Did we intentionally film an AI episode in front of robot monster art? Maybe…

As machine learning becomes more prevalent, the industry is actively exploring how to leverage AI's capabilities to its advantage. On the newest episode of Pipeline Things, Christopher De Leon and Rhett Dotson are joined by Adrian Belanger as they explore the application of machine learning and his IPC 2022 paper: “Not All Data Is Good Data: The Challenges of Using Machine Learning ILI.”

While machine learning efficiently analyzes data and uncovers patterns for analysts, Rhett, Chris, and Adrian discuss the critical role that quality inputs play in actionable outputs. Bad data can arise from mismatches between ILI data and field data, or from incomplete data that has been excluded. Belanger discusses the importance of screening data and looking for anomalies in data when utilizing AI.

Rhett, Chris, and Adrian discuss the growth and use of machine learning, what good data is, and how machine learning can effectively be utilized in the industry.

Highlights:

• What type of data is fed into machine learning, and what goes wrong?
• Is the future of machine learning to replace jobs in pipeline integrity?
• Is parameter data leveraged in machine learning rather than just ILI signal data?
• What does good and bad data look like from Adrian’s perspective?
• What are the common mistakes with data?

Connect:

Rhett Dotson

Christopher De Leon

D2 Integrity

Adrian Belanger

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Pipeline Things is presented by D2 Integrity and produced by ADV Marketing.

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