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  • All HBS Web  (1,222)
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← Page 18 of 1,222 Results →
  • January 2021
  • Case

Anodot: Autonomous Business Monitoring

By: Antonio Moreno and Danielle Golan
Autonomous business monitoring platform Anodot leveraged machine learning to provide real-time alerts regarding business anomalies. Anodot’s solution was used in various industries in order to primarily monitor business health, such as revenue and payments, product... View Details
Keywords: Digital Platforms; Internet and the Web; Knowledge Sharing; Information Management; Sales; Value Creation; Product Positioning; Israel
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Moreno, Antonio, and Danielle Golan. "Anodot: Autonomous Business Monitoring." Harvard Business School Case 621-084, January 2021.

    Linda A. Hill

    Linda A. Hill is the Wallace Brett Donham Professor of Business Administration at the Harvard Business School and Faculty Chair of the Leadership Initiative. Hill is regarded as one of the top experts on leadership and innovation. Hill is... View Details

    • 2023
    • Article

    Probabilistically Robust Recourse: Navigating the Trade-offs between Costs and Robustness in Algorithmic Recourse

    By: Martin Pawelczyk, Teresa Datta, Johannes van-den-Heuvel, Gjergji Kasneci and Himabindu Lakkaraju
    As machine learning models are increasingly being employed to make consequential decisions in real-world settings, it becomes critical to ensure that individuals who are adversely impacted (e.g., loan denied) by the predictions of these models are provided with a means... View Details
    Keywords: AI and Machine Learning; Decision Choices and Conditions; Mathematical Methods
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    Pawelczyk, Martin, Teresa Datta, Johannes van-den-Heuvel, Gjergji Kasneci, and Himabindu Lakkaraju. "Probabilistically Robust Recourse: Navigating the Trade-offs between Costs and Robustness in Algorithmic Recourse." Proceedings of the International Conference on Learning Representations (ICLR) (2023).
    • 27 Oct 2016
    • HBS Seminar

    Andrea Pratt, Richard Paul Richman Professor of Business and Professor of Economics, Columbia University

    • 2023
    • Article

    Experimental Evaluation of Individualized Treatment Rules

    By: Kosuke Imai and Michael Lingzhi Li
    The increasing availability of individual-level data has led to numerous applications of individualized (or personalized) treatment rules (ITRs). Policy makers often wish to empirically evaluate ITRs and compare their relative performance before implementing them in a... View Details
    Keywords: Causal Inference; Heterogeneous Treatment Effects; Precision Medicine; Uplift Modeling; Analytics and Data Science; AI and Machine Learning
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    Imai, Kosuke, and Michael Lingzhi Li. "Experimental Evaluation of Individualized Treatment Rules." Journal of the American Statistical Association 118, no. 541 (2023): 242–256.
    • 18 May 2009
    • Research & Ideas

    The Unseen Link Between Savings and National Growth

    between savings and growth through investment. This link, however, disappears in open economy models, which is surely the relevant scenario in reality. An alternative interpretation of the relationship between savings and growth is that... View Details
    Keywords: by Sarah Jane Gilbert
    • 2021
    • Article

    To Thine Own Self Be True? Incentive Problems in Personalized Law

    By: Jordan M. Barry, John William Hatfield and Scott Duke Kominers
    Recent years have seen an explosion of scholarship on “personalized law.” Commentators foresee a world in which regulators armed with big data and machine learning techniques determine the optimal legal rule for every regulated party, then instantaneously disseminate... View Details
    Keywords: Personalized Law; Regulation; Regulatory Avoidance; Regulatory Arbitrage; Law And Economics; Law And Technology; Law And Artificial Intelligence; Futurism; Moral Hazard; Elicitation; Signaling; Privacy; Law; Governing Rules, Regulations, and Reforms; Information Technology; AI and Machine Learning
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    Barry, Jordan M., John William Hatfield, and Scott Duke Kominers. "To Thine Own Self Be True? Incentive Problems in Personalized Law." Art. 2. William & Mary Law Review 62, no. 3 (2021).
    • July 2024
    • Technical Note

    What Is AI?

    By: Michael Parzen and Jo Ellery
    This note discusses definitions of artificial intelligence and covers the broad types of learning used in training AI, as well as explaining in detail how neural networks are built, trained, and used. View Details
    Keywords: AI and Machine Learning
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    Parzen, Michael, and Jo Ellery. "What Is AI?" Harvard Business School Technical Note 625-010, July 2024.
    • 2024
    • Working Paper

    Igniting Innovation: Evidence from PyTorch on Technology Control in Open Collaboration

    By: Daniel Yue and Frank Nagle
    Many companies offer free access to their technology to encourage outside addon innovation, hoping to later profit by raising prices or harnessing the power of the crowd while continuing to steer the direction of innovation. They can achieve this balance by opening... View Details
    Keywords: Technological Innovation; Power and Influence; Collaborative Innovation and Invention; Corporate Governance
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    Yue, Daniel, and Frank Nagle. "Igniting Innovation: Evidence from PyTorch on Technology Control in Open Collaboration." Harvard Business School Working Paper, No. 25-013, September 2024.

      Work‐from‐anywhere: The productivity effects of geographic flexibility

      An emerging form of remote work allows employees to work‐from‐anywhere, so that the worker can choose to live in a preferred geographic location. While traditional work‐from‐home (WFH) programs offer the worker temporal flexibility,... View Details
      • December 2014
      • Article

      The Discipline of Business Experimentation

      By: Stefan Thomke and Jim Manzi
      The data you already have can't tell you how customers will react to innovations. To discover if a truly novel concept will succeed, you must subject it to a rigorous experiment. In most companies, tests do not adhere to scientific and statistical principles. As a... View Details
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      Thomke, Stefan, and Jim Manzi. "The Discipline of Business Experimentation." Harvard Business Review 92, no. 12 (December 2014): 70–79.
      • August 2017 (Revised July 2019)
      • Case

      GROW: Using Artificial Intelligence to Screen Human Intelligence

      By: Ethan Bernstein, Paul McKinnon and Paul Yarabe
      Over 10% of all 2017 university graduates in Japan used GROW, an artificial intelligence platform and mobile app developed by Tokyo-based people analytics startup IGS, to recruit for a job. This case puts participants in the shoes of IGS founder and CEO Masahiro... View Details
      Keywords: Big Data; Artificial Intelligence; Talent and Talent Management; Recruitment; Selection and Staffing; Human Resources; Information Technology; AI and Machine Learning; Analytics and Data Science; Financial Services Industry; Air Transportation Industry; Advertising Industry; Manufacturing Industry; Technology Industry; Japan
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      Bernstein, Ethan, Paul McKinnon, and Paul Yarabe. "GROW: Using Artificial Intelligence to Screen Human Intelligence." Harvard Business School Case 418-020, August 2017. (Revised July 2019.)
      • 15 Oct 2001
      • Op-Ed

      Lessons from the Rubble

      Pundits and investors spoke giddily of the end of national borders, of markets that spanned the globe and replaced the hefty weight of machines and plants with ephemeral bits of information. This may be true. We do have global markets and... View Details
      Keywords: by Debora L. Spar
      • 2021
      • Article

      ThreeDWorld: A Platform for Interactive Multi-Modal Physical Simulation

      By: Chuang Gan, Jeremy Schwartz, Seth Alter, Damian Mrowca, Martin Schrimpf, James Traer, Julian De Freitas, Jonas Kubilius, Abhishek Bhandwaldar, Nick Haber, Megumi Sano, Kuno Kim, Elias Wang, Michael Lingelbach, Aidan Curtis, Kevin Feigelis, Daniel M. Bear, Dan Gutfreund, David Cox, Antonio Torralba, James J. DiCarlo, Joshua B. Tenenbaum, Josh H. McDermott and Daniel L.K. Yamins
      We introduce ThreeDWorld (TDW), a platform for interactive multi-modal physical simulation. TDW enables simulation of high-fidelity sensory data and physical interactions between mobile agents and objects in rich 3D environments. Unique properties include: real-time... View Details
      Keywords: Artificial Intelligence; Platform; Interactive Physical Simulation; Virtual Environment; Multi-modal; AI and Machine Learning
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      Gan, Chuang, Jeremy Schwartz, Seth Alter, Damian Mrowca, Martin Schrimpf, James Traer, Julian De Freitas, Jonas Kubilius, Abhishek Bhandwaldar, Nick Haber, Megumi Sano, Kuno Kim, Elias Wang, Michael Lingelbach, Aidan Curtis, Kevin Feigelis, Daniel M. Bear, Dan Gutfreund, David Cox, Antonio Torralba, James J. DiCarlo, Joshua B. Tenenbaum, Josh H. McDermott, and Daniel L.K. Yamins. "ThreeDWorld: A Platform for Interactive Multi-Modal Physical Simulation." Conference on Neural Information Processing Systems (NeurIPS), Datasets and Benchmarks Track 35th (2021).
      • 22 Oct 2007
      • Research & Ideas

      Bringing ‘Lean’ Principles to Service Industries

      and efficiency, lean's influence (and various interpretations of its tenets) continues to grow. In their working paper "Lean Principles and Software Production: Evidence from Indian Software Services," HBS doctoral student... View Details
      Keywords: by Julia Hanna; Computer
      • June 2024
      • Article

      Oral History and Business History in Emerging Markets

      By: Geoffrey Jones
      This article describes the motivation, structure and use of the Creating Emerging Markets (CEM) oral history-based project at the Harvard Business School. The project consists of lengthy interviews with business leaders from emerging markets. By June 2024 183... View Details
      Keywords: Emerging Economies; Oral History; Emerging Markets; Business History; Research
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      Jones, Geoffrey. "Oral History and Business History in Emerging Markets." Investigaciones de historia económica 20, no. 2 (June 2024): 1–4.

        The Discipline of Business Experimentation

        The data you already have can't tell you how customers will react to innovations. To discover if a truly novel concept will succeed, you must subject it to a rigorous experiment. In most companies, tests do not adhere to scientific and statistical principles. As... View Details
        • 12 Nov 2015
        • Blog Post

        3 Facts About HBS Discussion Groups

        different strengths and weaknesses learn from each other to better prepare for class. By reviewing interpretations and numbers for different cases, students can make sure they are on top of the material and... View Details
        • 2021
        • Working Paper

        An Empirical Study of Time Allotment and Delays in E-commerce Delivery

        By: M. Balakrishnan, MoonSoo Choi and Natalie Epstein
        Problem definition: We study how having more time allotted to deliver an order affects the speed of the delivery process. Furthermore, we seek to predict orders that are likely to be delayed early in the delivery process so that actions can be taken to avoid delays.... View Details
        Keywords: Logistics; E-commerce; Mathematical Methods; AI and Machine Learning; Performance Productivity
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        Balakrishnan, M., MoonSoo Choi, and Natalie Epstein. "An Empirical Study of Time Allotment and Delays in E-commerce Delivery." Working Paper, December 2021.
        • 2020
        • Working Paper

        Design in the Age of Artificial Intelligence

        By: Roberto Verganti, Luca Vendraminelli and Marco Iansiti
        Artificial Intelligence (AI) is affecting the scenario in which innovation takes place. What are the implications for our understanding of design? Is AI just another digital technology that, akin to many others, will not significantly question what we know about... View Details
        Keywords: Artificial Intelligence; Design Thinking; Technological Innovation; Design; Change; Theory; AI and Machine Learning
        Citation
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        Verganti, Roberto, Luca Vendraminelli, and Marco Iansiti. "Design in the Age of Artificial Intelligence." Harvard Business School Working Paper, No. 20-091, February 2020.
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