What changes when an AI mistake doesn’t just hurt a benchmark score, but affects someone’s housing or healthcare? EliseAI Head of Applied Research Mario Martone argues that deploying AI in essential industries requires a very different standard of reliability.Mario joins MTS to explain how EliseAI handles roughly 5 million phone calls a month, why LLMs aren’t the right solution for every problem, and how the team combines language models, traditional machine learning, neural networks, and internally built evaluations to decide what actually works in production.&nbsp;Drawing on 15 years studying theoretical physics, Mario also explains how the scientific method shapes EliseAI’s research culture, from testing new models against its own benchmarks to fine-tuning open-source models and automating increasingly complex workflows across housing and healthcare. And he makes the case that applying AI to the real world can produce problems every bit as intellectually difficult as the ones he encountered studying string theory.Resources:Learn more about EliseAI:<a href="https://www.eliseai.com/" rel="noopener noreferrer"> https://www.eliseai.com/</a>Follow Mario Martone on LinkedIn: <a href="https://www.linkedin.com/in/mcmartone/" rel="noopener noreferrer">https://www.linkedin.com/in/mcmartone/</a>&nbsp;Follow MTS: <a href="https://www.mts.now/" rel="noopener noreferrer">https://www.mts.now/</a>&nbsp;
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Why Real-World AI Is Harder Than the Benchmark | EliseAI’s Mario Martone

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