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Extraction Summary

3
People
3
Organizations
0
Locations
2
Events
1
Relationships
3
Quotes

Document Information

Type: Book excerpt / article page (evidence file)
File Size: 2.35 MB
Summary

This document is page 154 of a book or article discussing the history and mechanics of Artificial Intelligence, specifically focusing on 'Bottom-up Deep Learning' and 'Reinforcement Learning.' It traces the history from B.F. Skinner's work in the 1950s to modern applications by Google's DeepMind (such as AlphaZero). While the document bears a 'HOUSE_OVERSIGHT' Bates stamp, suggesting it was part of a document production (potentially related to Jeffrey Epstein's scientific interests or funding), the text itself is purely scientific/academic and contains no direct reference to Epstein or his associates.

People (3)

Name Role Context
B. F. Skinner Psychologist / Researcher
Mentioned historically regarding reinforcement learning and programming pigeons in the 1950s.
John Watson Psychologist
Mentioned as the predecessor whose work B.F. Skinner built upon.
Author (Unknown) Author/Narrator
Refers to themselves as 'I' in the first paragraph discussing generative models.

Organizations (3)

Name Type Context
Google
Parent company of DeepMind mentioned in context of deep-learning methods.
DeepMind
AI research laboratory mentioned for its deep-learning methods, Atari game playing, and AlphaZero program.
House Oversight Committee
Implied by the Bates stamp 'HOUSE_OVERSIGHT_016957'.

Timeline (2 events)

1950s
B.F. Skinner programmed pigeons to perform elaborate actions including guiding missiles.
Unknown
1980s
Computer scientists devised connectionist/neural-network architecture.
Unknown
Computer Scientists

Relationships (1)

B. F. Skinner Academic/Scientific John Watson
B. F. Skinner, building on the work of John Watson...

Key Quotes (3)

"In computer terms, I started out with a “generative model” that includes abstract concepts like greed and deception and describes the process that produces email scams."
Source
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Quote #1
"Computers are designed to perform simple operations over and over on a scale that dwarfs human imagination, and computational systems can learn remarkably complex skills in this way."
Source
HOUSE_OVERSIGHT_016957.jpg
Quote #2
"Deep learning helped interpret the features on the screen, and reinforcement learning rewarded the system for higher scores."
Source
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Quote #3

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