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AI Applications for the TIC industry (Part 2)
In this episode of the Changing Conversations podcast, host Daniel Tatarsky discusses AI deployment, governance, and strategic implementation with experts
Joost Vos from IBM and Nicola Colombo, and Tzveta Iordanova from SGS. They emphasize the importance of identifying valuable AI opportunities, integrating AI into existing services, and maintaining human decision control. The conversation highlights the need for robust governance to ensure AI systems are safe, ethical, and compliant. Future AI trends, such as multi-model strategies and virtual agents, are explored. The episode concludes with a focus on balancing AI automation with human expertise and the importance of continuous learning and critical thinking.
Identifying valuable AI opportunities (00:00:56) Discussion on identifying valuable AI opportunities within specific services and markets, including the integration of AI into existing services.
Importance of Governance in AI implementation (00:04:21) The significance of governance in directing, managing, and controlling AI activities to ensure efficient deployment and adoption.
AI Governance and ethical considerations (00:05:22) The role of AI governance in ensuring safe, ethical, and fair AI systems, including the need to avoid bias, discrimination, and unfair behavior.
Data quality and model selection (00:10:02) The importance of data quality in AI, including the impact of incomplete, inconsistent, or biased data on AI model accuracy and precision.
Future trends in AI development (00:12:34) Discussion on multi-model AI strategies, model customization, and chaining of models, as well as the potential for powerful virtual agents and enterprise-grade trustworthy solutions.
Balancing AI automation and human expertise (00:21:21) Exploring the evolving balance between AI automation and human expertise in the tech industry, emphasizing the need for a co-pilot approach and maintaining human control in AI implementation.
Quality of data and pragmatism (00:22:49) Discussion on dealing with data quality and pragmatic approaches to business use cases.
Experimenting with AI (00:23:19) Importance of experimenting with AI, developing a solid strategy, and managing the complete lifecycle from data to model deployment.
Emerging technology and risks (00:23:56) Exploration of generative AI as an emerging technology, its opportunities and risks, and the importance of critical thinking in leveraging AI.
Looking ahead and new series announcement (00:24:56) Announcement of a new series on tech for AI, which will explore AI regulations, their impact on companies, and the challenges for the tech industry.
View all episodes
By SGSIn this episode of the Changing Conversations podcast, host Daniel Tatarsky discusses AI deployment, governance, and strategic implementation with experts
Joost Vos from IBM and Nicola Colombo, and Tzveta Iordanova from SGS. They emphasize the importance of identifying valuable AI opportunities, integrating AI into existing services, and maintaining human decision control. The conversation highlights the need for robust governance to ensure AI systems are safe, ethical, and compliant. Future AI trends, such as multi-model strategies and virtual agents, are explored. The episode concludes with a focus on balancing AI automation with human expertise and the importance of continuous learning and critical thinking.
Identifying valuable AI opportunities (00:00:56) Discussion on identifying valuable AI opportunities within specific services and markets, including the integration of AI into existing services.
Importance of Governance in AI implementation (00:04:21) The significance of governance in directing, managing, and controlling AI activities to ensure efficient deployment and adoption.
AI Governance and ethical considerations (00:05:22) The role of AI governance in ensuring safe, ethical, and fair AI systems, including the need to avoid bias, discrimination, and unfair behavior.
Data quality and model selection (00:10:02) The importance of data quality in AI, including the impact of incomplete, inconsistent, or biased data on AI model accuracy and precision.
Future trends in AI development (00:12:34) Discussion on multi-model AI strategies, model customization, and chaining of models, as well as the potential for powerful virtual agents and enterprise-grade trustworthy solutions.
Balancing AI automation and human expertise (00:21:21) Exploring the evolving balance between AI automation and human expertise in the tech industry, emphasizing the need for a co-pilot approach and maintaining human control in AI implementation.
Quality of data and pragmatism (00:22:49) Discussion on dealing with data quality and pragmatic approaches to business use cases.
Experimenting with AI (00:23:19) Importance of experimenting with AI, developing a solid strategy, and managing the complete lifecycle from data to model deployment.
Emerging technology and risks (00:23:56) Exploration of generative AI as an emerging technology, its opportunities and risks, and the importance of critical thinking in leveraging AI.
Looking ahead and new series announcement (00:24:56) Announcement of a new series on tech for AI, which will explore AI regulations, their impact on companies, and the challenges for the tech industry.