• Summary

  • ibl.ai is a generative AI education platform based in NYC. This podcast, curated by its CTO, Miguel Amigot, focuses on high-impact trends and reports about AI.
    Copyright 2024 All rights reserved.
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Episodes
  • Stanford: Tutor CoPilot – A Human-AI Approach for Scaling Real-Time Expertise
    Nov 13 2024

    Summary of https://studentsupportaccelerator.org/sites/default/files/Tutor_CoPilot.pdf

    This paper describes the development and evaluation of "Tutor CoPilot," a human-AI system designed to improve the quality of tutoring sessions, particularly for novice tutors working with K-12 students from historically underserved communities.

    The system leverages large language models (LLMs) trained on expert thinking to generate real-time, expert-like suggestions for tutors, providing them with guidance on how to respond to student questions and mistakes.

    The research utilizes a randomized controlled trial with over 900 tutors and 1,800 students, demonstrating that Tutor CoPilot significantly improves student learning outcomes, particularly for lower-rated tutors.

    Additionally, the study finds that tutors using Tutor CoPilot are more likely to use high-quality pedagogical strategies that foster deeper understanding.

    This approach offers a scalable and cost-effective alternative to traditional training programs, making high-quality education more accessible to all students.

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    13 mins
  • MIT: Artificial Intelligence, Scientific Discovery, and Product Innovation
    Nov 6 2024

    Summary of https://conference.nber.org/conf_papers/f210475.pdf

    This research paper examines the impact of an artificial intelligence (AI) tool on materials discovery in the R&D lab of a large U.S. firm.

    The AI tool, which leverages deep learning to partially automate the materials discovery process, was rolled out to scientists in three waves, allowing the researchers to analyze the effects of the technology. The study found that the AI tool significantly accelerated materials discovery, resulting in an increase in patent filings and product prototypes, particularly for scientists with strong initial productivity.

    However, the tool's effectiveness depended on the scientist's ability to evaluate the AI-generated compounds, highlighting the importance of human judgment in the scientific discovery process.

    The paper concludes by exploring the AI tool's impact on scientist job satisfaction and beliefs about artificial intelligence, revealing that while the tool enhances productivity, it also leads to changes in the types of tasks scientists perform, potentially affecting job satisfaction and prompting a need for reskilling.

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    20 mins
  • Microsoft/GitHub: Generative AI and The Nature of Work
    Nov 5 2024

    Summary of https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5007084

    This working paper investigates the impact of generative AI on the nature of work, specifically focusing on software development. The authors study the introduction of GitHub Copilot, an AI-powered code completion tool, within the open-source software development ecosystem.

    They use a natural experiment based on GitHub's "top developer" program, which provides free Copilot access to developers of the most popular repositories. Through a regression discontinuity design, they find that access to Copilot induces developers to allocate more time towards coding activities and less towards project management.

    This shift is driven by two mechanisms: an increase in autonomous work and an increase in exploration activities. The authors also find that the effects are greater for developers with lower ability, suggesting that generative AI has the potential to flatten organizational hierarchies and reduce inequality in the knowledge economy.

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    15 mins

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