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Inference: International Review of Science
document IMAGES-006-HOUSE_OVERSIGHT_022455.txt House Oversight Committee — Epstein Estate Records (Nov 2025)

Inference: International Review of Science Feedback & Comments Wolf-Ekkehard Lénnig Former research scientist at the Max Planck Institute for Plant Breeding Research in Cologne, Germany. “Thank you very much for your informative mail and the print version of Inference. I like...

Eye on the Market | March 15, 2012 J.P Morgan
document IMAGES-007-HOUSE_OVERSIGHT_024134.txt Mar 15, 2012 House Oversight Committee — Epstein Estate Records (Nov 2025)

Eye on the Market | March 15, 2012 J.P Morgan Topics: Is US data as good as it looks? Is Chinese data as bad as it looks? Is European data as bizarre as it looks? adequacy rules; created a program through which municipalities can issue bonds with government guarantees (rather t...

The Chinese government organizes and pays for exchanges in which participants travel
document IMAGES-006-HOUSE_OVERSIGHT_020583.txt House Oversight Committee — Epstein Estate Records (Nov 2025)

124 Exchanges The Chinese government organizes and pays for exchanges in which participants travel from the United States, divulge technical knowledge through scripted venues, are briefed on China’s technology interests, return to their US base to collect more information, and...

Charity Tillemann Dick is an American-born soprano and
document IMAGES-004-HOUSE_OVERSIGHT_017560.txt House Oversight Committee — Epstein Estate Records (Nov 2025)

CHARITY TILLEMANN DICK Charity Tillemann Dick is an American-born soprano and a recipient of two double lung transplants. Charity has performed across the United States, Europe, and Asia in venues as diverse as the Rose Theater at Lincoln Center in New York City; The Kennedy C...

learning system can be trained to follow strategies that produce those outcomes. Wiener
document IMAGES-003-HOUSE_OVERSIGHT_016313.txt House Oversight Committee — Epstein Estate Records (Nov 2025)

…utcomes. Wiener hinted at this idea in the 1950s, but the intervening decades have developed it into a fine art. Modern machine-learning systems can find extremely effective strategies for playing computer games—from simple arcade games to complex real-time strategy games—by appl...

learning system can be trained to follow strategies that produce those outcomes. Wiener
document IMAGES-004-HOUSE_OVERSIGHT_016896.txt House Oversight Committee — Epstein Estate Records (Nov 2025)

…utcomes. Wiener hinted at this idea in the 1950s, but the intervening decades have developed it into a fine art. Modern machine-learning systems can find extremely effective strategies for playing computer games—from simple arcade games to complex real-time strategy games—by appl...

4.3 Emergentist Cognitive Architectures 67
document IMAGES-002-HOUSE_OVERSIGHT_012983.txt House Oversight Committee — Epstein Estate Records (Nov 2025)

…; and one key aspect of sensory data coming from our physical environments is its hierarchical structure. However, most machine learning and pattern recognition systems are “shallow” in structure, not explicitly incorporating the hierarchical structure of the world in their archi...

You can show this in children’s everyday learning, too. Young children rapidly
document IMAGES-003-HOUSE_OVERSIGHT_016377.txt House Oversight Committee — Epstein Estate Records (Nov 2025)

…biology, physics, and psychology in much the way adult scientists do, even with relatively little data. The remarkable machine-learning accomplishments of the recent AI systems, both bottom-up and top-down, take place in a narrow and well-defined space of hypotheses and concepts...

Origins February 24 — 26, 2017
document IMAGES-001-HOUSE_OVERSIGHT_011298.txt House Oversight Committee — Epstein Estate Records (Nov 2025)

…ng package developed in-house by the ML research teams. e The inputs for the system are current loads on the different machines, the incoming tasks queue (including priority for different tasks), and historical data on task runtimes. The output is an assignment of tasks to machi...

Esa Origins 7 February 24 — 26, 2017
document IMAGES-001-HOUSE_OVERSIGHT_011297.txt House Oversight Committee — Epstein Estate Records (Nov 2025)

…de, incentives for securing loT systems are low, key vulnerabilities are stockpiled rather than globally patched. Using machine learning the techniques for vulnerability detection are increasingly sophisticated but opaque. At some point adaptive cyber defense/offense systems bec...

Quantitative Analysis of Culture Using Millions of Digitized Books
document IMAGES-004-HOUSE_OVERSIGHT_016996.txt House Oversight Committee — Epstein Estate Records (Nov 2025)

Research Article Quantitative Analysis of Culture Using Millions of Digitized Books Jean-Baptiste Michel,?***+ Yuan Kui Shen,° Aviva Presser Aiden,° Adrian Veres,’ Matthew K. Gray,* The Google Books Team,® Joseph P. Pickett,’ Dale Hoiberg,’” Dan Clancy,® Peter Norvig,® Jon Orwa...

While USA Inc. Ranks # 1 in Defense Spending
document IMAGES-006-HOUSE_OVERSIGHT_020875.txt House Oversight Committee — Epstein Estate Records (Nov 2025)

While USA Inc. Ranks # 1 in Defense Spending... Top 25 Countries by 2009 Defense Spending, 2009 GIG = ee eee es cee ses es =r Sos en os Ss tS 3 Sm 5 eh — SA = Ho es = Ho REGS 5 mmm ome 2 aes cence = eae ca ses sn = ot es enn = Se en ES 6 9 UES 3 ND Ee = 9 HD = ED = HH BBE = ae...

prep for dinner, israel pres briefing /
email TEXT-001-HOUSE_OVERSIGHT_028728.txt Feb 8, 2013 House Oversight Committee — Epstein Estate Records (Nov 2025)

From: Jeffrey Epstein [[email protected]] Sent: 2/11/2013 5:45:09 PM To: Larry Summers_______________________ Subject: prep for dinner, israel pres briefing / The Wall Street Journal The Ayatollah Always Says No Editorial February 8, 2013 --The Farsi word for "no" is na h, wh...

While USA Inc. Ranks # 2 in Number of Troops
document IMAGES-006-HOUSE_OVERSIGHT_020876.txt House Oversight Committee — Epstein Estate Records (Nov 2025)

While USA Inc. Ranks # 2 in Number of Troops... Top 20 Countries by Active Number of Troops, 2008 BEBII me me o m= are se no we nae mo tw = 8 = em Ne He SM 8 A Aa = BEI meme om = ere ee owe nae eo tw = a em ne He HS mM Bm — oO 3 Sy 1500 a ° £ = a BI awn n-ne enna eR SOR ONT TN...