In this episode, Abate flies to Denver to get a behind-the-scenes look at the future of recycling with Joe Castagneri, the head of AI at Amp Robotics. With Materials Recovery Facilities (MRFs) processing a staggering 25 tons of trash per hour, robotic sorting is the clear long-term solution.

Recycling is a for-profit industry. When the margins don't make sense, the items will not be recycled. This is why Amp's mission to use robotics and AI to bring down the cost of recycling and increase the number of items that can be sorted for recycling is so impactful.

Joe Castagneri
Joe Castagneri graduated with his Master of Science in Applied Mathematics, with an undergrad degree in Physics. While still in university, he first joined the team at Amp Robotics in 2016 where he worked on Machine Learning models to identify recyclables in video streams of Trash in Materials Recovery Facilities (MRFs). Today, he is the Head of AI at Amp Robotics where he is changing the economics of recycling through automation.

Timestamps:
00:00 Introduction to Amp Robotics & MRFs
01:20 Joe Castagneri's journey to Amp
04:30 The company's evolution with AI & Robotics
07:50 Challenges of computing in trash facilities
10:20 Rapid iteration & growth of the robots
12:30 The unique challenges of recycling environments
15:00 Deep learning's role in recognizing varied waste items
16:01 Unusual Recycling Challenges
18:00 The Scale of Waste Management
20:00 Economic Impacts & Material Value
22:00 Using AI and robotics to address recycling's challenges.
25:14 Disruptive Potential of AI in Recycling
26:00 AI Versatility in Varied Environments
27:44 Benefits of AI Sorting vs. Human Sorting
28:24 Economic Aspects & Hidden Costs of Manual Sorting
29:41 Labor Challenges in Traditional Sorting
24:00 Algorithms & tradeoff between time and value in recycling
30:11 - Costs and Challenges of Manual Sorting
32:45 - Presence of MRFs and Their Global Reach
34:25 - EU Regulations & Quality Standards in Recycling
36:20 - Data-Driven Insights for Better Waste Management
38:35 - Vision for the Future: Efficient, AI-Driven Facilities