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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
- 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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- 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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- 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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- 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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- 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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- 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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- 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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- 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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- 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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- 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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- 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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- 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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- 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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- 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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- 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
- 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...
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- 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
- 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