AI Today Podcast

In this podcast, Sergio Villanueva-Meyer, an AI in Architecture, Engineering, and Construction (AEC) expert, discusses the implementation of AI tools in the construction industry. The speakers highlight the importance of understanding business objectives, integrating design and construction methods, and the challenges faced in integrating software tools. They also explore the benefits of getting certified in the CPM AI methodology and emphasize the importance of collaboration and team effort in AI projects.

The podcast explores the importance of explainable and interpretable AI, including the need for verifiable explanations, algorithmic transparency, and debugging AI systems. It discusses the significance of understanding root cause explanations and the challenges in achieving explainability. The chapter also highlights the difference between black box and white box algorithms and the importance of choosing the right algorithmic approach. It concludes by discussing trustworthy AI resources, upcoming interviews, and listener feedback.

The hosts emphasize the significance of establishing a common vocabulary for effective communication within teams. They discuss the importance of understanding and explaining AI systems, introducing 'black box' technology and 'explainable AI'. They explore the concept of explainable AI (XAI) and the trade-off between explainability and accuracy. They also discuss interpretable AI, algorithmic transparency, and ethical AI.

Jonelle Martinez, an expert in AI project management, shares insights on the challenges and consequences of AI projects, including the importance of skills, clean data, and resources. They discuss the future of AI and its application to organizations, highlighting potential benefits and expressing concerns about ethics and data privacy. They also talk about promoting feedback, project management resources, and more AI content on the podcast.

The hosts discuss the importance of understanding the challenges and drawbacks of generative AI. They explore the positive applications of generative AI and discuss its limitations and ethical concerns. They also highlight the difficulties associated with utilizing large language models in generative AI. The episode concludes with information about upcoming episodes and resources for AI enthusiasts.

The hosts discuss the benefits of staying informed about AI terminology and share a story about a data engineer. They introduce terms related to data in AI and discuss data engineering, data feed, data integration, and data pipeline. They explore data wrangling, pipelines, and data governance, emphasizing their importance. They also discuss AI terms, introduce the CPM AI methodology, and highlight the availability of a free intro course and CPM AI certification.

The podcast discusses the importance of governance in AI and the need for controls, processes, and organizational structure. It emphasizes trustworthy AI practices, including system auditability and risk management. The chapter also explores the importance of building trust in AI systems through an appeals process and transparency. Additionally, it discusses the significance of establishing controls, compliance, and data governance to ensure trustworthiness. The podcast highlights the importance of monitoring and managing AI systems to maintain their trustworthiness.

Hosts Kathleen Walch and Ron Schmelzer define terms like Data Science, Data Scientist, Citizen Data Scientist / Citizen Developer, and Data Custodian, discussing their importance and how they relate to AI. They highlight the skills and responsibilities of data scientists, as well as the concept of citizen data scientists and data custodians.

The podcast explores the concept of generative AI and its impact on organizations. They discuss the use of foundation models and the benefits of fine-tuning these models for specific applications. They also showcase Langchain, a system for building NLP applications on top of language models. Additionally, they touch on the CPM AI training and certification program and its topics.

This podcast episode discusses the importance of understanding analytics and data visualization in AI projects. It explains key terms such as descriptive, diagnostic, predictive, and projective analytics and highlights the tools and methods used for each. Understanding these concepts is crucial to the success of AI projects.