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- People (1)
- News (199)
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- Multimedia (7)
- Faculty Publications (429)
Show Results For
-
All HBS Web
(990)
- People (1)
- News (199)
- Research (540)
- Events (8)
- Multimedia (7)
- Faculty Publications (429)
- Article
Oracle Efficient Private Non-Convex Optimization
By: Seth Neel, Aaron Leon Roth, Giuseppe Vietri and Zhiwei Steven Wu
One of the most effective algorithms for differentially private learning and optimization is objective perturbation. This technique augments a given optimization problem (e.g. deriving from an ERM problem) with a random linear term, and then exactly solves it....
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Neel, Seth, Aaron Leon Roth, Giuseppe Vietri, and Zhiwei Steven Wu. "Oracle Efficient Private Non-Convex Optimization." Proceedings of the International Conference on Machine Learning (ICML) 37th (2020).
- Web
Generative AI - Alumni
Careers Generative AI Careers Generative AI In today's dynamic job market, Generative Artificial Intelligence (GenAI) stands out as a powerful ally for job seekers, offering personalized insights into...
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- June 19, 2023
- Article
Should You Start a Generative AI Company?
Many entrepreneurs are considering starting companies that leverage the latest generative AI technology, but they must ask themselves whether they have what it takes to compete on increasingly commoditized foundational models, or whether they should instead...
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De Freitas, Julian. "Should You Start a Generative AI Company?" Harvard Business Review (website) (June 19, 2023).
- April 2017
- Case
The Future of Patent Examination at the USPTO
By: Prithwiraj Choudhury, Tarun Khanna and Sarah Mehta
The U.S. Patent and Trademark Office (USPTO) is the federal government agency responsible for evaluating and granting patents and trademarks. In 2015, the USPTO employed approximately 8,000 patent examiners who granted nearly 300,000 patents to inventors. As of April...
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Keywords:
Machine Learning;
Telework;
Collaborating With Unions;
Human Resources;
Recruitment;
Retention;
Intellectual Property;
Copyright;
Patents;
Trademarks;
Knowledge Sharing;
Technology Adoption;
Organizational Change and Adaptation;
Performance Productivity;
Performance Improvement;
District of Columbia
Choudhury, Prithwiraj, Tarun Khanna, and Sarah Mehta. "The Future of Patent Examination at the USPTO." Harvard Business School Case 617-027, April 2017.
- 30 May 2023
- Research & Ideas
Can AI Predict Whether Shoppers Would Pick Crest Over Colgate?
but large language models like generative pre-trained transformers (GPTs) may allow companies to rely on AI to uncover consumers’ tastes, according to new research from Harvard Business School and Microsoft....
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- November 2023 (Revised April 2024)
- Case
Khanmigo: Revolutionizing Learning with GenAI
By: William A. Sahlman, Allison M. Ciechanover and Emily Grandjean
Already a leader in the edtech space since its 2008 launch, Khan Academy was now one of the first edtech organizations to embrace generative artificial intelligence ("genAI"). In March 2023, Khan Academy began beta testing Khanmigo, a genAI “guide” and tutor built with...
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Keywords:
Technology Adoption;
Leading Change;
Entrepreneurship;
Risk and Uncertainty;
Education Industry;
Technology Industry;
United States;
San Francisco
Sahlman, William A., Allison M. Ciechanover, and Emily Grandjean. "Khanmigo: Revolutionizing Learning with GenAI." Harvard Business School Case 824-059, November 2023. (Revised April 2024.)
- November 2022 (Revised January 2023)
- Case
Hugging Face: Serving AI on a Platform
By: Shane Greenstein, Daniel Yue, Kerry Herman and Sarah Gulick
It is fall 2022, and open-source AI model company Hugging Face is considering its three areas of priorities: platform development, supporting the open-source community, and pursuing cutting-edge scientific research. As it expands services for enterprise clients, which...
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Keywords:
Community;
Open-source;
AI and Machine Learning;
Product Development;
Networks;
Service Delivery;
Research;
Governance;
Business and Stakeholder Relations;
Information Industry;
Technology Industry;
United States
Greenstein, Shane, Daniel Yue, Kerry Herman, and Sarah Gulick. "Hugging Face: Serving AI on a Platform." Harvard Business School Case 623-026, November 2022. (Revised January 2023.)
- December 1, 2021
- Article
Do You Know How Your Teams Get Work Done?
By: Rohan Narayana Murty, Rajath B. Das, Scott Duke Kominers, Arjun Narayan, Suraj Srinivasan, Tarun Khanna and Kartik Hosanagar
In a research study at four Fortune 500 companies, when managers were asked about their teams’ work, on average they either did not know or could not remember 60% of the work their teams do. This is a major problem because it can lead to unrealistic digital...
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Keywords:
Leading Teams;
Work Recall Gap;
Machine Learning;
Algorithms;
Groups and Teams;
Management;
Technological Innovation
Murty, Rohan Narayana, Rajath B. Das, Scott Duke Kominers, Arjun Narayan, Suraj Srinivasan, Tarun Khanna, and Kartik Hosanagar. "Do You Know How Your Teams Get Work Done?" Harvard Business Review Digital Articles (December 1, 2021).
- September 2023 (Revised April 2024)
- Case
Atomwise: Strategic Opportunities in AI for Pharma
By: Satish Tadikonda
Abraham Heifets and his co-founder, Izhar Wallach, had founded Atomwise to develop i) an AI engine to transform drug discovery by creating better medicines faster, and ii) a machine learning-based discovery engine that combined the power of convolutional neural...
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Tadikonda, Satish. "Atomwise: Strategic Opportunities in AI for Pharma." Harvard Business School Case 824-043, September 2023. (Revised April 2024.)
- August 25, 2022
- Article
Find the Right Pace for Your AI Rollout
By: Rebecca Karp and Aticus Peterson
Implementing AI can introduce disruptive change and disfranchise staff and employees. When members are reluctant to adopt a new technology, they might hesitate to use it, push back against its deployment, or use it in limited capacity — which affects the benefits an...
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Karp, Rebecca, and Aticus Peterson. "Find the Right Pace for Your AI Rollout." Harvard Business Review Digital Articles (August 25, 2022).
- 14 Aug 2017
- Conference Presentation
A Convex Framework for Fair Regression
By: Richard Berk, Hoda Heidari, Shahin Jabbari, Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Seth Neel and Aaron Roth
We introduce a flexible family of fairness regularizers for (linear and logistic) regression problems. These regularizers all enjoy convexity, permitting fast optimization, and they span the range from notions of group fairness to strong individual fairness. By varying...
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Berk, Richard, Hoda Heidari, Shahin Jabbari, Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Seth Neel, and Aaron Roth. "A Convex Framework for Fair Regression." Paper presented at the 4th Workshop on Fairness, Accountability, and Transparency in Machine Learning, Special Interest Group on Knowledge Discovery and Data Mining (SIGKDD), August 14, 2017.
- July–August 2021
- Article
Why You Aren't Getting More from Your Marketing AI
By: Eva Ascarza, Michael Ross and Bruce G.S. Hardie
Fewer than 40% of companies that invest in AI see gains from it, usually because of one or more of these errors: (1) They don’t ask the right question, and end up directing AI to solve the wrong problem. (2) They don’t recognize the differences between the value of...
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Keywords:
Artificial Intelligence;
Marketing;
Decision Making;
Communication;
Framework;
AI and Machine Learning
Ascarza, Eva, Michael Ross, and Bruce G.S. Hardie. "Why You Aren't Getting More from Your Marketing AI." Harvard Business Review 99, no. 4 (July–August 2021): 48–54.
- October 2019
- Case
Feeling Machines: Emotion AI at Affectiva
By: Shane Greenstein and John Masko
In 2016, Affectiva—a Boston-based emotion AI software company with a long track record of building emotion-sensing software for market research—had attempted to expand into new verticals by releasing a mobile software development kit (SDK) that downloaders could adapt...
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Keywords:
Artificial Intelligence;
Market Research;
Business Model;
Finance;
Revenue;
Decision Making;
Risk and Uncertainty;
Market Entry and Exit;
Applications and Software;
AI and Machine Learning;
Information Technology Industry;
Auto Industry;
United States
Greenstein, Shane, and John Masko. "Feeling Machines: Emotion AI at Affectiva." Harvard Business School Case 620-058, October 2019.
- Research Summary
Overview
I develop machine learning tools and techniques which enable human decision makers to make better decisions. More specifically, my research addresses the following fundamental questions pertaining to human and algorithmic decision-making:
1. How to build... View Details
1. How to build... View Details
- October 2023
- Article
Improving Regulatory Effectiveness Through Better Targeting: Evidence from OSHA
By: Matthew S. Johnson, David I. Levine and Michael W. Toffel
We study how a regulator can best target inspections. Our case study is a U.S. Occupational Safety and Health Administration (OSHA) program that randomly allocated some inspections. On average, each inspection averted 2.4 serious injuries (9%) over the next five years....
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Keywords:
Safety Regulations;
Regulations;
Regulatory Enforcement;
Machine Learning Models;
Safety;
Operations;
Service Operations;
Production;
Forecasting and Prediction;
Decisions;
United States
Johnson, Matthew S., David I. Levine, and Michael W. Toffel. "Improving Regulatory Effectiveness Through Better Targeting: Evidence from OSHA." American Economic Journal: Applied Economics 15, no. 4 (October 2023): 30–67. (Profiled in the Regulatory Review.)
- September–October 2023
- Article
Reskilling in the Age of AI
In the coming decades, as the pace of technological change continues to increase, millions of workers may need to be not just upskilled but reskilled—a profoundly complex societal challenge that will sometimes require workers to both acquire new skills and...
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Keywords:
Competency and Skills;
AI and Machine Learning;
Training;
Adaptation;
Employees;
Digital Transformation
Tamayo, Jorge, Leila Doumi, Sagar Goel, Orsolya Kovács-Ondrejkovic, and Raffaella Sadun. "Reskilling in the Age of AI." Harvard Business Review 101, no. 5 (September–October 2023): 56–65.
- March 2023 (Revised March 2024)
- Case
Accelerating AI Adoption in the U.S. Air Force
By: Maria P. Roche and Alexander Farrow
In August 2022, the Pentagon tasked U.S. Air Force Captain Victor Lopez to launch a new office for AFWERX, an Air Force innovation unit that leveraged commercial developers and military talent to acquire advanced technologies. This task was particularly arduous because...
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Keywords:
Technological Innovation;
Organizational Design;
AI and Machine Learning;
Adoption;
Technology Adoption;
United States
Roche, Maria P., and Alexander Farrow. "Accelerating AI Adoption in the U.S. Air Force." Harvard Business School Case 723-429, March 2023. (Revised March 2024.)
Competing in the Age of AI
Marco Iansiti and Karim R. Lakhani show how reinventing the firm around data, analytics, and AI removes traditional constraints on scale, scope, and learning that have restricted business growth for hundreds of years. From... View Details
- 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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- September 2020
- Article
Creativity, Artificial Intelligence, and a World of Surprises
In recent years, progress has been made toward AI Creativity, which I define as the production of highly novel, yet appropriate, ideas, problem solutions, or other outputs by autonomous machines. I argue that organizational researchers of creativity and innovation...
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Keywords:
Artificial Intelligence;
AI Creativity;
Computer Science;
Organizational Behavior;
Psychology;
Creativity;
Technological Innovation;
AI and Machine Learning
Amabile, Teresa M. "Creativity, Artificial Intelligence, and a World of Surprises." Academy of Management Discoveries 6, no. 3 (September 2020): 351–354.