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Show Results For
- All HBS Web
(1,432)
- People (1)
- News (271)
- Research (903)
- Events (17)
- Multimedia (6)
- Faculty Publications (724)
- November 2023
- Case
Open Source Machine Learning at Google
Set in early 2023, the case exposes students to the challenges of managing open source software at Google. The case focuses on the challenges for Alex Spinelli, Vice President of Product Management for Core Machine Learning. He must set priorities for Google’s efforts... View Details
Keywords: Decision Choices and Conditions; Technological Innovation; Open Source Distribution; Strategy; AI and Machine Learning; Applications and Software; Technology Industry; United States
Greenstein, Shane, Martin Wattenberg, Fernanda B. Viégas, Daniel Yue, and James Barnett. "Open Source Machine Learning at Google." Harvard Business School Case 624-015, November 2023.
- March 1988
- Background Note
Machine Tool Industry and Industrial Policy--1955-82 (Condensed)
By: Norman A. Berg
Berg, Norman A. "Machine Tool Industry and Industrial Policy--1955-82 (Condensed)." Harvard Business School Background Note 388-117, March 1988.
- August 2023 (Revised December 2023)
- Case
Automating Morality: Ethics for Intelligent Machines
By: Joseph L. Badaracco Jr. and Tom Quinn
As autonomy became a more significant part of modern life – most notably in autonomous vehicles (AVs), such as Teslas – ethical debates about whether and how to impart ethics to machines heated up. Utilitarians pointed out that autonomous vehicles crashed much less... View Details
Keywords: Cost vs Benefits; Judgments; Fairness; Moral Sensibility; Values and Beliefs; Cross-Cultural and Cross-Border Issues; Disruptive Innovation; Technology Adoption; Risk and Uncertainty; Cognition and Thinking; Technological Innovation; Auto Industry; Technology Industry; Africa; Asia; Europe; North and Central America; Oceania; South America
Badaracco, Joseph L., Jr., and Tom Quinn. "Automating Morality: Ethics for Intelligent Machines." Harvard Business School Case 324-007, August 2023. (Revised December 2023.)
- Research Summary
Making Machine Learning Robust to Adversarial Attacks
The goal of this research is to ensure that machine learning models that we build and deploy are not easily susceptible to attacks by adversarial or malicious entities. View Details
- July 1988
- Supplement
LTV Aerospace and Defense: Flexible Machining Cell, Video
By: David A. Garvin
Garvin, David A. "LTV Aerospace and Defense: Flexible Machining Cell, Video." Harvard Business School Video Supplement 889-501, July 1988.
- June 1983
- Teaching Note
Note on the Paper Machinery Industry, Teaching Note
Teaching Note for (9-383-185). View Details
Keywords: Pulp and Paper Industry
Work Mate Marry Love: How Machines Shape Our Human Destiny
What will happen to our notions of marriage and parenthood as reproductive technologies increasingly allow for newfangled ways of creating babies? What will happen to our understanding of gender as medical advances enable individuals to transition from one set of... View Details
- 02 Aug 2017
- Working Paper Summaries
Machine Learning Methods for Strategy Research
Keywords: by Mike Horia Teodorescu
- 25 Oct 2017
- Research & Ideas
Will Machine Learning Make You a Better Manager?
buy, how we talk, and even how we feel—and use that to make predictions about how we’ll act next. As the field of machine learning (ML) has become increasingly mainstream, says Harvard Business School... View Details
- February 2008 (Revised August 2011)
- Case
Olympia Machine Company, Inc.
By: Frank V. Cespedes and Benson P. Shapiro
The management team of an industrial equipment supplier is debating the company's method of compensating salespeople. Different executives have offered different alternatives to the current method of straight salary plus expenses. Each option has different implications... View Details
Keywords: Governance Controls; Compensation and Benefits; Mission and Purpose; Salesforce Management; Motivation and Incentives; Business Strategy; Industrial Products Industry
Cespedes, Frank V., and Benson P. Shapiro. "Olympia Machine Company, Inc." Harvard Business School Case 708-490, February 2008. (Revised August 2011.)
- February 2021
- Tutorial
Assessing Prediction Accuracy of Machine Learning Models
By: Michael Toffel and Natalie Epstein
This video describes how to assess the accuracy of machine learning prediction models, primarily in the context of machine learning models that predict binary outcomes, such as logistic regression, random forest, or nearest neighbor models. After introducing and... View Details
- 2013
- Case
Innovation and Development of China Machine Press in the New Century
By: F. Warren McFarlan, Ning Jia and Guo Jia
China Machine Press (CMP), founded in 1952, is a leading multi-field, multi-discipline and multimedia publishing group in China with large scale, comprehensive and specialized business that integrates paper media, audiovisual media and online media, and combines... View Details
McFarlan, F. Warren, Ning Jia, and Guo Jia. "Innovation and Development of China Machine Press in the New Century." Tsinghua University Case, 2013.
- Working Paper
Visual Uniqueness in Peer-to-Peer Marketplaces: Machine Learning Model Development, Validation, and Application
By: Flora Feng, Charis Li and Shunyuan Zhang
Peer-to-peer (P2P) marketplaces have seen exponential growth in recent years featured by unique offerings from individual providers. Despite the perceived value of uniqueness, scalable quantification of visual uniqueness in P2P platforms like Airbnb has been largely... View Details
Keywords: Peer-to-peer Markets; Marketplace Matching; AI and Machine Learning; Demand and Consumers; Digital Platforms; Marketing
Feng, Flora, Charis Li, and Shunyuan Zhang. "Visual Uniqueness in Peer-to-Peer Marketplaces: Machine Learning Model Development, Validation, and Application." SSRN Working Paper Series, No. 4665286, February 2024.
- 06 Mar 2021
- News
How to Upgrade Judges with Machine Learning
- December 1978 (Revised December 1985)
- Case
Clairol Skin Machine (C)
By: Walter J. Salmon and Steven R. Palesy
Keywords: Beauty and Cosmetics Industry
Salmon, Walter J., and Steven R. Palesy. "Clairol Skin Machine (C)." Harvard Business School Case 579-112, December 1978. (Revised December 1985.)
- January 2011 (Revised March 2011)
- Teaching Note
The Wright Brothers and their Flying Machines (TN)
By: Tom Nicholas
Teaching Note for 811-034. View Details
- February 1986 (Revised June 1987)
- Case
Ingersoll Milling Machine Co.
By: Robin Cooper and Robert S. Kaplan
Keywords: Manufacturing Industry
Cooper, Robin, and Robert S. Kaplan. "Ingersoll Milling Machine Co." Harvard Business School Case 186-189, February 1986. (Revised June 1987.)
- November 2000 (Revised April 2002)
- Teaching Note
International Business Machines Corporation (A), (B), and (C) TN
By: David F. Hawkins
Teaching Note for (9-100-032), (9-100-033), and (9-100-034). View Details
- November 1987 (Revised March 1988)
- Case
Searching for Trade Remedies: The U.S. Machine Tool Industry--1983
By: David B. Yoffie
In 1983 the National Machine Tools Builder Association was predicting a declining market for the United States and rising imports. Machine tool manufacturers had to decide if they should ask the U.S. government for help, and if they did, which administrative channels... View Details
Keywords: Economic Slowdown and Stagnation; Machinery and Machining; Government and Politics; Law; Production; Business and Government Relations; Competition; Manufacturing Industry; Japan; Germany; United States
Yoffie, David B. "Searching for Trade Remedies: The U.S. Machine Tool Industry--1983." Harvard Business School Case 388-071, November 1987. (Revised March 1988.)
- 2020
- Working Paper
Overcoming the Cold Start Problem of CRM Using a Probabilistic Machine Learning Approach
By: Eva Ascarza
The success of Customer Relationship Management (CRM) programs ultimately depends on the firm's ability to understand consumers' preferences and precisely capture how these preferences may differ across customers. Only by understanding customer heterogeneity, firms can... View Details
Keywords: Customer Management; Targeting; Deep Exponential Families; Probabilistic Machine Learning; Cold Start Problem; Customer Relationship Management; Customer Value and Value Chain; Consumer Behavior; Analytics and Data Science; Mathematical Methods; Retail Industry
Padilla, Nicolas, and Eva Ascarza. "Overcoming the Cold Start Problem of CRM Using a Probabilistic Machine Learning Approach." Harvard Business School Working Paper, No. 19-091, February 2019. (Revised May 2020. Accepted at the Journal of Marketing Research.)