Last week i spoke at websummit 2022 about  closing the loop with AI. Just wanted to share some thoughts from it.
It’s no secret that the world is producing more waste than ever before. In 1950, the world population was just over 2 billion. Today, it’s estimated to be 7.8 billion. With more people comes more consumption—and more waste. It’s estimated that by 2050, the world will produce 3.40 billion tons of waste per year. That’s enough to fill 1,000 Empire State Buildings!

Clearly, something needs to be done about this mounting problem. One potential solution is the circular economy, which aims to keep resources in use for as long as possible and then recycle them back into the system when they’re no longer needed. But can artificial intelligence (AI) really help close the loop on waste? Let’s take a look.


Waste management and deep learning

It’s estimated that only 9% of all plastic ever produced has been recycled. The rest ends up in landfills or as litter in our oceans and waterways. This is a huge problem, but one that AI may be able to help solve.

Deep learning is a type of machine learning that mimics the workings of the human brain. Using deep learning, AI can be taught to recognise patterns in data—including patterns in waste data. This could potentially be used to develop systems that can sort waste automatically, making recycling easier and more efficient.


Waste analytics

In order for the circular economy to work, we need to know what types of waste are being produced and where they’re coming from. That’s where waste analytics comes in. Waste analytics is the process of using data to understand and optimise waste management processes – this what greyparrot is doing bringing more transparency in the waste industry. 

Traditionally, waste management has been a largely manual process, with workers sorting through garbage by hand to pull out items that can be recycled. But with AI-powered waste analytics, this process can be automated. By analysing data from  fast moving waste flows at material recovery facilities  computer vision based  AI can identify the waste composition of the materials  what’s being thrown away in residue line and remainder being sorted. This information can then be used to optimise sorting  processes and reduce overall waste production and increase supply of materials in the secondary market. 

Waste sorting

Once we know what types of waste are being produced, we need to figure out how to sort them so they can be properly recycled or reused. This is where AI-powered robots come in. Robots powered with deep learning algorithms can be taught to sort waste automatically, making the process faster and more efficient than traditional methods.

What’s more, these robots can work around the clock—sorting through garbage even when humans are not present—which means they can potentially increase the amount of recyclable material that’s collected each day. And as an added bonus, they can also help reduce labor costs associated with traditional methods of waste sorting.

As the world population continues to grow, so does the amount of waste we produce each year. Something needs to be done about this mounting problem—and artificial intelligence may hold the key. From deep learning algorithms that can sort through garbage automatically to optical sorters and robots that work around the clock, AI has the potential to revolutionise waste management and help close the loop on our growing mountain of trash.

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