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AI is in trouble. Both generative AI and predictive AI face crippling limitations that compromise … More their value. The solution? GenAI helps predictive AI and vice versa. Eric Siegel AI is in trouble. Both of its main two flavors, generative AI and predictive AI, face crippling limitations that compromise their ability to realize value. The solution? GenAI helps predictive AI and vice versa. GenAI’s problem is reliability. For example, while almost three-quarters of lawyers plan to use genAI for their work, their AI tools hallucinate at least one-sixth of the time. Predictive AI’s problem is that it’s hard to use. While it has enjoyed decades of success improving large-scale business operations, it still realizes only a fraction of its potential because its deployment demands that stakeholders hold a semi-technical understanding. These two flavors of AI – strictly speaking, two categories of use cases of machine learning – are positioned to solve one another’s problems. Here are five ways they can work together. 1. Predictive Intervention For GenAI Predictive AI has the potential to do what might otherwise be impossible: Realize genAI’s bold, ambitious promise of autonomy – or at least a great deal of that often overzealous promise. By predicting which cases require a human in the loop, an otherwise unusable genAI system will gain the trust needed to unleash it broadly. For example, consider a question-answering system based on genAI. Such systems can be quite reliable if only meant to answer questions pertaining to several pages worth of knowledge, but performance comes into question for more ambitious, wider-scoped systems. Let’s assume the system is 95% reliable, meaning users receive false or otherwise problematic information 5% of the time. Often, that’s a deal-killer; it’s not viable for deployment. The solution is predictive intervention. If predictive AI flags for human…

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Filed under: News - @ May 15, 2025 10:27 pm