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Extraction Summary
4
People
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Organizations
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Locations
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Events
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Relationships
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Quotes
Document Information
Type:
Essay / article / publication excerpt
File Size:
Summary
This document is page 152 of a larger file (stamped HOUSE_OVERSIGHT_016372), likely from a collection of scientific essays or a book. It features an essay titled 'AIs VERSUS FOUR-YEAR-OLDS' by developmental psychologist Alison Gopnik. The text argues that despite advances in AI, human children possess a distinct, superior form of learning based on structured, abstract representations rather than just statistical pattern detection. While Jeffrey Epstein is not mentioned on this specific page, the House Oversight stamp suggests this document may be part of evidence regarding his connections to the scientific community (likely the Edge Foundation/Edge.org).
People (4)
| Name | Role | Context |
|---|---|---|
| Alison Gopnik | Author / Developmental Psychologist |
Developmental psychologist at UC Berkeley; author of the essay comparing AI to child learning.
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| Aristotle | Philosopher |
Referenced regarding the 'bottom up' approach to knowledge and learning.
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| Plato | Philosopher |
Referenced regarding basic ways of addressing the problem of knowledge.
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| David Hume | Philosopher |
Referenced as a classic associationist who carried Aristotle's approach further.
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Organizations (2)
| Name | Type | Context |
|---|---|---|
| UC Berkeley |
Academic institution where Alison Gopnik is a developmental psychologist.
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| House Oversight Committee |
Implied by the footer stamp 'HOUSE_OVERSIGHT_016372', indicating this document is part of an investigation file.
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Locations (1)
| Location | Context |
|---|---|
|
Place of employment for Alison Gopnik.
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Relationships (1)
Alison Gopnik is a developmental psychologist at UC Berkeley
Key Quotes (4)
"But the most sophisticated AIs are still far from being able to solve problems that human four-year-olds accomplish with ease."Source
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Quote #1
"In spite of the impressive name, artificial intelligence largely consists of techniques to detect statistical patterns in large data sets."Source
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Quote #2
"Although children are dramatically bad at planning and decision making, they are the best learners in the universe."Source
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Quote #3
"Much of the process of turning data into theories happens before we are five."Source
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Quote #4
Full Extracted Text
Complete text extracted from the document (3,281 characters)
AIs VERSUS FOUR-YEAR-OLDS
Alison Gopnik
Alison Gopnik
Alison Gopnik is a developmental psychologist at UC Berkeley; her books include The Philosophical Baby and, most recently, The Gardener and the Carpenter: What the New Science of Child Development Tells Us About the Relationship Between Parents and Children.
Everyone’s heard about the new advances in artificial intelligence, and especially machine learning. You’ve also heard utopian or apocalyptic predictions about what those advances mean. They have been taken to presage either immortality or the end of the world, and a lot has been written about both those possibilities. But the most sophisticated AIs are still far from being able to solve problems that human four-year-olds accomplish with ease. In spite of the impressive name, artificial intelligence largely consists of techniques to detect statistical patterns in large data sets. There is much more to human learning.
How can we possibly know so much about the world around us? We learn an enormous amount even when we are small children; four-year-olds already know about plants and animals and machines; desires, beliefs, and emotions; even dinosaurs and spaceships.
Science has extended our knowledge about the world to the unimaginably large and the infinitesimally small, to the edge of the universe and the beginning of time. And we use that knowledge to make new classifications and predictions, imagine new possibilities, and make new things happen in the world. But all that reaches any of us from the world is a stream of photons hitting our retinas and disturbances of air at our eardrums. How do we learn so much about the world when the evidence we have is so limited? And how do we do all this with the few pounds of grey goo that sits behind our eyes?
The best answer so far is that our brains perform computations on the concrete, particular, messy data arriving at our senses, and those computations yield accurate representations of the world. The representations seem to be structured, abstract, and hierarchical; they include the perception of three-dimensional objects, the grammars that underlie language, and mental capacities like “theory of mind,” which lets us understand what other people think. Those representations allow us to make a wide range of new predictions and imagine many new possibilities in a distinctively creative human way.
This kind of learning isn’t the only kind of intelligence, but it’s a particularly important one for human beings. And it’s the kind of intelligence that is a specialty of young children. Although children are dramatically bad at planning and decision making, they are the best learners in the universe. Much of the process of turning data into theories happens before we are five.
Since Aristotle and Plato, there have been two basic ways of addressing the problem of how we know what we know, and they are still the main approaches in machine learning. Aristotle approached the problem from the bottom up: Start with senses—the stream of photons and air vibrations (or the pixels or sound samples of a digital image or recording)—and see if you can extract patterns from them. This approach was carried further by such classic associationists as philosophers David Hume
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