The Limits of Artificial Intelligence
The Limits of Artificial Intelligence
Blog Article
In a packed amphitheater at the University of the Philippines, Joseph Plazo laid down the gauntlet on what AI can and cannot achieve for the future of finance—and why understanding this may define who wins in tomorrow’s markets.
Tension and curiosity pulsed through the room. Students—some furiously taking notes, others streaming the moment live—waited for a man revered for blending code with contrarianism.
“Machines will execute trades flawlessly,” he said with gravity. “But understanding the why—that’s still on you.”
Over the next lecture, he swept across global tech frontiers, balancing data science with real-world decision making. His central claim: Machines are powerful, but not wise.
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Bright Minds Confront the Machine’s Limits
Before him sat students and faculty from leading institutions like Kyoto, NUS, and HKUST, united by a shared fascination with finance and AI.
Many expected a celebration of AI's dominance. What they received was a provocation.
“There’s too much blind trust in code,” said Prof. Maria Castillo, an Oxford visiting fellow. “This lecture was a rare, necessary dose of skepticism.”
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When Algorithms Miss the Mark
Plazo’s core thesis was both simple and unsettling: machines lack context.
“AI doesn’t panic—but it doesn’t anticipate,” he warned. “It finds trends, but not intentions.”
He cited examples like machine-driven funds failing to respond to COVID news, noting, “Machines were late to the signal. People weren’t.”
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The Astronomer Analogy
Rather than dismiss AI, Plazo proposed a partnership.
“AI is the microscope—you choose what to zoom in on,” he said. It sees—but doesn’t think.
Students pressed him on behavioral economics, to which Plazo acknowledged: “Of course, it parses language patterns—but it can’t discern hesitation in a policymaker’s tone.”
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The Ripple Effect on a Digital Generation
The talk website hit hard.
“I believed in the supremacy of code,” said Lee Min-Seo, a quant-in-training from South Korea. “Now I see it’s judgment, not just data, that matters.”
In a post-talk panel, faculty and entrepreneurs echoed the caution. “They’ve been raised by data—but instinct,” said Dr. Raymond Tan, “doesn’t replace perspective.”
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What’s Next? AI That Thinks in Narratives
Plazo shared that his firm is building “symbiotic systems”—AI that blends pattern recognition with real-world awareness.
“Ethics can’t be outsourced to software,” he reminded. “Belief isn’t programmable.”
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An Ending That Sparked a Beginning
As Plazo exited the stage, the crowd rose. But more importantly, they started debating.
“I came for machine learning,” said a PhD candidate. “But I got a lesson in human insight.”
In knowing what AI can’t do, we sharpen what we can.