AI Takes Out the Trash as Recycling Goes High-Tech

AMP is giving recycling a robotic makeover.

The Colorado-based company just raised $91 million to expand its artificial intelligence (AI)-powered recycling operations, where robots with advanced vision systems sort trash faster and more accurately than humans.

As the recycling industry grapples with rising costs and stricter contamination standards, AMP’s technology promises to streamline operations and boost recovery rates, turning garbage into gold.

AMP’s AI relies on deep learning, continuously training itself by analyzing millions of images of waste. It identifies materials by recognizing patterns in colors, textures, shapes, and logos — spotting a stray plastic bottle in a sea of trash faster than any human.

“Recycling rates have stagnated in the United States,” said AMP founder Matanya Horowitz in a news release. “Our AI systems help recover more materials, reduce contamination, and ultimately optimize waste operations.”

More Efficient?

The AMP technology also targets a longstanding industry problem: labor shortages. Robots work around the clock, eliminating the need for the grueling manual sorting often performed by humans. With over 400 AI systems deployed worldwide and growing demand for sustainable waste solutions, AMP’s machines could redefine recycling facilities’ operations.

“I believe bins themselves can be made intelligent to reduce human errors, then recycling system can start sorting materials with the help of AI,” Abhishek Agrawal, an Amazon AI software engineer, told PYMNTS.

Barry Honig, CEO of TruckPay, previously told PYMNTS how digital technology is redefining the waste management industry. Traditionally reliant on cash transactions, the sector is now adopting digital platforms like TruckPay to enhance efficiency and transparency. These platforms offer real-time tracking of materials, streamlined logistics, and various cashless payment options, including virtual wallets and instant transactions.

AI Cleans Up Waste Management

Over the past year, AI-powered systems have gained traction in waste management facilities, which are used to sort materials more accurately and streamline operations.

In Europe, Greyparrot, a London-based company, has deployed its AI technology in over 50 recycling facilities. Its systems use cameras and machine learning to analyze waste on conveyor belts in real time, identifying materials such as plastics, paper and metals. The insights help facilities reduce contamination and improve sorting efficiency. Greyparrot’s technology is also pushing manufacturers to redesign packaging for easier recycling.

Another player, Recycleye, uses robotic arms equipped with AI to automate sorting. The robots can quickly identify and separate materials like aluminum and PET plastic, boosting productivity and addressing labor shortages. In the U.K., Recorra, a recycling firm, recently upgraded its operations with Recycleye’s technology to modernize its material recovery process.

AI’s influence extends beyond sorting lines. Companies like WestRock are turning to AI to design sustainable packaging that is easier to recycle. These efforts align with the broader goals of transitioning to a circular economy, where resources are reused and waste is minimized.

Municipalities are also adopting AI to address recycling challenges. A $24 million materials recovery facility featuring AI sorting systems in Tasmania now processes recyclables from more than 100,000 households. Over the past year, AI-powered systems have gained traction in waste management facilities, where they are being used to sort materials more accurately and streamline operations.

“In the metals recycling industry, AI is being used to predict market trends and help recyclers make informed decisions regarding the buying, selling and inventory management of scrap metals,” Joe Floam, owner of ScrapWare wrote on his company’s blog.

“AI algorithms can analyze large volumes of market data — including prices, supply and demand, and trade patterns — and then identify patterns within the data to predict future market trends. This helps recyclers plan their operations better by optimizing production and managing inventory based on the expected demand for specific metals.”