Why data matters | The right data for the right objective with AI

The AI Fundamentalists

Episode 3. Get ready because we're bringing stats back! An AI model can only learn from the data it has seen. And business problems can’t be solved without the right data. The Fundamentalists break down the basics of data from collection to regulation to bias to quality in AI.

• Introduction to this episode
  • Why data matters.
• How do big tech's LLM models stack up to the proposed EU AI Act?
  • How major models such as Open AI and Bard stack up against current regulations.
  • Stanford HAI - Do Foundation Model Providers Comply with the Draft EU AI Act?
  • Risk management documentation and risk management.
• The EU is adding teeth outside of the banking and financial sectors now.
  • Time - Exclusive: OpenAI Lobbied the E.U. to Water Down AI Regulation
• Bringing stats back: Why does data matter in all this madness?
  • How AI is taking us away from human intelligence.
  • Having quality data and bringing stats back!
  • The importance of having representative data, sampling data
• What are your business objectives? Don’t just throw data into it.
  • Understanding the use case of the data.
  • GDPR and EU AI regulations.
  • AI field caught off guard by new regulations.
  • Expectations for regulatory data.
• What is data governance? How do you validate data?
  • Data management, data governance, and data quality.
  • Structured data collection for financial companies.
• What else should we learn about our data collection and data processes?
  • Example: US Census data collection and data processes.
  • The importance of representativeness and being representative of the community in the census.
  • Step one, the fine curation of data, the intentional and knowledgeable creation of data that meets the specific business need.
  • Step two, fairness through awareness.
• The importance of data curation and data selection in data quality.
  • What data quality looks like at a high level.
  • Rights to be forgotten.
• The importance of data provenance and data governance in data science.
  • Synthetic data and privacy.
• Data governance seems to be 40 % of the path to AI model governance. What else needs to be in place?
  • What companies are missing with machine learning.
  • The impact that data will have on the future of AI.
  • The future of general AI in the future.

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