The recent destruction of the Pacific Palisades in Los Angeles was a brutal reminder of why we need robust early wildfire detection systems. Joining me today is Shahab Bahrami, the co-founder and CTO at SenseNet – a company that provides advanced AI-powered cameras and sensors to protect communities and valuable assets against wildfires.

Shahab is passionate about using interdisciplinary research to bridge the gap between machine learning and optimization, and he begins today’s conversation by detailing his professional background and how it led him to co-found SenseNet. Then, we unpack SenseNet and how its technology works, how it gathers data for its AI models, the challenges of relying on images and other sensor data to train machine learning models, and how SenseNet uses multiple sources to detect or define any one problem. To end, we learn why and how SenseNet uses various AI models in a single sensor, how it measures the overall impact of its tech, where the company plans to be in the next five years, and Shahab’s valuable advice for other leaders of AI-powered startups.

Key Points:

• Shahab Bahrami walks us through his professional background and how it led to SenseNet.
• The ins and outs of SenseNet and how its technology works.
• How machine learning fits into SenseNet’s offerings, and how it gathers the necessary data.
• The challenges of working with images and other sensor data to train models.
• How SenseNet integrates information from different sources to zero in on a single anomaly.
• Understanding how it uses multiple AI models to adapt to variations post-installation.
• How the system chooses which AI model to apply and when.
• Shahab describes how his company measures the overall impact of its technology.
• His advice to other leaders of AI-powered startups, and his five-year vision for SenseNet.

Quotes:

“We have one of the most comprehensive wildfire detection solutions in the world, and it is proven by multiple, real-world projects.” — Shahab Bahrami

“Having separate AI models is the solution that we are now implementing.” — Shahab Bahrami

“For the sensor’s AI – because it is a semi-supervised AI, it automatically adapts itself to local conditions. It learns gradually what is normal and what is abnormal, and it is a continuous learning. It won’t stop.” — Shahab Bahrami

“AI changes fast. Every day we have a new AI engine, we have a new model, and leaders, I believe, need to stay updated and make sure their teams have the support and also the resources to keep innovating.” — Shahab Bahrami

Links:

Shahab Bahrami

Shahab Bahrami on LinkedIn

SenseNet

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