Charles Davi

• B.A. Computer Science; J.D.
• Black Tree AutoML

We propose a model of time-dilation that follows from the application of concepts from information theory and computer theory to physical systems. Our model predicts equations for time-dilation that are identical in form to those predicted by the special theory of relativity. In short, our model can be viewed as an alternative set of postulates roo...

In this article, I'll present a new model of artificial intelligence rooted in information theory that makes use of tractable, low-degree polynomial algorithms that nonetheless allow for the analysis of the same types of extremely high-dimensional datasets typically used in machine learning and deep learning techniques. Specifically, I'll show how...

In a series of lemmas and corollaries, I proved that under certain reasonable assumptions, you can classify and cluster datasets with literally perfect accuracy. Of course, real world datasets don't perfectly conform to the assumptions, but my work nonetheless shows, that worst-case polynomial runtime algorithms can produce astonishingly high accur...

Below we introduce two theorems that show a set of real numbers is sorted if and only if (1) the distance between adjacent pairs in the resultant ordering is minimized, and (2) if and only if the amount of information required to encode the resultant ordering, when expressed as a particular class of recurrence relations, is minimized. As such, thes...

Below we present and apply a fundamental equation of epistemology, that relates information, knowledge, and uncertainty, and apply that same equation to random variables using information theory. Also attached is software that applies the results to a deep learning classification problem.

We present a series of Machine Learning algorithms that can quickly classify and analyze large datasets of genetic sequences. All of the algorithms presented have a worst-case polynomial runtime, even when run in serial. As an application, we show that human mtDNA can be used to quickly and reliably predict ethnicity across 36 global ethnicities, u...

Below I present an optimization algorithm that appears to be universal, in that it can solve high-dimensional interpolation problems, problems involving physical objects, and even sort a list, in each case without any specialization. The runtime is fixed ex ante, though the algorithm is itself non-deterministic.

Below are several papers that contain fundamental results in Artificial Intelligence, Computer Theory, and Set Theory. When appropriate, code is attached.

Many consumer devices can be used to perform parallel computations, and in a series of approximately five-hundred research notes, I introduced a new and comprehensive model of artificial intelligence rooted in information theory and parallel computing, that allows for classification and prediction in worst-case polynomial time, using what is effect...

Below is a set of lemmas, corollaries, and proofs, related to the logarithm of Aleph_0, at the intersection of information theory, computer theory, and set theory.

My letter to The Congressional A.I. Caucus regarding likely anticompetitive practices in the market for A.I. software.

A letter I wrote to the U.S. Congressional Artificial Intelligence Caucus regarding the dangers of A.I. in small devices.

We present a new model of artificial intelligence and a new model of physics, each rooted in information theory, computer theory, and combinatorics. Chapter II covers the standard topics in machine learning and deep learning, including image processing, data classification, and function prediction. More advanced topics include vectorized image proc...

We present a model of physics rooted in discrete mathematics that implies the correct equations for time-dilation, gravity, charge, magnetism, and is consistent with the fundamentals of quantum mechanics. We show that the model presented herein is consistent with all experiments, of which we are aware, that test the Theory of Relativity, and propos...

In a previous paper, I introduced a new model of artificial intelligence rooted in information theory that can solve deep learning problems quickly and accurately in polynomial time. In this paper, I'll present another set of algorithms that are so efficient, they allow for real-time deep learning on consumer devices. The obvious corollary of this...

In a previous paper, I introduced a new model of Artificial Intelligence rooted in information theory that can solve essentially any machine learning or deep learning problem in low-degree polynomial time. In this paper, I'm going to introduce an application called Prometheus that consolidates this model into a simple GUI interface that allows for...

In this article, I'm going to present a low-degree polynomial runtime image partition algorithm that can quickly and reliably partition an image into objectively distinct regions, using only the original image as input, without any training dataset or other exogenous information. All of the code necessary to run the algorithm is available on my res...

In this article, I'm going to apply the new, polynomial time model of artificial intelligence that I've developed to the MNIST numerical character dataset, as well as two small image datasets made with an ordinary iPhone camera. The MNIST dataset was analyzed on a supervised basis, with a success rate of 95.402%, where success is measured by the pe...

In this note, I'll present an unsupervised algorithm that can extract three-dimensional features from an ordinary two-dimensional image, and detect edges within the image, thereby extracting two-dimensional shape information, in each case in polynomial time. Note that I retain all rights, copyright and otherwise, to all of the algorithms, and other...

In this article, I'm going to apply the new, polynomial time model of artificial intelligence that I've developed to four well-known datasets from the UCI Machine Learning Repository. For each of the four classification problems, the categorizations and predictions generated by the algorithms were generated on an unsupervised basis. Over the four c...

This paper is for the most part a refutation of the works of Hu and Black on the incentives created by credit default swaps in the bankruptcy context. We show that Hu and Black's concerns over empty voting and negative economic interest in the bankruptcy context are entirely without merit.

In 1966, Erdős, Goodman, and Posa showed that the intersection number of a finite graph G is the minimum number of complete subgraphs of G needed to cover G. We show that the theorem also holds for infinite graphs with countable vertex sets. We then categorize the class of all graphs with countable vertex sets that have a finite intersection number...