| --- |
| title: "House Oversight: Estate Documents (Nov 12) (HOUSE_OVERSIGHT_016373)" |
| source: "House Oversight: Estate Documents (Nov 12)" |
| sourceUrl: "https://www.justice.gov/epstein" |
| date: "2026-01-01" |
| category: "House Oversight" |
| eftaNumber: "HOUSE_OVERSIGHT_016373" |
| ocrPages: 1 |
| ocrChars: 3546 |
| ocrElapsed: 0.0 |
| 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" |
| --- |
| |
| and J. S. Mill and later by behavioral psychologists, like Pavlov and B. F. Skinner. On |
| this view, the abstractness and hierarchical structure of representations is something of an |
| illusion, or at least an epiphenomenon. All the work can be done by association and |
| pattern detection especially if there are enough data. |
| Over time, there has been a seesaw between this bottom-up approach to the |
| mystery of learning and Plato's alternative, top-down one. Maybe we get abstract |
| knowledge from concrete data because we already know a lot, and especially because we |
| already have an array of basic abstract concepts, thanks to evolution. Like scientists, we |
| can use those concepts to formulate hypotheses about the world. Then, instead of trying |
| to extract patterns from the raw data, we can make predictions about what the data should |
| look like if those hypotheses are right. Along with Plato, such "rationalist" philosophers |
| and psychologists as Descartes and Noam Chomsky took this approach. |
| Here's an everyday example that illustrates the difference between the two |
| methods: solving the spam plague. The data consist of a long unsorted list of messages in |
| your in-box. The reality is that some of these messages are genuine and some are spam. |
| How can you use the data to discriminate between them? |
| Consider the bottom-up technique first. You notice that the spam messages tend |
| to have particular features: a long list of addressees, origins in Nigeria, references to |
| million-dollar prizes or Viagra. The trouble is that perfectly useful messages might have |
| these features, too. If you looked at enough examples of spam and non-spam emails, you |
| might see not only that spam emails tend to have those features but that the features tend |
| to go together in particular ways (Nigeria plus a million dollars spells trouble). In fact, |
| there might be some subtle higher-level correlations that discriminate the spam messages |
| from the useful ones — a particular pattern of misspellings and IP addresses, say. If you |
| detect those patterns, you can filter out the spam. |
| The bottom-up machine-learning techniques do just this. The learner gets |
| millions of examples, each with some set of features and each labeled as spam (or some |
| other category) or not. The computer can extract the pattern of features that distinguishes |
| the two, even if it's quite subtle. |
| How about the top-down approach? I get an email from the editor of the Journal |
| of Clinical Biology. It refers to one of my papers and says that they would like to publish |
| an article by me. No Nigeria, no Viagra, no million dollars; the email doesn't have any |
| of the features of spam. But by using what I already know, and thinking in an abstract |
| way about the process that produces spam, I can figure out that this email is suspicious. |
| (1) I know that spammers try to extract money from people by appealing to |
| human greed |
| (2) I also know that legitimate "open access" journals have started covering their |
| costs by charging authors instead of subscribers, and that I don't practice anything like |
| clinical biology. |
| Put all that together and I can produce a good new hypothesis about where that |
| email came from. It's designed to sucker academics into paying to "publish" an article in |
| a fake journal. The email was a result of the same dubious process as the other spam |
| emails, even though it looked nothing like them. I can draw this conclusion from just one |
| example, and I can go on to test my hypothesis further, beyond anything in the email |
| itself, by googling the "editor." |
| 153 |
| HOUSE_OVERSIGHT_016373 |
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