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All HBS Web
(1,456)
- People (1)
- News (264)
- Research (813)
- Events (13)
- Multimedia (7)
- Faculty Publications (654)
- 01 Mar 2019
- News
Alumni and Faculty Books for March 2019
As the Ottoman Empire crumbled, the Middle East and Balkans became the site of contestation and cooperation between the traditional forces of religion and the emergent View Details
- January 1995 (Revised October 1995)
- Case
Schweizerische Maschinenfabrik Zug, A.G.
Schweizerische Maschinenfabrik Zug (SMZ), a 110-year old Swiss machinery firm, faces several issues at the end of 1992. It's not clear whether the company will be able to maintain its traditional price premium in the face of foreign competition. The firm must chart a...
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Keywords:
Competition;
Machinery and Machining;
Globalized Markets and Industries;
Manufacturing Industry;
Switzerland
Enright, Michael J. "Schweizerische Maschinenfabrik Zug, A.G." Harvard Business School Case 795-026, January 1995. (Revised October 1995.)
- 12 Apr 2022
- Research & Ideas
Swiping Right: How Data Helped This Online Dating Site Make More Matches
some estimates, with players such as Bumble, Tinder, and OKCupid vying to help people find love. While McFowland is not a dating expert, his work in machine learning and social...
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by Kara Baskin
- 08 Feb 2016
- Research & Ideas
The Civic Benefits of Google Street View and Yelp
to use it, and when not to use it.” Taming the data flow To get a handle on all this data and to better predict outcomes of policies, Luca believes cities need to develop algorithms to coordinate their own...
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- Web
Technical Benefits and Features - Research Computing Services
Multiple servers that provide redundancy to avoid a single point of failure Up to 10x speedup for certain math, algebraic, and statistical functions & algorithms. No restriction on RAM (other than physical View Details
- 2024
- Working Paper
Warnings and Endorsements: Improving Human-AI Collaboration Under Covariate Shift
By: Matthew DosSantos DiSorbo and Kris Ferreira
Problem definition: While artificial intelligence (AI) algorithms may perform well on data that are representative of the training set (inliers), they may err when extrapolating on non-representative data (outliers). These outliers often originate from covariate shift,...
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DosSantos DiSorbo, Matthew, and Kris Ferreira. "Warnings and Endorsements: Improving Human-AI Collaboration Under Covariate Shift." Working Paper, February 2024.
- 11 Feb 2020
- Sharpening Your Skills
10 Rules Entrepreneurs Need to Know Before Adopting AI
Although adoption of artificial intelligence (AI) and machine learning (ML) for the enterprise is still in the early days, the technology has matured enough for entrepreneurs to start gathering inspiration...
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by Rocio Wu
Fintech, Small Business & the American Dream: How Technology Is Transforming Lending and Shaping a New Era of Small Business Opportunity
Fintech, Small Business & the American Dream describes the needs of small businesses for capital and demonstrates how technology—novel data sources, artificial intelligence, machine learning—will transform the small business lending market. This market... View Details
- January–February 2023
- Article
Forecasting COVID-19 and Analyzing the Effect of Government Interventions
By: Michael Lingzhi Li, Hamza Tazi Bouardi, Omar Skali Lami, Thomas Trikalinos, Nikolaos Trichakis and Dimitris Bertsimas
We developed DELPHI, a novel epidemiological model for predicting detected cases and deaths in the prevaccination era of the COVID-19 pandemic. The model allows for underdetection of infections and effects of government interventions. We have applied DELPHI across more...
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Keywords:
COVID-19 Pandemic;
Epidemics;
Analytics and Data Science;
Health Pandemics;
AI and Machine Learning;
Forecasting and Prediction
Li, Michael Lingzhi, Hamza Tazi Bouardi, Omar Skali Lami, Thomas Trikalinos, Nikolaos Trichakis, and Dimitris Bertsimas. "Forecasting COVID-19 and Analyzing the Effect of Government Interventions." Operations Research 71, no. 1 (January–February 2023): 184–201.
- Research Summary
Overview
Paul is primarily interested in studying explainable machine learning (ML), digital transformation, and data science operations. He works on research that explores how stakeholders within organizations can use machine learning to make better decisions. In particular,...
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- Web
Buy Now, Pay Later: Credit and Information Technology
thought differently, and, in 1874, placed an order with E. Remington and Sons for one hundred machines at $55 each. The ability to paste typewritten entries into the credit ledgers considerably speeded up...
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- 04 Apr 2022
- Research & Ideas
Tech Hubs: How Software Brought Talent and Prosperity to New Cities
software patents. These classified patents were then used to train a machine learning algorithm to identify among millions of patents those that were software related. Once software and non-software patents...
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by Rachel Layne
- 19 Jan 2023
- Research & Ideas
What Makes Employees Trust (vs. Second-Guess) AI?
products were grouped in 241 “style-colors'' and sizes. When the allocators received a recommendation from an interpretable algorithm, they often overruled it based on their own intuition. But when the same allocators had a recommendation...
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by Rachel Layne
- 10 Mar 2015
- Research & Ideas
The Surprising Winners and Losers in the Retail Revolution
and you say, holy cow. That's the conundrum that Walmart is in. Think about this. There are more than 30,000 dollar stores in the US, you can find one everywhere: in rural regions you find Dollar General and...
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- 01 Nov 1999
- Research & Ideas
John H. Patterson and the Sales Strategy of the National Cash Register Company, 1884 to 1922
described the register for the first time and explained how it would prevent theft and give an accurate account of the day's receipts. The goal of this stage was to schedule a demonstration of the View Details
Keywords:
by Walter A. Friedman
- 03 Oct 2017
- First Look
First Look at Research and Ideas, October 3, 2017
using people analytics in hiring. The case also provides an accessible yet thorough explanation of the key aspects of artificial intelligence (supervised, unsupervised, and reinforcement machine learning)....
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by Sean Silverthorne
- 2018
- Working Paper
Design Rules, Volume 2: How Technology Shapes Organizations: Chapter 7 The Value Structure of Technologies, Part 2: Technical and Strategic Bottlenecks as Guides for Action
The purpose of this chapter is to present analytic tools based on functional maps that can be used to identify investment opportunities and to formulate strategy in large, evolving technical systems. I argue that the points of value creation and value capture in a...
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Baldwin, Carliss Y. "Design Rules, Volume 2: How Technology Shapes Organizations: Chapter 7 The Value Structure of Technologies, Part 2: Technical and Strategic Bottlenecks as Guides for Action." Harvard Business School Working Paper, No. 19-042, October 2018.
- 2022
- Working Paper
The Regulation of Medical AI: Policy Approaches, Data, and Innovation Incentives
By: Ariel Dora Stern
For those who follow health and technology news, it is difficult to go more than a few days without reading about a compelling new application of Artificial Intelligence (AI) to health care. AI has myriad applications in medicine and its adjacent industries, with...
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Keywords:
AI and Machine Learning;
Health Care and Treatment;
Governing Rules, Regulations, and Reforms;
Technological Innovation;
Medical Devices and Supplies Industry
Stern, Ariel Dora. "The Regulation of Medical AI: Policy Approaches, Data, and Innovation Incentives." NBER Working Paper Series, No. 30639, December 2022.
- 2021
- Chapter
Towards a Unified Framework for Fair and Stable Graph Representation Learning
By: Chirag Agarwal, Himabindu Lakkaraju and Marinka Zitnik
As the representations output by Graph Neural Networks (GNNs) are increasingly employed in real-world applications, it becomes important to ensure that these representations are fair and stable. In this work, we establish a key connection between counterfactual...
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Agarwal, Chirag, Himabindu Lakkaraju, and Marinka Zitnik. "Towards a Unified Framework for Fair and Stable Graph Representation Learning." In Proceedings of the 37th Conference on Uncertainty in Artificial Intelligence, edited by Cassio de Campos and Marloes H. Maathuis, 2114–2124. AUAI Press, 2021.