Machine learning models excel at finding patterns across enormous amounts of historical data. For instance, in the credit industry, machine learning models analyze massive volumes of historical data to identify patterns no human analyst could realistically detect across millions of outcomes.
Machine learning models excel at finding patterns across enormous amounts of historical data.
For instance, in the credit industry, machine learning models analyze massive volumes of historical data to identify patterns no human analyst could realistically detect across millions of outcomes.
Over the past two years, I've had hundreds of conversations about AI with regulators, financial institutions, engineers, executives, and skeptics.
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Over the past two years, I've had hundreds of conversations about AI with regulators, financial institutions, engineers, executives, and skeptics. Everyone talks about AI as though it's a single technology moving in a single direction, but the reality is much more nuanced. AI is the convergence of two fundamentally different systems: one designed to predict and one designed to reason.
It synthesizes information, navigates ambiguity, and communicates in human language. It can explain why a trend matters, summarize complex findings, surface strategic tradeoffs, and help people interact with systems that previously required specialized expertise to understand.
What keeps coming up is how much confusion there is about AI.
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