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- 2024
- Book
The Ritual Effect: From Habit to Ritual, Harness the Surprising Power of Everyday Actions
By: Michael Norton
Our lives are filled with repetitive tasks meant to keep us on track—what we come to know as habits. Over time, these routines (for example, brushing your teeth or putting on your right sock first) tend to be performed automatically. But when we’re more mindful about...
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Norton, Michael. The Ritual Effect: From Habit to Ritual, Harness the Surprising Power of Everyday Actions. New York: Scribner, 2024.
- February 2024 (Revised February 2024)
- Teaching Note
TimeCredit
Teaching Note for HBS Case No. 824-139. TimeCredit is an artificial intelligence (AI) startup that is developing large language models (LLMs) to generate accounting memos. The case follows Ndonga Sagnia, a Gambian Harvard Business School MBA student with an accounting...
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- February 2024
- Case
TimeCredit
TimeCredit is an artificial intelligence (AI) startup that is developing large language models (LLMs) to generate accounting memos. The case follows Ndonga Sagnia, a Gambian Harvard Business School MBA student with an accounting background, as she decides how much...
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Colonnelli, Emanuele, Raymond Kluender, and Shai Benjamin Bernstein. "TimeCredit." Harvard Business School Case 824-139, February 2024.
- February 2024
- Case
More than Optics: Olympus's Vision to Become a Leading Global MedTech Company
By: David J. Collis and Haisley Wert
In August 2022, CEO Yasuo Takeuchi reflected on Olympus Corporation’s recent transformation from being known as a Japanese consumer camera company to becoming a leading global medical technology (MedTech) company. Over the past dozen years, Takeuchi and prior...
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Keywords:
Global Human Resource Management;
Medical Technology;
Corporate Strategy;
Transformation;
Globalization;
Business Model;
Leading Change;
Organizational Structure;
Organizational Change and Adaptation;
Medical Devices and Supplies Industry;
Japan;
United States
Collis, David J., and Haisley Wert. "More than Optics: Olympus's Vision to Become a Leading Global MedTech Company." Harvard Business School Case 724-426, February 2024.
- 2024
- Working Paper
Age at Immigrant Arrival and Career Mobility: Evidence from Vietnamese Refugee Migration and the Amerasian Homecoming Act
By: Sari Pekkala Kerr, William R. Kerr and Kendall Smith
We study the long-run career mobility of young immigrants, mostly refugees, from Vietnam who moved to the United States during 1989-1995. This third and final migration wave of young Vietnamese immigrants was sparked by unexpected events that culminated in the...
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Keywords:
Vietnam;
Vietnam War;
Assimilation;
Immigration;
Refugees;
Age;
Outcome or Result;
Personal Development and Career;
Viet Nam
Kerr, Sari Pekkala, William R. Kerr, and Kendall Smith. "Age at Immigrant Arrival and Career Mobility: Evidence from Vietnamese Refugee Migration and the Amerasian Homecoming Act." Harvard Business School Working Paper, No. 24-044, January 2024.
- 2024
- Working Paper
The Value of Open Source Software
By: Manuel Hoffmann, Frank Nagle and Yanuo Zhou
The value of a non-pecuniary (free) product is inherently difficult to assess. A pervasive
example is open source software (OSS), a global public good that plays a vital role in the economy
and is foundational for most technology we use today. However, it is...
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Hoffmann, Manuel, Frank Nagle, and Yanuo Zhou. "The Value of Open Source Software." Harvard Business School Working Paper, No. 24-038, January 2024.
- 2024
- Conference Paper
Quantifying Uncertainty in Natural Language Explanations of Large Language Models
By: Himabindu Lakkaraju, Sree Harsha Tanneru and Chirag Agarwal
Large Language Models (LLMs) are increasingly used as powerful tools for several
high-stakes natural language processing (NLP) applications. Recent prompting
works claim to elicit intermediate reasoning steps and key tokens that serve as
proxy explanations for LLM...
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Lakkaraju, Himabindu, Sree Harsha Tanneru, and Chirag Agarwal. "Quantifying Uncertainty in Natural Language Explanations of Large Language Models." Paper presented at the Society for Artificial Intelligence and Statistics, 2024.
- 2023
- Article
Benchmarking Large Language Models on CMExam—A Comprehensive Chinese Medical Exam Dataset
By: Junling Liu, Peilin Zhou, Yining Hua, Dading Chong, Zhongyu Tian, Andrew Liu, Helin Wang, Chenyu You, Zhenhua Guo, Lei Zhu and Michael Lingzhi Li
Recent advancements in large language models (LLMs) have transformed the field of question answering (QA). However, evaluating LLMs in the medical field is challenging due to the lack of standardized and comprehensive datasets. To address this gap, we introduce CMExam,...
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Keywords:
Large Language Model;
AI and Machine Learning;
Analytics and Data Science;
Health Industry
Liu, Junling, Peilin Zhou, Yining Hua, Dading Chong, Zhongyu Tian, Andrew Liu, Helin Wang, Chenyu You, Zhenhua Guo, Lei Zhu, and Michael Lingzhi Li. "Benchmarking Large Language Models on CMExam—A Comprehensive Chinese Medical Exam Dataset." Conference on Neural Information Processing Systems (NeurIPS), Datasets and Benchmarks Track 36 (2023).
- November 2023
- Article
Knowledge About the Source of Emotion Predicts Emotion-Regulation Attempts, Strategies, and Perceived Emotion-Regulation Success
By: Yael Millgram, Matthew K. Nock, David D. Bailey and Amit Goldenberg
People’s ability to regulate emotions is crucial to healthy emotional functioning. One overlooked aspect in emotion-regulation research is that knowledge about the source of emotions can vary across situations and individuals, which could impact people’s ability to...
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Millgram, Yael, Matthew K. Nock, David D. Bailey, and Amit Goldenberg. "Knowledge About the Source of Emotion Predicts Emotion-Regulation Attempts, Strategies, and Perceived Emotion-Regulation Success." Psychological Science 34, no. 11 (November 2023): 1244–1255.
- 2023
- Article
MoPe: Model Perturbation-based Privacy Attacks on Language Models
By: Marvin Li, Jason Wang, Jeffrey Wang and Seth Neel
Recent work has shown that Large Language Models (LLMs) can unintentionally leak sensitive information present in their training data. In this paper, we present Model Perturbations (MoPe), a new method to identify with high confidence if a given text is in the training...
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Li, Marvin, Jason Wang, Jeffrey Wang, and Seth Neel. "MoPe: Model Perturbation-based Privacy Attacks on Language Models." Proceedings of the Conference on Empirical Methods in Natural Language Processing (2023): 13647–13660.
- 2023
- Article
Post Hoc Explanations of Language Models Can Improve Language Models
By: Satyapriya Krishna, Jiaqi Ma, Dylan Slack, Asma Ghandeharioun, Sameer Singh and Himabindu Lakkaraju
Large Language Models (LLMs) have demonstrated remarkable capabilities in performing complex tasks. Moreover, recent research has shown that incorporating human-annotated rationales (e.g., Chain-of-Thought prompting) during in-context learning can significantly enhance...
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Krishna, Satyapriya, Jiaqi Ma, Dylan Slack, Asma Ghandeharioun, Sameer Singh, and Himabindu Lakkaraju. "Post Hoc Explanations of Language Models Can Improve Language Models." Advances in Neural Information Processing Systems (NeurIPS) (2023).
- 2023
- Other Article
The Harvard USPTO Patent Dataset: A Large-Scale, Well-Structured, and Multi-Purpose Corpus of Patent Applications
By: Mirac Suzgun, Luke Melas-Kyriazi, Suproteem K. Sarkar, Scott Duke Kominers and Stuart Shieber
Innovation is a major driver of economic and social development, and information about many kinds of innovation is embedded in semi-structured data from patents and patent applications. Though the impact and novelty of innovations expressed in patent data are difficult...
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Keywords:
USPTO;
Natural Language Processing;
Classification;
Summarization;
Patent Novelty;
Patent Trolls;
Patent Enforceability;
Patents;
Innovation and Invention;
Intellectual Property;
AI and Machine Learning;
Analytics and Data Science
Suzgun, Mirac, Luke Melas-Kyriazi, Suproteem K. Sarkar, Scott Duke Kominers, and Stuart Shieber. "The Harvard USPTO Patent Dataset: A Large-Scale, Well-Structured, and Multi-Purpose Corpus of Patent Applications." Conference on Neural Information Processing Systems (NeurIPS), Datasets and Benchmarks Track 36 (2023).
- 2023
- Working Paper
Toward a Better Understanding of Open Ecosystems: Implications for Policymakers
By: Feng Zhu and Carmelo Cennamo
The digital realm is undergoing a significant transformation, marked by the emergence of platform
business models and the concept of open ecosystems. This paper delves into the intricate nature of
ecosystem openness, underscoring the point that the openness of...
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Zhu, Feng, and Carmelo Cennamo. "Toward a Better Understanding of Open Ecosystems: Implications for Policymakers." Working Paper, November 2023.
- October 2023
- Teaching Note
Timnit Gebru: 'SILENCED No More' on AI Bias and The Harms of Large Language Models
By: Tsedal Neeley and Tim Englehart
Teaching Note for HBS Case No. 422-085. Dr. Timnit Gebru—a leading artificial intelligence (AI) computer scientist and co-lead of Google’s Ethical AI team—was messaging with one of her colleagues when she saw the words: “Did you resign?? Megan sent an email saying that...
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- October 2023
- Case
Fixie and Conversational AI Sidekicks
By: Jeffrey J. Bussgang and Carin-Isabel Knoop
In March 2023, Fixie Co-Founder and Chief Architect Matt Welsh and co-founders had the kind of meeting no founders want to have. The president of leading artificial intelligence (AI) research and deployment firm OpenAI, which had catapulted into fame with its ChatGPT...
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Keywords:
Large Language Model;
Entrepreneurship;
Decision Choices and Conditions;
AI and Machine Learning;
Technological Innovation;
Competitive Strategy;
Technology Industry;
United States
Bussgang, Jeffrey J., and Carin-Isabel Knoop. "Fixie and Conversational AI Sidekicks." Harvard Business School Case 824-037, October 2023.
- 2023
- Working Paper
Emotion Regulation Contagion
By: Michael Pinus, Eran Halperin, Yajun Cao, Alin Coman, James Gross and Amit Goldenberg
In intergroup conflicts, emotion regulation interventions can decrease negative intergroup emotions and increase support for concessions. However, it is usually infeasible to provide emotion regulation interventions to everyone in a population of interest. This raises...
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Pinus, Michael, Eran Halperin, Yajun Cao, Alin Coman, James Gross, and Amit Goldenberg. "Emotion Regulation Contagion." Working Paper, October 2023. (OSF Preprint.)
- 2023
- Working Paper
In-Context Unlearning: Language Models as Few Shot Unlearners
By: Martin Pawelczyk, Seth Neel and Himabindu Lakkaraju
Machine unlearning, the study of efficiently removing the impact of specific training points on the
trained model, has garnered increased attention of late, driven by the need to comply with privacy
regulations like the Right to be Forgotten. Although unlearning is...
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Pawelczyk, Martin, Seth Neel, and Himabindu Lakkaraju. "In-Context Unlearning: Language Models as Few Shot Unlearners." Working Paper, October 2023.
- 2023
- Working Paper
Navigating the Jagged Technological Frontier: Field Experimental Evidence of the Effects of AI on Knowledge Worker Productivity and Quality
By: Fabrizio Dell'Acqua, Edward McFowland III, Ethan Mollick, Hila Lifshitz-Assaf, Katherine C. Kellogg, Saran Rajendran, Lisa Krayer, François Candelon and Karim R. Lakhani
The public release of Large Language Models (LLMs) has sparked tremendous interest in how humans will use Artificial Intelligence (AI) to accomplish a variety of tasks. In our study conducted with Boston Consulting Group, a global management consulting firm, we examine...
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Keywords:
Large Language Model;
AI and Machine Learning;
Performance Efficiency;
Performance Improvement
Dell'Acqua, Fabrizio, Edward McFowland III, Ethan Mollick, Hila Lifshitz-Assaf, Katherine C. Kellogg, Saran Rajendran, Lisa Krayer, François Candelon, and Karim R. Lakhani. "Navigating the Jagged Technological Frontier: Field Experimental Evidence of the Effects of AI on Knowledge Worker Productivity and Quality." Harvard Business School Working Paper, No. 24-013, September 2023.
- September 2023 (Revised January 2024)
- Case
AI21 Labs in 2023: Strategy for Generative AI
By: David Yoffie, Orna Dan and Elena Corsi
Israeli generative artificial intelligence company AI21 Labs was founded in 2017 to realize the vision of true machine intelligence. It sought to reinvent writing and reading and in 2020 it launched Wordtune, an app using GenAI software to offer alternate text...
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Keywords:
Decision Making;
AI and Machine Learning;
Innovation Strategy;
Growth and Development Strategy;
Applications and Software;
Competitive Strategy;
Technology Industry;
Israel
Yoffie, David, Orna Dan, and Elena Corsi. "AI21 Labs in 2023: Strategy for Generative AI." Harvard Business School Case 724-383, September 2023. (Revised January 2024.)
- 2024
- Working Paper
Generative AI and Creative Problem Solving
The rapid advances in generative artificial intelligence (AI) open up attractive opportunities for creative
problem-solving through human-guided AI partnerships. To explore this potential, we initiated a
crowdsourcing challenge focused on sustainable, circular...
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Boussioux, Léonard, Jacqueline N. Lane, Miaomiao Zhang, Vladimir Jacimovic, and Karim R. Lakhani. "Generative AI and Creative Problem Solving." Harvard Business School Working Paper, No. 24-005, July 2023. (Revised March 2024.)