Category: AI, Computation, and Technology

RT @WalterIII: AI IS NOT GUIDED BY REALTIME CAPITAL-IN-TOTO EVALUATION NOR OF ITS OFFSPRING
When we “imagine”, do we not “experience” what…

RT @curtdoolittle: @ladypharaoh777 @BrianRoemmele –“How is the legacy data you choose entered into the AI? Is some of it scanned?”–

Most…

–“How is the legacy data you choose entered into the AI? Is some of it scanned?”–

Most human knowledge is now available digitally, and automated programs (‘bots’) can crawl the internet much like search engines do, programmatically collecting and indexing information.

Some relevant estimates:
Arts & Letters – The digitization of literature, music, and other cultural artifacts has surged. While quantifying this as “knowledge” is complex, the volume of digital content available today vastly exceeds anything prior to the digital era.
Books – An estimated 25-50% of all books published since the invention of the printing press have been produced since the desktop computing era.
Photographs – Over 90% of all photographs ever taken were created in the digital era.
Science – 70-80% of all scientific literature has been published since the rise of digital computing.
Data – The volume of digital data is even more dramatic, with some estimates suggesting that 90% of all existing data was generated in just the last two years.

Given this explosion of digital content:
– Search engines and other online repositories can be mined for stored information.
– Some databases and archives require paid access.
– Others are circumvented, with their data acquired without permission.
– Most non-fiction books, along with a substantial portion of fiction, have been digitized and are freely available, particularly in Eastern Europe and Asia.
– Materials that remain undigitized can be manually scanned, but I am unaware of any AI company actively engaging in large-scale scanning.

At present, AI models are effectively reducing this massive corpus into a compressed form of meaningful, high-quality human knowledge—acting as a synthesized distillation of available intellectual content.

(I mean, ChatGPT is familiar with my work, even if it’s ‘off by a bit’ and I’m a relatively minor figure in philosophy and social science.)

Cheers,
CD

Reply addressees: @ladypharaoh777 @BrianRoemmele

RT @ThruTheHayes: AN AI EXPLORATION OF SUBJECTS IN WHICH ONLY MY PEOPLE DEMONSTRATE MASTERY

As an American white man, with a masculine bra…

RT @PalmerLuckey: DeepSeek is legitimately impressive, but the level of hysteria is an indictment of so many.

The $5M number is bogus. It…

(Doolittle on AI)
Q: Curt: –“How does AI intersect with Metaphor Theory? AI seems very logic based while Metaphor Theory seems to postulate that we humans think in layers and layers of metaphors built up based on our physical experience in the world.”– @patriciamdavis

Metaphor is a category of analogy and analogy is a category of generalization – neurons compete to achieve a coherence of competing networks of generalizations.

AutoAssociation of episodic memory in the hippocampal region seeks analogy for the purpose of identification > prediction > competition > valence > attention (choice).

LLM AI is not logic based (sets), but bayesian probabilism (statistics). Instead, we are having a hell of a problem making it Reason because reason (wayfinding) requires parallel competition and preservation of state. So, Math and Programming ( set logic, procedural logic, finite references and referents) are much much easier than reasoning (episodic logic, infinite referents). And my (our) work requires mathematical decidability, programmatic reasoning, and auto-associative narrating (episodic traversal).

Cheers
CURT

Reply addressees: @patriciamdavis