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Analysis

China/USA/CyberAI – China’s CyberAI Talent Pipeline (CSET)

by Dakota Cary

CSET writes: To what extent does China’s cultivation of talent in cybersecurity and AI matter in terms of competitiveness with other countries? Right now, it seems to have an edge: China’s 11 World-Class Cybersecurity Schools offer more classes on artificial intelligence and machine learning than do the 20 U.S. universities certified as Centers of Academic Excellence in Cyber Operations. This policy brief recommends tracking 13 research grants from the National Science Foundation that attempt to integrate AI into cybersecurity curricula.

go to CSET: China’s CyberAI Talent Pipeline – Center for Security and Emerging Technology (georgetown.edu)

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Analysis

(Artificial Intelligence) National Power After AI (CSET)

Matthew Daniels and Ben Chang write for CSET: AI technologies will likely alter great power competitions in foundational ways, changing both how nations create power and their motives for wielding it against one another. This paper is a first step toward thinking more expansively about AI & national power and seeking pragmatic insights for long-term U.S. competition with authoritarian governments.

go to CSET website: National Power After AI – Center for Security and Emerging Technology (georgetown.edu)

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Analysis

TechInnovation – Poison in the Well. Securing the Shared Resources of Machine Learning (Andrew Lohn, CSET)

Modern machine learning often relies on open-source datasets, pretrained models, and machine learning libraries from across the internet, but are those resources safe to use? Previously successful digital supply chain attacks against cyber infrastructure suggest the answer may be no. This report introduces policymakers to these emerging threats and provides recommendations for how to secure the machine learning supply chain.

Poison in the Well – Center for Security and Emerging Technology (georgetown.edu)

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Uncategorized

Science/Artificial Intelligence – Creating a Map of Science and Measuring the Role of AI in it (Autumn Toney, CSET)

Data Snapshots are informative descriptions and quick analyses that dig into CSET’s unique data resources. Our first series of Snapshots will introduce CSET’s Map of Science and explore the underlying data and analytic utility of this new tool. Check in every two weeks to see our newest Snapshot, and keep an eye out for the future release of CSET’s Map of Science user interface, which will enable users to interact with the Map directly.

Creating a Map of Science and Measuring the Role of AI in it – Center for Security and Emerging Technology (georgetown.edu)

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Analysis

TechInnovation – PARAT – Tracking the Activity of AI Companies (Rebecca Gelles, Zachary Arnold, Ngor Luong, Jennifer Melot, CSET)

CSET’s Private-sector AI-Related Activity Tracker (PARAT) collects data related to companies’ AI research and development to inform analysis of the global AI sector. The global AI market is already expanding rapidly and is likely to continue growing in the coming years. Identifying “AI companies” helps illustrate the size and health of the AI industry in which they participate as well as the most sought-after skills and experience in the AI workforce.

PARAT – Tracking the Activity of AI Companies – Center for Security and Emerging Technology (georgetown.edu)

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Analysis

USA – U.S. Demand for AI Certifications. Promise or Hype? (Diana Gehlhaus Ines Pancorbo, CSET)

This issue brief explores whether artificial intelligence and AI-related certifications serve as potential pathways to enter the U.S. AI workforce. The authors find that according to U.S. AI occupation job postings data over 2010–2020, there is little demand from employers for AI and AI-related certifications. From this perspective, such certifications appear to present more hype than promise.

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Analysis

TechInnovation/Cybersecurity – Machine Learning and Cybersecurity. Hype and Reality (Micah Musser, Ashton Garriott, CSET)

Cybersecurity operators have increasingly relied on machine learning to address a rising number of threats. But will machine learning give them a decisive advantage or just help them keep pace with attackers? This report explores the history of machine learning in cybersecurity and the potential it has for transforming cyber defense in the near future.

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Analysis

Language Models/Disinformation – Truth, Lies, and Automation. How Language Models Could Change Disinformation (Ben Buchanan (On leave), Andrew Lohn, Micah Musser, Katerina Sedova, CSET)

Truth, Lies, and Automation Report Cover

Growing popular and industry interest in high-performing natural language generation models has led to concerns that such models could be used to generate automated disinformation at scale. This report examines the capabilities of GPT-3–a cutting-edge AI system that writes text–to analyze its potential misuse for disinformation. A model like GPT-3 may be able to help disinformation actors substantially reduce the work necessary to write disinformation while expanding its reach and potentially also its effectiveness.

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Analysis

USA/China – Comparing the United States’ and China’s Leading Roles in the Landscape of Science (Autumn Toney, Melissa Flagg, CSET)

Comparing the United States’ and China’s Leading Roles in the Landscape of Science – Center for Security and Emerging Technology (georgetown.edu)

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