Joe Schlanger
Talking Machines (But Chill)
AI is everywhere, and it’s moving fast. Keeping Up with AI is your go-to podcast for making sense of it all. We talk trends, tools, breakthroughs, and curveballs in plain language, so you can stay informed, curious, and one step ahead in an AI-powered world.
Where to listen?
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Episodes
The Future of Property Claims Is Proactive, Not Reactive 30.06.2026 22:02
This week's conversation focused on one of the biggest shifts happening in our industry: moving from reactive claims handling to proactive claims management. We explored how artificial intelligence, real-time data, and human expertise are working together to transform the property claims experience. Some of the key takeaways: Predicting losses before they become larger catastrophes. Improving cata...
AI Scales Catastrophe Insurance Claims 08.04.2026 21:43
Welcome to today’s episode, where we explore how artificial intelligence and drone technology are reshaping the insurance industry. From aerial imagery that makes property inspections safer and faster, to natural language processing that uncovers subrogation opportunities buried deep in claim files, insurers are shifting toward smarter, data‑driven decision‑making. These tools are reducing human e...
The Environmental Cost of Generative AI 12.02.2026 17:43
Generative AI has a significant environmental footprint due to its high energy, water, and hardware demands. Training large models can consume several times more energy than typical computing tasks—sometimes enough to power over 100 homes for a year—while data centers also use substantial water for cooling. Rapid expansion often relies on fossil fuel-based electricity, increasing carbon emissions,...
Fixing Agile for Machine Learning Development 04.02.2026 16:03
Fixing Agile for Machine Learning explores why traditional Agile frameworks struggle in data science and AI—and what to do instead. Agile was built for predictable software delivery. Machine learning is anything but predictable. Models fail, data shifts, experiments dead-end, and “done” is never binary. When teams force ML work into classic Scrum rituals, the result is frustration, fake estimates,...
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