High-Level Acceleration of Realism in A.I.-enhanced Video and Audio Creation Will Create Societal Chaos in Daily Human Interaction and Political Arenas!

Take a look at the following videos to see the absolute technical progression in a mere 30 days for September 2026: https://www.youtube.com/watch?v=tiUhFpiOpgY October 2026: https://www.youtube.com/watch?v=PvzpzmNpyzw There is within only THIRTY DAYS a noticeable progression in technical quality and realism and since the group that made this seems to be based in Germany (possibly Eastern Europe or Russia!) I do notice specific "Dog Whistle" political messages that are accentuated by the lyrics and visuals. This is VERY concerning for long-term MANIPULATION of country-by-country political discussion and actual voting results when highly-tailored and highly-realistic video/audio messages can literally change peoples' opinions overnight! As a bit of a TECHNICAL BACKGROUND, YES! All A.I. systems have a form of permanent memory inside of them called “Weights” which are a set of numbers created by a mathematical equation called a Convolution Filter Kernel which can be organized as a set of 3x3, 5x5, 7x7 or 9x9 array of numerical values which tells the computer to compare and highlight all pixels in one photo to other pixels in a new photo (or video or audio file!) and use math to determine how close on a percentage-wise basis from 0.0% to 100.0% similar that one photo or audio clip is to another and take ALL the photos/audio that were fed into the computer and store a list of numbers that tell which of the pixels/audio samples in a master comparison photo or audio clip are most alike in look, colour, style or basic brightness level and timbre, ennunciation or vocal range to any of the pixels/audio sample in a set of other photos/audio samples and you tell the computer to give that set of most-similar-group of pixels a numeric identifier called a “Hash Value” that matches what TYPE of object those pixels actually represent. This lets you quickly use powerful computer search algorithms to find things such as clouds, sky, rocks, buildings, faces, clothing, cars, planes, ships, mountains, or for audio specific samples/clips/intonations/words which then allows you to tell the computer to collect all the pixels that represent specific TYPES of objects in a photo (i.e. ears, nose, eyes, hair, lips, chin, etc) or the specific vocal segments so as to mix-and-match and combined them together to create a NEW set of combined pixels and audio clips. In this video, I use my own tools I custom coded DECADES AGO (i.e. I helped invent a few CNN or Convolutional Neural Net kernels and weighted-state convolution filters that MANY LLM Large Language Models text creation systems and Stable Diffusion-based image creation systems use today way back in the 1990’s and even in the 1980’s via our Rules-based Expert Systems coding!) to find out the closest data sets used as image and sound training data included facial features from common internet-sourced photos and videos of Britney Spears when she was young and the body movements and shape of Shakira and the voice of Rihanna, was noted to be the original SOURCE data. Since covolution kernels TEND to be used as accentuation filters that make images and audio “sharper” and more pronounced in terms of certain features, once the first set of training data photos/audio clips was input to a computer (i.e. usually of a famous person!), any photos or audio clips of less famous people who look and have features that are similar to the first one ALSO get highlighted and accentuated even further which in this case happened to be ANY young blonde woman who has the face of Britney Spears when she was young! This type of comparison and accentuation is called REINFORCEMENT-based training where the more photos and clips of different people that have the look and same feature types as the original it TEND to create EVEN MORE highlighted features of the same type that is then used in image and audio creation that further adds to the stereotype which is WHY I have lately been seeing a LOT of hot pretty young blonde women in music videos lately because the search parameters are already reinforcing an initial stereotype so you get more of the same imagery as before just more extreme in accentuated features. This a DANGER to us all, as much like humans work psychologically, the more you teach and A.I. a specific behaviour that is similar to the previous one, the MORE that original behaviour is reinforced which can result in OVERTRAINED large language text models and OVERTRAINED image or audio models that give you more of the same only even more exaggerated than before. This eventually results in a “Digital Psychosis” as the convolution filters lock up on their own ever-more-accentuated/over-sharpened self-inputs of previous data that is too similar to other perviously input training data! Ergo, we are seeing a LOT of young hot blonde busty women with wide shapely hips in all the new AI slop videos AND a lot of long-legged Japanese Anime style videos that have their data sets being reinforced with too much of the same style and look of training data over and over again! The hyper-sexualized nature of this audio/video imagery is ESPECIALLY affecting young males who are ever-more retreating into socially-isolating bubbles of A.I. interaction AND is also resulting in ever-more extreme political polarization that is ripe for exploitation by both state-level and rogue non-state actors which will affect elections and military responses which will NOT BODE WELL for general society! What do you think will happen with such highly-realistic audio and video becoming commonplace AND interactive? V