Unlock the secrets to AI's modeling paradigms. We emphasize the importance of modeling practices, how they interact, and how they should be considered in relation to each other before you act. Using the right tool for the right job is key. We hope you enjoy these examples of where the greatest AI and machine learning techniques exist in your routine today.

More AI agent disruptors (0:56)

• Proxy from London start-up Convergence AI
• Another hit to OpenAI, this product is available for free, unlike OpenAI’s Operator.

AI Paris Summit - What's next for regulation? (4:40)

• [Vice President] Vance tells Europeans that heavy regulation can kill AI
• US federal administration withdrawing from the previous trend of sweeping big tech regulation on modeling systems.
• The EU is pushing to reduce bureaucracy but not regulatory pressure

Modeling paradigms explained (10:33)

• As companies look for an edge in high-stakes computations, we’ve seen best-in-class rediscovering expert system-based techniques that, with modern computing power, are breathing new light into them.
  • Paradigm 1: Agents (11:23)
  • Paradigm 2: Generative (14:26)
  • Paradigm 3: Mathematical optimization (regression) (18:33)
  • Paradigm 4: Predictive (classification) (23:19)
  • Paradigm 5: Control theory (24:37)

The right modeling paradigm for the job? (28:05)

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Agentic AI is the latest foray into big-bet promises for businesses and society at large. While promising autonomy and efficiency, AI agents raise fundamental questions about their accuracy, governance, and the potential pitfalls of over-reliance on automation.

Does this story sound vaguely familiar? Hold that thought. This discussion about the over-under of certain promises is for you.

Show Notes

The economics of LLMs and DeepSeek R1 (00:00:03)

• Reviewing recent developments in AI technologies and their implications
• Discussing the impact of DeepSeek’s R1 model on the AI landscape, NVIDIA

The origins of agentic AI (00:07:12)

• Status quo of AI models to date: Is big tech backing away from promise of generative AI?
• Agentic AI designed to perceive, reason, act, and learn

Governance and agentic AI (00:13:12)

• Examining the tension between cost efficiency and performance risks [LangChain State of AI Agents Report]
• Highlighting governance concerns related to AI agents

Issues with agentic AI implementation (00:21:01)

• Considering the limitations of AI agents and their adoption in the workplace
• Analyzing real-world experiments with AI agent technologies, like Devin

What's next for complex and agentic AI systems (00:29:27)

• Offering insights on the cautious integration of these systems in business practices
• Encouraging a thoughtful approach to leveraging AI capabilities for measurable outcomes

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What if privacy could be as dynamic and socially aware as the communities it aims to protect? Sebastian Benthall, a senior research fellow from NYU’s Information Law Institute, shows us how privacy is complex. He uses Helen Nissenbaum’s work with contextual integrity and concepts in differential privacy to explain the complexity of privacy. Our talk explains how privacy is not just about protecting data but also about following social rules in different situations, from healthcare to education. These rules can change privacy regulations in big ways.

Show notes

Intro: Sebastian Benthall (0:03)

• Research: Designing Fiduciary Artificial Intelligence (Benthall, Shekman)
• Integrating Differential Privacy and Contextual Integrity (Benthall, Cummings)

Exploring differential privacy and contextual integrity (1:05)

• Discussion about the origins of each subject
• How are differential privacy and contextual integrity used to enforce each other?

Accepted context or legitimate context? (9:33)

• Does context develop from what society accepts over time?
• Approaches to determine situational context and legitimacy

Next steps in contextual integrity (13:35)

• Is privacy as we know it ending?
• Areas where integrated differential privacy and contextual integrity can help (Cummings)

Interpretations of differential privacy (14:30)

• Not a silver bullet
• New questions posed from NIST about its application

Privacy determined by social norms (20:25)

• Game theory and its potential for understanding social norms

Agents and governance: what will ultimately decide privacy? (25:27)

• Voluntary disclosures and the biases it can present towards groups that are least concerned with privacy
• Avoiding self-fulfilling prophecy from data and context

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