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  • All HBS Web  (1,194)
    • People  (1)
    • News  (232)
    • Research  (675)
    • Events  (17)
    • Multimedia  (8)
  • Faculty Publications  (560)
← Page 5 of 1,194 Results →
  • May 2022
  • Case

AWS and Amazon SageMaker (A): The Commercialization of Machine Learning Services

By: Karim R. Lakhani, Shane Greenstein and Kerry Herman
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Lakhani, Karim R., Shane Greenstein, and Kerry Herman. "AWS and Amazon SageMaker (A): The Commercialization of Machine Learning Services." Harvard Business School Case 622-060, May 2022.

    The Experimentation Machine

    Leverage AI to be a 10x Founder

    Today’s most successful founders know that the startups that learn the fastest will win. In The Experimentation Machine, HBS professor, entrepreneur, and venture capitalist Jeffrey J. Bussgang reveals... View Details

    • 17 Jan 2020
    • News

    AB InBev Taps Machine Learning to Root Out Corruption

    • May 2024
    • Teaching Note

    AI21 Labs in 2023: Strategy for Generative AI

    By: David Yoffie
    Teaching Note for HBS Case 724-383. The case has 3 important teaching purposes: First, what are the advantages and disadvantages of imitation? (e.g., Should AI21 imitate OpenAI with a chatbot?) Second, what are the advantages and disadvantages of keeping new technology... View Details
    Keywords: AI; Generative Ai; Generative Models; AI and Machine Learning; Innovation Strategy; Growth and Development Strategy; Business Model; Business Startups; Open Source Distribution; Competitive Advantage; Technology Industry; Israel
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    Yoffie, David. "AI21 Labs in 2023: Strategy for Generative AI." Harvard Business School Teaching Note 724-461, May 2024.
    • 2024
    • Working Paper

    The Crowdless Future? Generative AI and Creative Problem Solving

    By: Léonard Boussioux, Jacqueline N. Lane, Miaomiao Zhang, Vladimir Jacimovic and Karim R. Lakhani
    The rapid advances in generative artificial intelligence (AI) open up attractive opportunities for creative problem-solving through human-guided AI partnerships. To explore this potential, we initiated a crowdsourcing challenge focused on sustainable, circular economy... View Details
    Keywords: Large Language Models; Crowdsourcing; Generative Ai; Creative Problem-solving; Organizational Search; AI-in-the-loop; Prompt Engineering; AI and Machine Learning; Innovation and Invention
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    Boussioux, Léonard, Jacqueline N. Lane, Miaomiao Zhang, Vladimir Jacimovic, and Karim R. Lakhani. "The Crowdless Future? Generative AI and Creative Problem Solving." Harvard Business School Working Paper, No. 24-005, July 2023. (Revised July 2024.)
    • January 2024 (Revised February 2024)
    • Case

    Data-Driven Denim: Financial Forecasting at Levi Strauss

    By: Mark Egan
    The case examines Levi Strauss’ journey in implementing machine learning and AI into its financial forecasting process. The apparel company partnered with the IT company Wipro in 2017 to develop a machine learning algorithm that could help Levi Strauss forecast its... View Details
    Keywords: Investor Relations; Forecasting; Machine Learning; Artificial Intelligence; Apparel; Corporate Finance; Forecasting and Prediction; AI and Machine Learning; Digital Transformation; Apparel and Accessories Industry; United States
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    Egan, Mark. "Data-Driven Denim: Financial Forecasting at Levi Strauss." Harvard Business School Case 224-029, January 2024. (Revised February 2024.)
    • Article

    Towards the Unification and Robustness of Perturbation and Gradient Based Explanations

    By: Sushant Agarwal, Shahin Jabbari, Chirag Agarwal, Sohini Upadhyay, Steven Wu and Himabindu Lakkaraju
    As machine learning black boxes are increasingly being deployed in critical domains such as healthcare and criminal justice, there has been a growing emphasis on developing techniques for explaining these black boxes in a post hoc manner. In this work, we analyze two... View Details
    Keywords: Machine Learning; Black Box Explanations; Decision Making; Forecasting and Prediction; Information Technology
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    Agarwal, Sushant, Shahin Jabbari, Chirag Agarwal, Sohini Upadhyay, Steven Wu, and Himabindu Lakkaraju. "Towards the Unification and Robustness of Perturbation and Gradient Based Explanations." Proceedings of the International Conference on Machine Learning (ICML) 38th (2021).
    • April 2021 (Revised August 2021)
    • Case

    Borusan CAT: Monetizing Prediction in the Age of AI (A)

    By: Navid Mojir and Gamze Yucaoglu
    Borusan Cat is an international distributor of Caterpillar heavy machines. Esra Durgun (Director of Strategy, Digitization, and Innovation) and Ozgur Gunaydin (CEO) seem to have bet their careers on developing Muneccim, a new predictive technology that is designed to... View Details
    Keywords: Monetization Strategy; Artificial Intelligence; AI; Forecasting and Prediction; Applications and Software; Technological Innovation; Marketing; Segmentation; AI and Machine Learning; Construction Industry; Turkey
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    Mojir, Navid, and Gamze Yucaoglu. "Borusan CAT: Monetizing Prediction in the Age of AI (A)." Harvard Business School Case 521-053, April 2021. (Revised August 2021.)
    • 2019
    • Working Paper

    Machine Learning Approaches to Facial and Text Analysis: Discovering CEO Oral Communication Styles

    By: Prithwiraj Choudhury, Dan Wang, Natalie A. Carlson and Tarun Khanna
    We demonstrate how a novel synthesis of three methods—(1) unsupervised topic modeling of text data to generate new measures of textual variance, (2) sentiment analysis of text data, and (3) supervised ML coding of facial images with a cutting-edge convolutional neural... View Details
    Keywords: Spoken Communication; Business History; Analytics and Data Science; Finance; Performance
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    Choudhury, Prithwiraj, Dan Wang, Natalie A. Carlson, and Tarun Khanna. "Machine Learning Approaches to Facial and Text Analysis: Discovering CEO Oral Communication Styles." Harvard Business School Working Paper, No. 18-064, January 2018. (Revised May 2019.)
    • April 2018 (Revised February 2019)
    • Supplement

    Improving Worker Safety in the Era of Machine Learning (B)

    By: Michael W. Toffel, Dan Levy, Astrid Camille Pineda, Jose Ramon Morales Arilla and Matthew S. Johnson
    Supplements the (A) case. View Details
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    Toffel, Michael W., Dan Levy, Astrid Camille Pineda, Jose Ramon Morales Arilla, and Matthew S. Johnson. "Improving Worker Safety in the Era of Machine Learning (B)." Harvard Business School Supplement 618-064, April 2018. (Revised February 2019.)
    • Article

    Robust and Stable Black Box Explanations

    By: Himabindu Lakkaraju, Nino Arsov and Osbert Bastani
    As machine learning black boxes are increasingly being deployed in real-world applications, there has been a growing interest in developing post hoc explanations that summarize the behaviors of these black boxes. However, existing algorithms for generating such... View Details
    Keywords: Machine Learning; Black Box Models; Framework
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    Lakkaraju, Himabindu, Nino Arsov, and Osbert Bastani. "Robust and Stable Black Box Explanations." Proceedings of the International Conference on Machine Learning (ICML) 37th (2020): 5628–5638. (Published in PMLR, Vol. 119.)
    • Web

    Machine Learning frameworks (Tensorflow, PyTorch, Keras, OpenCV) - Research Computing Services

    Software Tools Machine Learning frameworks (Tensorflow, PyTorch, Keras, OpenCV) 20ms The HBSGrid offers artificial intelligence(AI) and machine... View Details
    • Teaching Interest

    Data Science and AI for Leaders

    By: Dennis Campbell
    Modern business increasingly relies... View Details
    Keywords: Artificial Intelligence; Data Science
    • 2025
    • Article

    Statistical Inference for Heterogeneous Treatment Effects Discovered by Generic Machine Learning in Randomized Experiments

    By: Kosuke Imai and Michael Lingzhi Li
    Researchers are increasingly turning to machine learning (ML) algorithms to investigate causal heterogeneity in randomized experiments. Despite their promise, ML algorithms may fail to accurately ascertain heterogeneous treatment effects under practical settings with... View Details
    Keywords: AI and Machine Learning; Mathematical Methods; Analytics and Data Science
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    Imai, Kosuke, and Michael Lingzhi Li. "Statistical Inference for Heterogeneous Treatment Effects Discovered by Generic Machine Learning in Randomized Experiments." Journal of Business & Economic Statistics 43, no. 1 (2025): 256–268.
    • October 2018
    • Article

    The Operational Value of Social Media Information

    By: Ruomeng Cui, Santiago Gallino, Antonio Moreno and Dennis J. Zhang
    While the value of using social media information has been established in multiple business contexts, the field of operations and supply chain management have not yet explored the possibilities it offers in improving firms' operational decisions. This study attempts to... View Details
    Keywords: Machine Learning; Information; Sales; Forecasting and Prediction; Social Media
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    Cui, Ruomeng, Santiago Gallino, Antonio Moreno, and Dennis J. Zhang. "The Operational Value of Social Media Information." Special Issue on Big Data in Supply Chain Management. Production and Operations Management 27, no. 10 (October 2018): 1749–1774.
    • 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... View Details
    Keywords: Artificial Intelligence; Diagnosis; Computer-assisted; Image Interpretation; Machine Learning; Radiography; Panoramic Radiograph; AI and Machine Learning
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    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).
    • Clubs

    AI Club

    Keywords: Technology
    • October 2023
    • Case

    Fixie and Conversational AI Sidekicks

    By: Jeffrey J. Bussgang and Carin-Isabel Knoop
    In March 2023, Fixie Co-Founder and Chief Architect Matt Welsh and co-founders had the kind of meeting no founders want to have. The president of leading artificial intelligence (AI) research and deployment firm OpenAI, which had catapulted into fame with its ChatGPT... View Details
    Keywords: Large Language Model; Entrepreneurship; Decision Choices and Conditions; AI and Machine Learning; Technological Innovation; Competitive Strategy; Technology Industry; United States
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    Bussgang, Jeffrey J., and Carin-Isabel Knoop. "Fixie and Conversational AI Sidekicks." Harvard Business School Case 824-037, October 2023.
    • Research Summary

    Overview

    Jenny is broadly interested in interpretable machine learning (ML), identity and inequality, and improving existing methods used to answer social and policy-relevant questions. Her recent projects have focused on developing tools that explore how LLMs are reshaping... View Details
    • April 2024
    • Article

    A Machine Learning Algorithm Predicting Risk of Dilating VUR among Infants with Hydronephrosis Using UTD Classification

    By: Hsin-Hsiao Scott Wang, Michael Lingzhi Li, Dylan Cahill, John Panagides, Tanya Logvinenko, Jeanne Chow and Caleb Nelson
    Backgrounds: Urinary Tract Dilation (UTD) classification has been designed to be a more objective grading system to evaluate antenatal and post-natal UTD. Due to unclear association between UTD classifications to specific anomalies such as vesico-ureteral reflux (VUR),... View Details
    Keywords: Health Disorders; Health Testing and Trials; AI and Machine Learning; Health Industry
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    Wang, Hsin-Hsiao Scott, Michael Lingzhi Li, Dylan Cahill, John Panagides, Tanya Logvinenko, Jeanne Chow, and Caleb Nelson. "A Machine Learning Algorithm Predicting Risk of Dilating VUR among Infants with Hydronephrosis Using UTD Classification." Journal of Pediatric Urology 20, no. 2 (April 2024): 271–278.
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