Jehan Wickramasuriya — AI in High-Stress Scenarios

Weights & Biases · Beginner ·🚀 Entrepreneurship & Startups ·3y ago
Jehan Wickramasuriya is the Vice President of AI, Platform & Data Services at Motorola Solutions, a global leader in public safety and enterprise security. In this episode, Jehan discusses how Motorola Solutions uses AI to simplify data streams to help maximize human potential in high-stress situations. He also shares his thoughts on augmenting real data with synthetic data and the challenges posed in partnering with startups. Show notes (transcript and links): http://wandb.me/gd-jehan-wickramasuriya - ⏳ Timestamps: 00:00 Intro 00:42 How AI fits into the safety/security industry 09:33 Event matching and object detection 14:47 Running models on the right hardware 17:46 Scaling model evaluation 23:58 Monitoring and evaluation challenges 26:30 Identifying and sorting issues 30:27 Bridging vision and language domains 39:25 Challenges and promises of natural language technology 41:35 Production environment 43:15 Using synthetic data 49:59 Working with startups 53:55 Multi-task learning, meta-learning, and user experience 56:44 Optimization and testing across multiple platforms 59:36 Outro - Connect with Jehan and Motorola Solutions: 📍 Jehan on LinkedIn: https://www.linkedin.com/in/jehanw/ 📍 Jehan on Twitter: https://twitter.com/jehan/ 📍 Motorola Solutions on Twitter: https://twitter.com/MotoSolutions/ 📍 Careers at Motorola Solutions: https://www.motorolasolutions.com/en_us/about/careers.html - 💬 Host: Lukas Biewald 📹 Producers: Riley Fields, Cayla Sharp, Angelica Pan, Lavanya Shukla, Anish Shah - Subscribe and listen to our podcast today! 👉 Apple Podcasts: http://wandb.me/apple-podcasts​​ 👉 Google Podcasts: http://wandb.me/google-podcasts​ 👉 Spotify: http://wandb.me/spotify​
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 42 Nicolas Koumchatzky — Machine Learning in Production for Self-Driving Cars
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 44 Applications of Machine Learning to COVID-19 Research with Isaac Godfried
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Chapters (15)

Intro
0:42 How AI fits into the safety/security industry
9:33 Event matching and object detection
14:47 Running models on the right hardware
17:46 Scaling model evaluation
23:58 Monitoring and evaluation challenges
26:30 Identifying and sorting issues
30:27 Bridging vision and language domains
39:25 Challenges and promises of natural language technology
41:35 Production environment
43:15 Using synthetic data
49:59 Working with startups
53:55 Multi-task learning, meta-learning, and user experience
56:44 Optimization and testing across multiple platforms
59:36 Outro
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