Tag Archive: Artificial intelligence

CSET Data Research Assistant Simon Rodriguez joins this episode of The Data Exchange to discuss how research in machine learning and AI affects public consciousness.

Mapping India’s AI Potential

Husanjot Chahal, Sara Abdulla, Jonathan Murdick, and Ilya Rahkovsky
| March 2021

With its massive information technology workforce, thriving research community and a growing technology ecosystem, India has a significant stake in the development of artificial intelligence globally. Drawing from a variety of original CSET datasets, the authors evaluate India’s potential for AI by examining its progress across five categories of indicators pertinent to AI development: talent, research, patents, companies and investments, and compute.

Key Concepts in AI Safety: Interpretability in Machine Learning

Tim G. J. Rudner and Helen Toner
| March 2021

This paper is the third installment in a series on “AI safety,” an area of machine learning research that aims to identify causes of unintended behavior in machine learning systems and develop tools to ensure these systems work safely and reliably. The first paper in the series, “Key Concepts in AI Safety: An Overview,” described three categories of AI safety issues: problems of robustness, assurance, and specification. This paper introduces interpretability as a means to enable assurance in modern machine learning systems.

Key Concepts in AI Safety: Robustness and Adversarial Examples

Tim G. J. Rudner and Helen Toner
| March 2021

This paper is the second installment in a series on “AI safety,” an area of machine learning research that aims to identify causes of unintended behavior in machine learning systems and develop tools to ensure these systems work safely and reliably. The first paper in the series, “Key Concepts in AI Safety: An Overview,” described three categories of AI safety issues: problems of robustness, assurance, and specification. This paper introduces adversarial examples, a major challenge to robustness in modern machine learning systems.

Key Concepts in AI Safety: An Overview

Tim G. J. Rudner and Helen Toner
| March 2021

This paper is the first installment in a series on “AI safety,” an area of machine learning research that aims to identify causes of unintended behavior in machine learning systems and develop tools to ensure these systems work safely and reliably. In it, the authors introduce three categories of AI safety issues: problems of robustness, assurance, and specification. Other papers in this series elaborate on these and further key concepts.

Jack Corrigan is a Senior Research Analyst focusing on the U.S. innovation ecosystem and national competitiveness.

Chinese Government Guidance Funds

Ngor Luong, Zachary Arnold, and Ben Murphy
| March 2021

The Chinese government is pouring money into public-private investment funds, known as guidance funds, to advance China’s strategic and emerging technologies, including artificial intelligence. These funds are mobilizing massive amounts of capital from public and private sources—prompting both concern and skepticism among outside observers. This overview presents essential findings from our full-length report on these funds, analyzing the guidance fund model, its intended benefits and weaknesses, and its long-term prospects for success.

Understanding Chinese Government Guidance Funds

Ngor Luong, Zachary Arnold, and Ben Murphy
| March 2021

China’s government is using public-private investment funds, known as guidance funds, to deploy massive amounts of capital in support of strategic and emerging technologies, including artificial intelligence. Drawing exclusively on Chinese-language sources, this report explores how guidance funds raise and deploy capital, manage their investment, and interact with public and private actors. The guidance fund model is no silver bullet, but it has many advantages over traditional industrial policy mechanisms.

AI Verification

Matthew Mittelsteadt
| February 2021

The rapid integration of artificial intelligence into military systems raises critical questions of ethics, design and safety. While many states and organizations have called for some form of “AI arms control,” few have discussed the technical details of verifying countries’ compliance with these regulations. This brief offers a starting point, defining the goals of “AI verification” and proposing several mechanisms to support arms inspections and continuous verification.

Using Machine Learning to Fill Gaps in Chinese AI Market Data

Zachary Arnold, Joanne Boisson, Lorenzo Bongiovanni, Daniel Chou, Carrie Peelman, and Ilya Rahkovsky
| February 2021

In this proof-of-concept project, CSET and Amplyfi Ltd. used machine learning models and Chinese-language web data to identify Chinese companies active in artificial intelligence. Most of these companies were not labeled or described as AI-related in two high-quality commercial datasets. The authors' findings show that using structured data alone—even from the best providers—will yield an incomplete picture of the Chinese AI landscape.