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- May 2024
- Teaching Note
AI21 Labs in 2023: Strategy for Generative AI
By: David Yoffie
Tel Aviv-based generative artificial intelligence company AI21 Labs considers how to build a competitive advantage versus the biggest players in the technology and AI arena.
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- March 2024
- Case
Amperity: First-Party Data at a Crossroads
By: Elie Ofek, Hema Yoganarasimhan and Alexis Lefort
In the summer of 2023, Amperity management was facing a critical decision on its future direction. Given the dramatic changes occurring within the digital advertising ecosystem, as concerns over consumer privacy placed limits on the ability to engage in third-party...
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- March 2024
- Simulation
'Storrowed'
By: Mitchell Weiss
The game was built to accompany "Storrowed": A Generative AI Exercise, available through Harvard Business Publishing. The game adds a timing element to "Storrowed" and enables the teacher to reward teams for strong prompts or penalize teams for believing AI...
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Keywords:
AI and Machine Learning
- March 2024
- Teaching Note
'Storrowed': A Generative AI Exercise
By: Mitchell Weiss
Teaching Note for HBS Exercise No. 824-188. “Storrowed” is an exercise to help participants raise their proficiency with generative AI. It begins by highlighting a problem: trucks getting wedged underneath bridges in Boston, Massachusetts on the city’s Storrow Drive....
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- March 2024
- Exercise
'Storrowed': A Generative AI Exercise
By: Mitchell Weiss
"Storrowed" is an exercise to help participants raise their capacity and curiosity for generative AI. It focuses on generative AI for problem understanding and ideation, but can be adapted for use more broadly. Participants use generative AI tools to understand a...
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Keywords:
AI and Machine Learning
Weiss, Mitchell. "'Storrowed': A Generative AI Exercise." Harvard Business School Exercise 824-188, March 2024.
- March 2024
- Teaching Note
CoPilot(s): Generative AI at Microsoft and GitHub
By: Frank Nagle and Maria P. Roche
This teaching note is the companion to case N9-624-010 CoPilot(s): Generative AI at Microsoft and GitHub, which takes place in late 2021. The case briefly describes the history of both GitHub and Microsoft with a particular focus on open source software (OSS) –...
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- March 7, 2024
- Article
Integrating Digital Tools into Every Stage of Your Sales Strategy
By: Frank V. Cespedes and Georg Krentzel
In their growth and customer-acquisition activities, most companies now face twin challenges: understanding and responding to omni-channel buying behavior and doing that without inadvertently decreasing sales productivity. Thirty years ago, Peter Drucker noted that...
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Keywords:
Sales Management;
Digital Tools;
Sales;
Marketing Channels;
Technology Adoption;
Brands and Branding
Cespedes, Frank V., and Georg Krentzel. "Integrating Digital Tools into Every Stage of Your Sales Strategy." Harvard Business Review (website) (March 7, 2024).
- March 2024
- Case
Governing OpenAI
By: Lynn S. Paine, Suraj Srinivasan and Will Hurwitz
In late November 2023, OpenAI’s new board of directors took stock of the situation. The company, which sought to develop artificial general intelligence (AGI)—computer systems with capabilities exceeding human abilities—was looking to regain its footing after a chaotic...
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Keywords:
Artificial Intelligence;
Board Of Directors;
Board Decisions;
Board Dynamics;
Business Ethics;
Corporate Boards;
Governance Changes;
Governance Structure;
Leadership Change;
Legal Aspects Of Business;
Nonprofit;
Nonprofit Governance;
Open Source;
Partnerships;
Regulation;
Strategy And Execution;
Technological Change;
AI and Machine Learning;
Corporate Governance;
Leadership;
Management;
Mission and Purpose;
Technological Innovation;
Technology Industry;
San Francisco;
United States
- February 2024
- Case
Taffi: Entrepreneurship in Saudi Arabia
By: Paul A. Gompers and Fares Khrais
Taffi was a tech-enabled fashion styling startup founded by Shahad Geoffrey in Saudi Arabia in 2020. Within three years of operating, Geoferry had pivoted the business multiple times. In 2023, Geoferry was attempting the business’s most ambitious pivot yet, shifting...
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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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Keywords:
Accounting;
Business Startups;
Entrepreneurship;
Financing and Loans;
AI and Machine Learning;
Technology Industry
Colonnelli, Emanuele, Raymond Kluender, and Shai Benjamin Bernstein. "TimeCredit." Harvard Business School Case 824-139, February 2024.
- February 2024
- Case
SundaySky: Changing Customer Experiences through Personalized Video
By: David C. Edelman and James Barnett
In June 2023, SundaySky CEO Jim Dicso considers growth strategies. The software-as-a-service company provided software to create advertising videos, customer service videos, and other videos, like employee training modules, and had begun to pilot a new generative...
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- January 2024
- Background Note
Note on Generative AI for Business Students
By: Andrew Rashbass, Ramon Casadesus-Masanell and Jordan Mitchell
Rashbass, Andrew, Ramon Casadesus-Masanell, and Jordan Mitchell. "Note on Generative AI for Business Students." Harvard Business School Background Note 724-411, January 2024.
- January 2024
- Case
The Financial Times (FT) and Generative AI
By: Andrew Rashbass, Ramon Casadesus-Masanell and Jordan Mitchell
- 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.
- December 2023 (Revised February 2024)
- Case
Generative AI and the Future of Work
By: Christopher Stanton and Matt Higgins
Generative AI seemed poised to reshape the world of work, including the higher-wage, white-collar jobs typically pursued by MBA graduates. Informed by the latest research, this case explores generative AI's potential impacts on work, productivity, value creation, and...
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Keywords:
AI;
Future Of Work;
Labor Market;
AI and Machine Learning;
Labor;
Value Creation;
Performance Productivity;
Technology Industry;
United States
Stanton, Christopher, and Matt Higgins. "Generative AI and the Future of Work." Harvard Business School Case 824-130, December 2023. (Revised February 2024.)
- December 2023
- Case
Monsters in the Machine? Tackling the Challenge of Responsible AI
By: Paul M. Healy and Debora L. Spar
In November of 2022, the small tech company OpenAI released ChatGPT, an artificial intelligence chatbot which quickly captured the public’s imagination—becoming the world’s fastest-growing consumer application within months of its release. Though observers from across...
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Keywords:
Technological Innovation;
AI and Machine Learning;
Ethics;
Governing Rules, Regulations, and Reforms;
Technology Adoption;
Corporate Social Responsibility and Impact;
Technology Industry;
United States;
European Union;
China
Healy, Paul M., and Debora L. Spar. "Monsters in the Machine? Tackling the Challenge of Responsible AI." Harvard Business School Case 324-062, December 2023.
- 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).
- 2023
- Book
Beyond AI: ChatGPT, Web3, and the Business Landscape of Tomorrow
By: Ken Huang, Yang Wang, Feng Zhu, Xi Chen and Chunxiao Xing
This book explores the transformative potential of ChatGPT, Web3, and their impact on productivity and various industries. It delves into Generative AI (GenAI) and its representative platform ChatGPT, their synergy with Web3, and how they can revolutionize business...
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Huang, Ken, Yang Wang, Feng Zhu, Xi Chen, and Chunxiao Xing, eds. Beyond AI: ChatGPT, Web3, and the Business Landscape of Tomorrow. Springer, 2023.
- 2023
- Working Paper
The Uneven Impact of Generative AI on Entrepreneurial Performance
By: Nicholas G. Otis, Rowan Clarke, Solène Delecourt, David Holtz and Rembrand Koning
There is a growing belief that scalable and low-cost AI assistance can improve firm
decision-making and economic performance. However, running a business involves
a myriad of open-ended problems, making it hard to generalize from recent studies
showing that...
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Keywords:
AI and Machine Learning;
Performance Improvement;
Small Business;
Decision Choices and Conditions;
Kenya
Otis, Nicholas G., Rowan Clarke, Solène Delecourt, David Holtz, and Rembrand Koning. "The Uneven Impact of Generative AI on Entrepreneurial Performance." Harvard Business School Working Paper, No. 24-042, December 2023.
- 2023
- Article
Which Models Have Perceptually-Aligned Gradients? An Explanation via Off-Manifold Robustness
By: Suraj Srinivas, Sebastian Bordt and Himabindu Lakkaraju
One of the remarkable properties of robust computer vision models is that their input-gradients are often aligned with human perception, referred to in the literature as perceptually-aligned gradients (PAGs). Despite only being trained for classification, PAGs cause...
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Srinivas, Suraj, Sebastian Bordt, and Himabindu Lakkaraju. "Which Models Have Perceptually-Aligned Gradients? An Explanation via Off-Manifold Robustness." Advances in Neural Information Processing Systems (NeurIPS) (2023).