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cover of episode EP 475: AI Without Mistakes: How Good Data Makes It Happen

EP 475: AI Without Mistakes: How Good Data Makes It Happen

2025/3/5
logo of podcast Everyday AI Podcast – An AI and ChatGPT Podcast

Everyday AI Podcast – An AI and ChatGPT Podcast
AI Deep Dive AI Chapters Transcript
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B
Barr Moses
J
Jordan Wilson
一位经验丰富的数字策略专家和《Everyday AI》播客的主持人，专注于帮助普通人通过 AI 提升职业生涯。
Topics
Jordan Wilson: 我认为，在生成式AI领域，我们有时会忽略一些最重要的事情，那就是数据的可靠性。我们需要关注数据来源的可靠性、准确性以及数据出错后的应对措施。 Barr Moses: Monte Carlo的使命是通过减少数据停机时间（数据错误或不准确的时间段）来加速数据和AI的采用。数据产品（包括生成式AI应用、报告等）经常基于错误数据，数据团队常常是最后知道问题的人。数据可观察性不仅在于知道问题的存在，更在于理解问题的原因、重要性以及解决方法。小型和中型企业常常没有充分理解其数据如何运作，这会影响生成式AI的应用效果。数据准确性在数据使用量增加和生成式AI时代变得越来越重要，因为错误数据会导致用户流失和品牌声誉受损。大多数企业对用于生成式AI模型的数据缺乏信心，数据可靠性与品牌声誉和收入息息相关。企业数据是构建个性化生成式AI产品的竞争优势，数据质量和可靠性至关重要。即使是小型企业，数据质量也至关重要，不应该降低标准；小型企业在数据处理方面具有速度优势。高质量数据与生成式AI的结合可以产生有价值的应用，例如通过数据质量监控器推荐来提高数据质量。生成式AI可以用于分析体育数据，例如识别棒球投球类型和速度中的异常情况，从而提高数据质量监控的效率。Credit Karma利用其用户数据构建个性化金融产品，生成式AI与可靠数据的结合提高了用户体验和产品质量。许多组织利用生成式AI提高内部效率，例如编码助手、合规报告生成等。生成式AI还可以用于处理非结构化数据，例如客户支持聊天记录，将其结构化并进行分析。虽然合成数据在训练LLM和提高性能方面有潜力，但它无法替代企业所需的真实世界数据；数据治理在当前环境下变得越来越重要。生成式AI产品的质量取决于数据的质量，确保数据可靠性是至关重要的第一步。

Deep Dive

Chapters
This chapter explores the critical role of reliable data in generative AI. It emphasizes the importance of data trustworthiness and highlights the challenges of identifying and resolving data issues. The discussion also touches on the field of data observability and its importance in ensuring data reliability.
  • Data reliability is paramount for generative AI success.
  • Data downtime, periods of inaccurate data, significantly impacts AI applications.
  • Data observability helps identify and resolve data issues promptly.
  • Understanding the root cause of data problems is crucial for effective solutions.

Shownotes Transcript

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Your data is your moat. Everyone's got AI now. Find out how reliable data can make your competitive edge happen. Barr Moses, Co-Founder and CEO of Monte Carlo, joins us to discuss.Newsletter: Sign up for our free daily newsletter)**More on this Episode: **Episode Page)**Join the discussion: **Ask Jordan and Barr questions on AI and data)Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineup)Website: YourEverydayAI.com)Email The Show: [email protected])**Connect with Jordan on **LinkedIn)**Topics Covered in This Episode:1. the Importance of Data2. Challenges and Opportunities in Leveraging Data3. Adoption of Data Practices4. Data Use Case Examples5.Generative AI, LLMs, and Data IntegrationTimestamps:**00:00 Empower AI proficiency with daily insights.06:02 Data observability ensures reliability and issue resolution.07:15 Understanding data's importance is crucial for businesses.13:07 Personalized AI relies on unique enterprise data.15:20 Large enterprises struggle with data consistency, smaller teams advantage.19:42 Generative AI analyzes sports data for insights.22:56 Personalized financial products using reliable data.23:56 Credit Karma Intune boosts external and internal productivity.28:02 Peak data reached; synthetic data becomes crucial.30:36 Recap available on your everydayai.com.**Keywords:**Generative AI, Data Usage, Data Accuracy, High-Quality Data, AI Implementation, Brand Reputation, Small Business Data Management, Data Systems, Trusting Data Sources, Everyday AI Podcast, Microsoft Partnership, Barr Moses, Monte Carlo, Data Downtime, Data Issues, Data Products, Data Observability, Data Adoption Forecast, Smaller Team Advantages, Microsoft WorkLab Podcast, Data Quality Monitor Recommendations, AI and Data Integration, Personalized Financial Products, Coding Assistants, AI for Compliance Reporting, Large Language Models, Synthetic Data, Real-World Data, Data Governance, Data Quality Management.

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