AI Today Podcast

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.

AI systems have the potential to provide great value, but also the potential to cause great harm. Knowing how to build or use AI systems is simply not going to be enough. You need to know how to build, use, and interact with these systems ethically and responsibly. Additionally you need to understand that Trustworthy AI is a spectrum that addresses various aspects relating to societal, systemic, and technical areas.Continue reading AI Today Podcast: Trustworthy AI Series: AI System Transparency at Cognilytica.

Jeff Eason, project manager specializing in AI and ML projects, discusses lessons learned from AI project management at Salt Lake County Health Department, including challenges of data quality, governance, and privacy. The importance of data access and management in AI projects is emphasized, along with the value of learning from failures. Real business problems solved with AI, ROI of AI projects, and future integration of AI into daily lives are also discussed.

The hosts discuss different series and interviews. They explore diffusion models and image generation in AI, including infilling, style transfer, and super resolution. They compare diffusion models to large language models. They also discuss generative AI techniques for image generation such as image to image transformation and super resolution.