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- 2022
- Article
Efficiently Training Low-Curvature Neural Networks
By: Suraj Srinivas, Kyle Matoba, Himabindu Lakkaraju and Francois Fleuret
Standard deep neural networks often have excess non-linearity, making them susceptible to issues such as low adversarial robustness and gradient instability. Common methods to address these downstream issues, such as adversarial training, are expensive and often...
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Keywords:
AI and Machine Learning
Srinivas, Suraj, Kyle Matoba, Himabindu Lakkaraju, and Francois Fleuret. "Efficiently Training Low-Curvature Neural Networks." Advances in Neural Information Processing Systems (NeurIPS) (2022).
- November–December 2022
- Article
Can AI Really Help You Sell?
By: Jim Dickie, Boris Groysberg, Benson P. Shapiro and Barry Trailer
Many salespeople today are struggling; only 57% of them make their annual quotas, surveys show. One problem is that buying processes have evolved faster than selling processes, and buyers today can access a wide range of online resources that let them evaluate products...
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Dickie, Jim, Boris Groysberg, Benson P. Shapiro, and Barry Trailer. "Can AI Really Help You Sell?" Harvard Business Review (November–December 2022): 120–129.
- 2022
- Working Paper
What Would It Mean for a Machine to Have a Self?
By: Julian De Freitas, Ahmet Kaan Uğuralp, Zeliha Uğuralp, Laurie Paul, Joshua B. Tenenbaum and Tomer Ullman
What would it mean for autonomous AI agents to have a ‘self’? One proposal for a minimal
notion of self is a representation of one’s body spatio-temporally located in the world, with a tag
of that representation as the agent taking actions in the world. This turns...
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De Freitas, Julian, Ahmet Kaan Uğuralp, Zeliha Uğuralp, Laurie Paul, Joshua B. Tenenbaum, and Tomer Ullman. "What Would It Mean for a Machine to Have a Self?" Harvard Business School Working Paper, No. 23-017, September 2022.
- August 2022
- Article
What Makes a Good Image? Airbnb Demand Analytics Leveraging Interpretable Image Features
By: Shunyuan Zhang, Dokyun Lee, Param Vir Singh and Kannan Srinivasan
We study how Airbnb property demand changed after the acquisition of verified images (taken by Airbnb’s photographers) and explore what makes a good image for an Airbnb property. Using deep learning and difference-in-difference analyses on an Airbnb panel dataset...
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Keywords:
Sharing Economy;
Airbnb;
Property Demand;
Computer Vision;
Deep Learning;
Image Feature Extraction;
Content Engineering;
Property;
Marketing;
Demand and Consumers
Zhang, Shunyuan, Dokyun Lee, Param Vir Singh, and Kannan Srinivasan. "What Makes a Good Image? Airbnb Demand Analytics Leveraging Interpretable Image Features." Management Science 68, no. 8 (August 2022): 5644–5666.
- May 2022 (Revised July 2022)
- Case
The Voice War Continues: Hey Google vs. Alexa vs. Siri in 2022
By: David B. Yoffie and Daniel Fisher
In 2022, after five years of pursuing a new "AI-first" strategy, Google had captured a sizeable share of the American and global markets for voice assistants. Google Assistant was used by hundreds of millions of users around the world, but Amazon retained the largest...
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Keywords:
Strategy;
Artificial Intelligence;
Deep Learning;
Voice Assistants;
Smart Home;
Market Share;
Globalized Markets and Industries;
Competitive Strategy;
Digital Platforms;
AI and Machine Learning;
Technology Industry;
United States
Yoffie, David B., and Daniel Fisher. "The Voice War Continues: Hey Google vs. Alexa vs. Siri in 2022." Harvard Business School Case 722-462, May 2022. (Revised July 2022.)
- February 2022 (Revised September 2022)
- Case
InstaDeep: AI Innovation Born in Africa (A)
By: Shikhar Ghosh and Esel Çekin
Karim Beguir and Zohra Slim were the co-founders of InstaDeep, a deep tech startup focusing on artificial intelligence (AI) solutions. Instadeep was one of the few companies globally that were partnering with DeepMind, an AI subsidiary of Google [Alphabet Inc.]....
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Keywords:
AI;
Artificial Intelligence;
Entrepreneurship;
Operations;
Business Subsidiaries;
Brands and Branding;
Innovation and Invention;
Growth and Development Strategy;
AI and Machine Learning;
Technology Industry;
Africa
Ghosh, Shikhar, and Esel Çekin. "InstaDeep: AI Innovation Born in Africa (A)." Harvard Business School Case 822-104, February 2022. (Revised September 2022.)
- February 2022 (Revised July 2022)
- Supplement
InstaDeep: AI Innovation Born in Africa (B)
By: Shikhar Ghosh and Esel Çekin
Karim Beguir and Zohra Slim were the co-founders of InstaDeep, a deep tech startup focusing on artificial intelligence (AI) solutions. Instadeep was one of the few companies globally that were partnering with DeepMind, an AI subsidiary of Google [Alphabet Inc.]....
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Keywords:
AI;
Artificial Intelligence;
Entrepreneurship;
Operations;
Business Subsidiaries;
Brands and Branding;
Innovation and Invention;
Growth and Development Strategy;
AI and Machine Learning;
Technology Industry;
Africa
Ghosh, Shikhar, and Esel Çekin. "InstaDeep: AI Innovation Born in Africa (B)." Harvard Business School Supplement 822-105, February 2022. (Revised July 2022.)
- Article
Health Equity, Schooling Hesitancy, and the Social Determinants of Learning
By: Meira Levinson, Alan C. Geller, Joseph G. Allen and John D. Macomber
At least 62 million K-12 students in North America—disproportionately low-income children of color— have been physically out of school for over a year due to the COVID-19 pandemic. These children are at risk of significant academic, social, mental, and physical harm...
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Keywords:
COVID-19;
Public Health;
Air Quality;
Social Determinants Of Health;
Schooling Hesitancy;
Vaccine Hesitancy;
Racial Injustice;
Inequity;
Inequality;
Health Pandemics;
Education;
Health Care and Treatment;
Policy;
Race;
Equality and Inequality
Levinson, Meira, Alan C. Geller, Joseph G. Allen, and John D. Macomber. "Health Equity, Schooling Hesitancy, and the Social Determinants of Learning." Art. 100032. Lancet Regional Health – Americas 2 (October 2021).
- October 2021
- Article
Overcoming the Cold Start Problem of CRM Using a Probabilistic Machine Learning Approach
By: Nicolas Padilla and 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...
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Keywords:
Customer Management;
Targeting;
Deep Exponential Families;
Probabilistic Machine Learning;
Cold Start Problem;
Customer Relationship Management;
Programs;
Consumer Behavior;
Analysis
Padilla, Nicolas, and Eva Ascarza. "Overcoming the Cold Start Problem of CRM Using a Probabilistic Machine Learning Approach." Journal of Marketing Research (JMR) 58, no. 5 (October 2021): 981–1006.
- 2021
- Working Paper
Deep Learning for Two-Sided Matching
By: Sai Srivatsa Ravindranatha, Zhe Feng, Shira Li, Jonathan Ma, Scott Duke Kominers and David Parkes
We initiate the use of a multi-layer neural network to model two-sided matching and to explore the design space between strategy-proofness and stability. It is well known that both properties cannot be achieved simultaneously but the efficient frontier in this design...
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Keywords:
Strategy-proofness;
Deep Learning;
Two-Sided Platforms;
Marketplace Matching;
Balance and Stability
Srivatsa Ravindranatha, Sai, Zhe Feng, Shira Li, Jonathan Ma, Scott Duke Kominers, and David Parkes. "Deep Learning for Two-Sided Matching." Working Paper, July 2021.
- June 2021
- Case
Bozoma Saint John: Leading with Authenticity and Urgency
By: Francesca Gino and Frances X. Frei
In this multimedia case, Bozoma Saint John recounts numerous defining moments from her childhood and work experiences. We learn what empowered and inspired her to be her authentic self, to be vulnerable and open to new experiences, to find commonality with others, to...
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Keywords:
Biases;
Personal Development and Career;
Identity;
Interests;
Ethics;
Values and Beliefs;
Opportunities;
Leadership Style;
Diversity
Gino, Francesca, and Frances X. Frei. "Bozoma Saint John: Leading with Authenticity and Urgency." Harvard Business School Multimedia/Video Case 921-708, June 2021.
- 2021
- Working Paper
First Law of Motion: Influencer Video Advertising on TikTok
By: Jeremy Yang, Juanjuan Zhang and Yuhan Zhang
This paper engineers an intuitive feature that is predictive of the causal effect of influencer video advertising on product sales. We propose the concept of m-score, a summary statistic that captures the extent to which a product is advertised in the most engaging...
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Keywords:
Influencer Advertising;
Video Advertising;
Computer Vision;
Machine Learning;
Advertising;
Online Technology
Yang, Jeremy, Juanjuan Zhang, and Yuhan Zhang. "First Law of Motion: Influencer Video Advertising on TikTok." Working Paper, March 2021.
- February 2021
- Case
Emma Dench: Leadership and Ancient Rome
By: Francesca Gino and Frances X. Frei
In this multimedia case, classics scholar Emma Dench guides us in understanding leadership insights that can be captured from historical figures and works dating back to Ancient Rome. We learn the language, ideas, and patterns of behavior that are relevant to...
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Gino, Francesca, and Frances X. Frei. "Emma Dench: Leadership and Ancient Rome." Harvard Business School Multimedia/Video Case 921-702, February 2021.
- Article
Development of a Deep Learning Algorithm for Periapical Disease Detection in Dental Radiographs
By: Michael G. Endres, Florian Hillen, Marios Salloumis, Ahmad R. Sedaghat, Stefan M. Niehues, Olivia Quatela, Henning Hanken, Ralf Smeets, Benedicta Beck-Broichsitter, Carsten Rendenbach, Karim R. Lakhani, Max Helland and Robert A. Gaudin
Periapical radiolucencies, which can be detected on panoramic radiographs, are one of the most common radiographic findings in dentistry and have a differential diagnosis including infections, granuloma, cysts, and tumors. In this study, we seek to investigate the...
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Keywords:
Artificial Intelligence;
Diagnosis;
Computer-assisted;
Image Interpretation;
Machine Learning;
Radiography;
Panoramic Radiograph;
AI and Machine Learning
Endres, Michael G., Florian Hillen, Marios Salloumis, Ahmad R. Sedaghat, Stefan M. Niehues, Olivia Quatela, Henning Hanken, Ralf Smeets, Benedicta Beck-Broichsitter, Carsten Rendenbach, Karim R. Lakhani, Max Helland, and Robert A. Gaudin. "Development of a Deep Learning Algorithm for Periapical Disease Detection in Dental Radiographs." Diagnostics 10, no. 6 (June 2020).
- Article
Detecting Adversarial Attacks via Subset Scanning of Autoencoder Activations and Reconstruction Error
By: Celia Cintas, Skyler Speakman, Victor Akinwande, William Ogallo, Komminist Weldemariam, Srihari Sridharan and Edward McFowland III
Reliably detecting attacks in a given set of inputs is of high practical relevance because of the vulnerability of neural networks to adversarial examples. These altered inputs create a security risk in applications with real-world consequences, such as self-driving...
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Keywords:
Autoencoder Networks;
Pattern Detection;
Subset Scanning;
Computer Vision;
Statistical Methods And Machine Learning;
Machine Learning;
Deep Learning;
Data Mining;
Big Data;
Large-scale Systems;
Mathematical Methods;
Analytics and Data Science
Cintas, Celia, Skyler Speakman, Victor Akinwande, William Ogallo, Komminist Weldemariam, Srihari Sridharan, and Edward McFowland III. "Detecting Adversarial Attacks via Subset Scanning of Autoencoder Activations and Reconstruction Error." Proceedings of the International Joint Conference on Artificial Intelligence 29th (2020).
- July 2019
- Teaching Note
AT&T, Retraining, and the Workforce of Tomorrow
By: William R. Kerr and Carl Kreitzberg
A Teaching Note for the "AT&T, Retraining, and the Workforce of Tomorrow" case study (HBS#820-017). The case describes how AT&T designed and implemented a program to retrain 100,000 of its workers. The case first reviews the technological forces that compelled AT&T to...
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Keywords:
AT&T;
Workforce;
Future Of Work;
Telecommunications;
Unions;
Technological Change;
Layoffs;
MOOCS;
Strategic Planning;
Employees;
Training;
Labor;
Learning;
Labor Unions;
Technology Adoption;
Talent and Talent Management;
Transformation;
Telecommunications Industry;
Communications Industry;
United States
- July 2019 (Revised November 2019)
- Case
Osaro: Picking the Best Path
By: William R. Kerr, James Palano and Bastiane Huang
The founder of Osaro saw the potential of deep reinforcement learning to allow robots to be applied to new applications. Osaro targeted warehousing, already a dynamic industry for robotics and automation, for its initial product—a system which would allow robotic arms...
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Keywords:
Artificial Intelligence;
Machine Learning;
Robotics;
Robots;
Ecommerce;
Fulfillment;
Warehousing;
AI;
Startup;
Technology Commercialization;
Business Startups;
Entrepreneurship;
Logistics;
Order Taking and Fulfillment;
Information Technology;
Commercialization;
Learning;
Complexity;
Competition;
E-commerce
Kerr, William R., James Palano, and Bastiane Huang. "Osaro: Picking the Best Path." Harvard Business School Case 820-012, July 2019. (Revised November 2019.)
- 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.)
- September 2018 (Revised December 2019)
- Case
Zebra Medical Vision
By: Shane Greenstein and Sarah Gulick
An Israeli startup founded in 2014, Zebra Medical Vision developed algorithms that produced diagnoses from X-rays, mammograms, and CT-scans. The algorithms used deep learning and digitized radiology scans to create software that could assist doctors in making...
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Keywords:
Radiology;
Machine Learning;
X-ray;
CT Scan;
Medical Technology;
Probability;
FDA 510(k);
Diagnosis;
Business Startups;
Health Care and Treatment;
Information Technology;
Applications and Software;
Competitive Strategy;
Product Development;
Commercialization;
Decision Choices and Conditions;
Health Industry;
Medical Devices and Supplies Industry;
Technology Industry;
Israel
Greenstein, Shane, and Sarah Gulick. "Zebra Medical Vision." Harvard Business School Case 619-014, September 2018. (Revised December 2019.)
- 2017
- Mimeo
Science for Society: Science and Technology Based Social Entrepreneurship
By: Tarun Khanna, Shashank Shah and Kundan Madireddy
This publication is an outcome of the team's research, engagement and interactions with over 25 science and technology-based social enterprises in India. It provides details on the research process, insightful outcomes and innovative impact.
Throughout the... View Details
Throughout the... View Details
Keywords:
Social Entrepreneurship;
Science-Based Business;
Information Technology;
Business and Community Relations;
India
Khanna, Tarun, Shashank Shah, and Kundan Madireddy. "Science for Society: Science and Technology Based Social Entrepreneurship." Harvard University South Asia Institute, 2017. Mimeo. (This publication is an outcome of a grant from the Tata Trusts.)