| --- |
| title: "House Oversight: Estate Documents (Nov 12) (HOUSE_OVERSIGHT_016336)" |
| source: "House Oversight: Estate Documents (Nov 12)" |
| sourceUrl: "https://www.justice.gov/epstein" |
| date: "2026-01-01" |
| category: "House Oversight" |
| eftaNumber: "HOUSE_OVERSIGHT_016336" |
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| ocrChars: 3874 |
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| parseTier: "external-legacy" |
| engine: "engine undisclosed (ep-nov-12.greg.technology mirror)" |
| externalSource: "greg-ep-nov-12" |
| externalLicense: "not granted" |
| externalCredit: "ep-nov-12.greg.technology" |
| externalUrl: "https://ep-nov-12.greg.technology" |
| --- |
| |
| something much more significant than speculating at the time: He was laying the |
| foundations for the digital revolution. As a graduate student at MIT, he worked for |
| Vannevar Bush on the Differential Analyzer. This was one of the last great analog |
| computers, a room full of gears and shafts. Shannon's frustration with the difficulty of |
| solving problems this way led him in 1937 to write what might be the best master's thesis |
| ever. In it, he showed how electrical circuits could be designed to evaluate arbitrary |
| logical expressions, introducing the basis for universal digital logic. |
| After MIT, Shannon studied communications at Bell Labs. Analog telephone |
| calls degraded with distance; the farther they traveled, the worse they sounded. Rather |
| than continue to improve them incrementally, Shannon showed in 1948 that by |
| communicating with symbols rather than continuous quantities, the behavior is very |
| different. Converting speech waveforms to the binary values of 1 and 0 is an example, |
| but many other sets of symbols can be (and are) used in digital communications. What |
| matters is not the particular symbols but rather the ability to detect and correct errors. |
| Shannon found that if the noise is above a threshold (which depends on the system |
| design), then there are certain to be errors. But if the noise is below a threshold, then a |
| linear increase in the physical resources representing the symbol results in an exponential |
| decrease in the likelihood of making an error in correctly receiving the symbol. This |
| relationship was the first of what we'd now call a threshold theorem. |
| Such scaling falls off so quickly that the probability of an error can be so small as |
| to effectively never happen. Each symbol sent multiplies rather than adds to the |
| certainty, so that the probability of a mistake can go from 0.1 to 0.01 to 0.001, and so |
| forth. This exponential decrease in communication errors made possible an exponential |
| increase in the capacity of communication networks. And that eventually solved the |
| problem of where the knowledge in an Al system came from. |
| For many years, the fastest way to speed up a computation was to do nothing— |
| just wait for computers to get faster. In the same way, there were years of Al projects |
| that aimed to accumulate everyday knowledge by laboriously entering pieces of |
| information. That didn't scale; it could progress only as fast as the number of people |
| doing the entering. But when phone calls, newspaper stories, and mail messages all |
| moved onto the Internet, everyone doing any of those things became a data generator. |
| The result was an exponential rather than a linear rate of knowledge accumulation. |
| John von Neumann also has a cameo in The Human Use of Human Beings, for |
| game theory. What Wiener missed here was von Neumann's seminal role in digitizing |
| computation. Whereas analog communication degraded with distance, analog computing |
| (like the Differential Analyzer) degraded with time, accumulating errors as it progressed. |
| Von Neumann presented in 1952 a result corresponding to Shannon's for computation |
| (they had met at the Institute for Advanced Study, in Princeton), showing that it was |
| possible to compute reliably with an unreliable computing device by using symbols rather |
| than continuous quantities. This was, again, a scaling argument, with a linear increase in |
| the physical resources representing the symbol resulting in an exponential reduction in |
| the error rate as long as the noise was below a threshold. That's what makes it possible |
| to have a billion transistors in a computer chip, with the last one as useful as the first one. |
| This relationship led to an exponential increase in computing performance, which solved |
| a second problem in AI: how to process exponentially increasing amounts of data. |
| The third problem that scaling solved for Al was coming up with the rules for |
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| HOUSE_OVERSIGHT_016336 |
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