8-Bit Mystique: An Ingardenian Aesthetic Analysis of the Appeal of Retro Computer Games

“8-Bit Mystique: An Ingardenian Aesthetic Analysis of the Appeal of Retro Computer Games”

In Roman Ingarden’s Aesthetics and Ontology, edited by Leszek Sosnowski and Natalia Anna Michna • London: Bloomsbury, 2023

ABSTRACT: Recent years have seen revived interest in 8-bit computer games developed in the 1980s and the increasing popularity of “8-bit-style” or “retro” games designed to imitate their look. Judged objectively, 8-bit games appear far more “primitive” than typical contemporary video games, leading some to suggest that they are deficient works of art whose resurgent popularity results solely from nostalgia. However, by drawing on Ingarden’s analysis of artworks as schematic constructs, we argue that 8-bit-style games’ “primitiveness” is actually a form of indeterminacy that can generate singularly meaningful aesthetic experiences by allowing (and requiring) players to perform a uniquely enjoyable kind of concretization that is impossible with more “sophisticated” contemporary games. We also draw on Ingarden’s account of the “life cycle” of a work of art to show how 8-bit games’ pattern of initial popularity, neglect, and revival reflects an organic vitality demonstrated not by kitsch but by exceptional artwork.

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A Better Way of Forecasting Employees’ Performance: Evaluating the use of composite ceiling-floor models for predicting the likely range of workers’ future job performance

A joint range model created with Comport_AI

Medium.com • March 12, 2023

SUMMARY: This text is Part 3 of a three-article series on “Advanced modelling of workers’ future performance ranges through ANNs with custom loss functions.” It demonstrates how custom ceiling and floor models can be combined to create a composite prediction interval that can outperform simpler models based on MAE or SD in forecasting the probable range of workers’ future job performance.

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Optimize Your Performance Intervals! Use ANNs with custom loss functions to predict probable ceilings and floors for workers’ future job performance

A ceiling model created with Comport_AI

Medium.com • March 12, 2023

SUMMARY: This text is Part 2 of a three-article series on “Advanced modelling of workers’ future performance ranges through ANNs with custom loss functions.” It investigates the mechanics of independently modelling the likely ceiling and floor of the range of a worker’s probable future job performance using separate artificial neural networks with custom loss functions.

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One Number Is Rarely Enough: Why prediction intervals are critical (and challenging!) for HR predictive analytics

A joint range model created with Comport_AI

Medium.com • March 12, 2023

SUMMARY: This text is Part 1 of a three-article series on “Advanced modelling of workers’ future performance ranges through ANNs with custom loss functions.” It explores why it’s useful to predict the probable ceiling and floor for an employee’s future performance – and why it’s difficult to do so effectively, using conventional methods based on mean absolute error or standard deviation.

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Comport_AI™

Comport_AI screenshots

Comport_AI™ (version 0.3.22) • March 5, 2023

ABSTRACT: Comport_AI is a free open-source HR predictive analytics tool in the form of a Python-based web app that uses advanced machine learning to forecast the likely range of a worker’s future job performance. Rather than mechanistically deriving the predicted ceiling and floor of a worker’s future performance from a single predicted target value using calculations based on MAE or SD, Comport_AI treats the likely ceiling and likely floor of a worker’s performance during a future timeframe as independent entities, which are modelled by artificial neural networks whose custom loss functions enable them to formulate prediction intervals that are as small as possible, while being just large enough to contain a worker’s actual future performance value, in the vast majority of cases. This allows more precise, nuanced, and useful forecasting of workers’ future job performance. Comport_AI utilizes TensorFlow, Keras, scikit-learn, FastAPI, Uvicorn, Jinja2, NumPy, Pandas, and Matplotlib. It’s developed by Matthew E. Gladden (with support from Cognitive Firewall LLC and NeuraXenetica LLC) and is made available for use under GNU General Public License Version 3.

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