Summary

In this conversation, Maribel Lopez speaks with Ivana Bartoletti, the Global Privacy Chief Officer at Wipro, about the intersection of AI, privacy, and governance. They discuss the transformative impact of generative AI, the importance of embedding ethics in AI development, and the role of synthetic data in mitigating bias. Ivana also shares insights on the Audrey initiative aimed at promoting human rights in the digital age and highlights common mistakes in AI regulation. The conversation concludes with a positive outlook on the collaborative efforts to build fair and responsible AI.

Takeaways

• Public trust is essential to harness AI's benefits.
• Generative AI is transforming how we live and work.
• Privacy is a crucial collective good that must be respected.
• Ethics in AI goes beyond compliance with laws.
• AI should retain human agency and decision-making.
• Bias in algorithms can perpetuate social inequalities.
• Synthetic data can help mitigate bias but has limitations.
• Transparency in data usage is vital for equity.
• AI regulation should not be seen as opposing innovation.
• Collaboration across sectors is key to responsible AI governance.

You can follow Ivana here: https://www.linkedin.com/in/ivana-bartoletti-77b2b29/

You can follow me here:

https://www.linkedin.com/in/maribellopez/

https://www.youtube.com/@AIwithMaribelLopez

https://x.com/MaribelLopez

Episode Overview:

Maribel Lopez speaks with Tim Marklein, CEO of Big Valley Marketing, about how AI is changing marketing and communications. The conversation explores the practical applications, limitations, and future of AI in the marketing landscape.

Guest:

Tim Marklein - CEO of Big Valley Marketing, an award-winning consulting firm that helps technology companies grow and win in various markets including software infrastructure, AI, cybersecurity, digital health, and supply chain.

Key Topics Discussed:

• Current state of AI adoption in marketing: Despite surveys showing varied adoption rates, most professionals are still "dabbling" with AI rather than fully integrating it into workflows
• Three key areas where AI is proving valuable:
  • As a search alternative for market insights
  • For pattern analysis and audience research
  • For writing and editing assistance
• The continued importance of original thinking: AI can't replace a company's unique point of view, especially in B2B contexts where buyers want to understand a company's beliefs and perspectives
• Brand differentiation concerns: Discussion about whether widespread AI adoption might lead to homogenized marketing content and brand positioning
• AI for audience targeting: How AI can help with audience research but cannot replace strategic decisions about which audiences to prioritize
• Workflow integration challenges: The disconnect between the ideal AI tools and those integrated into existing workflows
• AI and marketing metrics: How AI primarily makes it easier to capture existing metrics rather than creating new ones
• Authenticity and ethics: The research showing that simply disclosing AI use doesn't build trust when 80% of people don't trust AI to begin with
• Appropriate vs. responsible use: The importance of communicating who is using AI and why, not just how it's being used
• Skills development for the AI era: The value of experimentation and curiosity over becoming a dedicated "prompt engineer"

You can follow Tim Marklein, the Founder and CEO, Big Valley Marketing ( bigvalley.co) at LinkedIn: https://www.linkedin.com/in/tmarklein/ X: @tmarklein

You can follow me at:

https://www.linkedin.com/in/maribellopez/

https://www.youtube.com/@AIwithMaribelLopez

https://x.com/MaribelLopez

Episode Summary

In this episode, Maribel Lopez interviews Kate Soule, Director of Technical Product Management for IBM's Granite products. They discuss IBM's third-generation AI models, their focus on efficiency and enterprise readiness, and the latest advancements including vision capabilities and reasoning features.

Guest

Kate Soule - Director of Technical Product Management for IBM's Granite products

Key Topics & Timestamps

00:04 - Introduction

• Maribel introduces the show and Kate Soule
• Brief overview of IBM Granite as fit-for-purpose, open-source enterprise AI models

00:48 - What is IBM Granite?

• Designed as core building blocks for enterprises building with generative AI
• Focus on efficiency with smaller model sizes
• Monthly innovation updates to keep pace with rapidly evolving field

02:19 - Understanding AI Reasoning

• Explanation of reasoning capabilities in AI models
• How allowing models to generate more text at inference time can improve performance
• Cost/benefit tradeoffs of reasoning features

03:13 - Enterprise AI Model Selection Criteria

• Moving beyond "one model to rule them all" thinking
• Importance of fit-for-purpose models
• Why smaller models can be customized more easily
• Trust and transparency considerations

05:38 - AI Governance and Safety

• How to evaluate models for governance requirements
• Safety evaluations and benchmarks as table stakes
• Systems-based approach to safety with guardrails
• IBM's Granite Guardian and protection mechanisms

08:55 - Benefits of Smaller Models

• Why size matters: cost, latency, and customization advantages
• Smaller models are easier to customize and require less computing power
• IBM's transparent approach to training data

10:13 - Future of AI Evaluation

• Performance per cost becoming the key evaluation metric
• The growing importance of flexibility in model selection
• How the "efficient frontier" between cost and performance will differentiate providers

12:41 - IBM's Vision Models

• IBM's pragmatic enterprise focus for multimodal capabilities
• Vision understanding (image in, text out) for practical business use cases
• Specialization for documents, charts, and dashboards
• Delivering powerful capabilities in only 2 billion parameters

15:25 - Understanding Model Size Context

• Evolution from millions to billions of parameters
• Practical considerations of deploying different-sized models
• Finding the right cost-benefit trade-off for specific use cases