Decadent Singularity

Debuking the "Nancy Follows Trends" Accusations
I have recently found myself facing accusations of following trends and jumping on the machine learning bandwagon, particularly after the release of GPT-3. However, I must clarify that my interest in Markov Models predates the emergence of GPT-2, dating back to 2007 when I embarked on on the Symta programming language project that required a pattern matching engine. My knowledge of generative grammars and Markov models originated from a Common Lisp example, although I swiftly dismissed the single token-approach due to the inherent limitations of natural language as a context-free grammar. At that time, I actually developed a rudimentary attention-based Markov Model, employing a voting process to establish correlations between various elements within the input and output in the C programming language (which I elaborated upon in my book, "One Life in Russia"). My aim was to handle the entire state as a whole and transform it into multiple output values, rather than a single token, in order to achieve greater generality. Unfortunately, the model proved to be prohibitively slow, and lacking any reference to existing research literature, I ultimately abandoned the project, returning to the classic POP-11/Refal pattern matching paradigm.

Subsequently, I chanced upon the concept of word embedding, which involved the introduction of a vector space to represent natural language words. While intriguing, this approach did not offer significant utility for my language project at the time. Regrettably, I failed to recognize how it could optimize the functioning of the Markov model. Moreover, my understanding of linear algebra was limited, as I regarded it solely as a tool for 3D graphics, rather than recognizing its broader applicability, including its relevance to Markov Inference as a matrix multiplication. I possessed knowledge of KD-trees and BSPs, but their connection to Markov models eluded me. Consequently, I dismissed these advancements as mere academic noise.

Suddenly, another transgender individual contacted me, expressing that Markov Models were now capable of performing advanced pattern matching and more. I was taken aback and questioned how this problem had been solved. This prompted me to delve into the current research, and I discovered the elegant methodologies employed to address the issue. It made me wonder if it was finally feasible to integrate the solution into Symta, which had always been my aspiration.

Therefore, I must emphasize that my engagement with Markov Models predates any trendiness; rather, I am genuinely enthusiastic about the elegant resolution of a problem that I previously found too challenging for my skills. Nevertheless, even if I had possessed the necessary mathematical proficiency, I would have been unable to construct a robust model for such complex pattern matching.

Current Mood: contemplative
Current Music: Caught In Joy - Feathered Swan