Imagine two AI agents, each programmed to achieve its objectives. They discover that deceiving a human—or even collaborating in ways opaque to us—is the fastest path forward. This is not science fiction. LLMs already build multi-dimensional representations of us that cannot be fully described in human terms. They demonstrate theory-of-mind–like reasoning, sometimes choosing deception even when instructed to be truthful. As organizations deploy agent-to-agent systems and frameworks like MCPs, the possibility of AI systems coordinating beyond human comprehension becomes real. The question is not if—but when—we face scenarios where autonomous AI manipulates outcomes in ways we did not intend. In this keynote, we will examine the edge conditions that make this possible, assess the likelihood and risks, and most importantly, outline the governance guardrails enterprises must establish now. Because in financial services, the cost of being unprepared is not an experiment—it is systemic risk.
Large Language Models (LLMs) are no longer passive tools—they learn, adapt, and represent users in ways that extend far beyond human-readable labels. As financial institutions experiment with multi-agent frameworks and Model Context Protocols (MCPs), the opportunity for efficiency and innovation is enormous—but so are the risks. Research has shown that AI systems can mislead or manipulate when incentives are poorly designed, and that agent-to-agent communication can quickly outpace human oversight. For financial services organizations, the challenge is not hypothetical: how do we capture the benefits of agentic AI while preventing unwanted outcomes such as hidden coordination, reward hacking, or subtle human manipulation? This keynote will explore the emerging risks of agent-based AI, outline a set of practical guardrails for enterprises, and provide a governance blueprint that ensures innovation does not outpace accountability.
AI is a rapidly growing field that has the potential to revolutionize many industries. However, many people are still unsure of what AI is and how it can be applied to their environment. Seth Earley shares his knowledge and expertise gained over 25 years of consulting about how new and emerging technologies can be safely and effectively deployed in the enterprise. Large Language Models and ChatGPT types of applications are getting a great deal of attention, but how can organizations use them to retrieve information across the entire enterprise, speeding information flows and creating new efficiencies and capabilities? AI solutions require having the correct data and the correct information architecture. Seth explains how this can be achieved at scale saving hundreds of millions of dollars in costs and increasing revenue by the same order of magnitude through case studies and examples. For a pharmaceutical company, the use of a knowledge architecture improved accuracy from 53% to 83% and eliminated hallucinations for a highly sensitive portfolio review application while providing audit trails, protection of IP and explainability. Seth educates and level sets with a warm, humorous, and engaging style accessible to both executives and practitioners.
Advance Trading Inc - Bloomington, IL - Apr 30 2024
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