Report - Belfer Center for Science and International Affairs, Harvard Kennedy School

Technology Primer: Artificial Intelligence & Machine Learning

  • Nicolas Christin
  • Susan Ritchie
  • Daniel Zhang
| Apr. 06, 2023

Executive Summary

Artificial Intelligence (AI), can be defined as the theory and application of machines—especially computer programs—to perform tasks that typically require human intelligence, such as image captioning and generation, speech recognition and synthesis, natural language understanding and production, tool assembly and utilization, as well as various other perception-action based engagements. AI, in its current technological state, is being applied in various industries and domains, such as online advertising, financial trading, healthcare, pharmaceutical, and robotics. The lucrative market opportunities offered by AI applications have attracted investments from tech giants like Alphabet, Apple, Meta, Amazon, and Microsoft, as well as research universities and startups.

Machine Learning (ML), commonly categorized as a subfield of AI, is a field of study concerning the automatic discovery of historical patterns in data using statistical algorithms. ML’s driving principle is that historical patterns are likely to reappear in the future. The discovered historical patterns can therefore be leveraged to make accurate predictions on data that has not been seen before. Once an algorithm is trained, it can be applied to new, larger streams of data. ML is already an integral component of many deployed commercial applications, such as content generation (e.g., text, image, audio, video generation), virtual assistants, social media feed ranking, content recommendation systems, financial market prediction, and healthcare screening and diagnostic tools, as well as administrative applications. In addition, ML is foundational in various other emerging technologies, such as autonomous vehicles and next-generation cybersecurity.

Currently, United States policy with regards to AI often derives from interpretations of various pre-existing legislations and legal precedents. However, with the increased awareness of AI-related risks (e.g., bias, accountability, misuse, etc.), and the potential size of their impact, over the last decade, the number of proposed bills containing AI provisions significantly increased at both the state and federal levels (i.e., from two bills in 2012 to 131 in 2021), with 2% of them becoming law at the federal level and 20% of them becoming law at the state level. Similarly, policies and regulatory frameworks are being crafted to guide the development and application of AI in other continents too, with Europe and Asia leading the process. Acknowledging the potential impact of this technology on human life and societal dynamics, there is a pressing need for U.S. legislators and policymakers to remain engaged in the ethical and practical development of artificial intelligence.


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For more information on this publication: Belfer Communications Office
For Academic Citation: Eddine Abail, Issam, Gopal Nadadur, Enrico Santus, Ariel Higuchi and Amritha Jayanti. “Technology Primer: Artificial Intelligence & Machine Learning.” Edited by Nicolas Christin, Susan Ritchie and Daniel Zhang. Belfer Center for Science and International Affairs, Harvard Kennedy School, April 6, 2023.

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