than trying to generate memes.
In fact, we'll prove mathematically that a Turing Machine (i.e., a computer), cannot generate information, & can only manipulate a constant amount of information, or destroy information, forcing the question of whether generative A.I. is by definition infringement.
I'll also discuss actual Artificial Intelligence, specifically, optimization, & how this differs from Machine Learning. I'll introduce a generalized optimization algorithm I developed that can balance weights, sort a list, or interpolate a polynomial, all without any specialization, suggesting the possibility of universally intelligent machines.
Slides will be coming soon, for now see Section 2.4 of my paper, "Vectorized Machine Learning" for the core physics ML algorithms.
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