The Difference between AI and a Calculator

And holiday wishes to all those 6B+ Dying Vaxxers!!!

Let’s kick this off by wishing the Pro Vaxxers a Merry Christmas and a New Year filled with misery and death - particularly the Hardcore MORONS who continue to shoot more Death Shot Boosters despite overwhelming evidence that they are deadly.

Fuck ya’ll.

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The thing is… calculators do not hallucinate nor deviate from reality. 1+1= 2. Always.

Not so with AI (Automated Idiocy):

“Training on AI-generated data causes models to hallucinate, become delusional, and deviate from reality to the point where they’re no longer useful: i.e., Model Collapse.

The more ‘poisoned’ the data is with artificial content, the more quickly an AI model collapses as minority data is forgotten or lost. The majority of data becomes corrupted, and long-tail statistical data distributions are either ignored or replaced with nonsense.

Those models lose the capacity to understand long-tail information (improbable, but important data) that is no longer represented. Information on topics like serious injuries, getting punched in the nose, how dangerous wild animals can be, and what it’s like to truly be hungry because you can’t find food. Their models default to synthetic human artifice instead of understanding real implications.

The proposed thesis is that AI models, human minds, larger human cultures, and our furry little friends, all train on available data.

Brains (or brain regions) undergo model collapse just like AI systems. They become unable to reference reality, they become delusional, and hallucinate things that make no sense. Hence, the ‘Why do we need farmers when food just comes from the store’ level of disconnection observed in urban populations.

In a heavily urban setting, humans train on ‘data sets’ that are nearly wholly artificial. The less time spent outside, the less time spent interacting with the real physical world around them, the less accurate their model of reality becomes. A rocky slope up a hill may be 100% real, a grass playing field may be 70% real, and a concrete sidewalk may be around 40% real. At some point, however, the ‘salted’ artificial data is sufficient to corrupt the real-world knowledge of individuals and cause model collapse.

I am reminded of an anecdote from when I was a child. A cousin came to play with my siblings and I. My family had been raised going camping and hiking and wandering the wilds since before I can remember. Somewhere around age 3 or 4, our cousin came to visit and we went cruising up a hill hiking with our fathers in tow.

This cousin, however, had grown up in a suburban hellhole where everything was artificial. As such, he found it nearly impossible to navigate a sloped hill. His experience with walking and running had only ever consisted of flat, soft, curated environments produced by other people. He had no experience, or ability, in navigating a dirt trail at a 20 degree incline. His neurological model of the world was trained on human-produced data, and could not function when confronted with reality.

The universal thesis for model collapse is that advanced modeling systems, when trained on information produced by entities of their own class, lose information fidelity inter-generationally. After multiple generations of training on poisoned datasets, the models themselves become delusional, hallucinate false information, and cease to function.

In the same way that AI models become delusional and hallucinate when too much AI-generated data is in the training dataset, humans also become delusional when too much human-generated data is in their training dataset.”

The author then applies this to the famous Mouse Utopia experiment of John Calhoun, in which a handful of breeding-pair mice were put in a large artificial “world” with unlimited food and water and space for 6,000 mice. The researchers anticipated the mice would proliferate to the maximum limit of 6,000 and then experience some version of population overshoot.

But this isn’t what happened. The mice population reached 2,200 and then collapsed as anti-social behaviors exploded and reproduction ceased. A wide variety of causal factors have been posited and discussed over the decades since the 1962-72 experiments. The Unified Theory of Model Collapse suggests the core causal mechanism is the mice born in this artificial world of endless abundance “trained” on increasingly artificial data and this disconnect from “raw” real-world experience deranged not just individual mice but the entire society of the artificial world of abundance in a crowded, artificial “urban” environment.

The author suggests the current human population has reached the urban population density (roughly 60% of humanity lives in urban settings) that triggers anti-social behaviors and a rapid decline in fertility / reproduction. Many explanations for the current sharp decline in birth rates in most developed-world economies have been offered, and the Unified Theory of Model Collapse shed important light on the causal mechanisms of artificial environments and abundance.

Source

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