Solid waste production is estimated at 1.3 billion tons every year and is on the rise. It is thought it will reach closer to 2.2 billion tons by 2025. Of this waste, around 45 million metric tons is electronic waste (e-waste), which is simply staggering. With the continuation of our insatiable need for electronics, it is estimated to rise to around 53 million metric tons by 2021. Sadly, only a very small amount, about 20%, is being recycled. It is clear that the linear economy model (make, use, dispose) being used today is unsustainable. Massive energy use, overutilization of natural resources causing ecosystem erosion and eventual collapse and the huge amount of waste generation cannot continue. However, we also can’t ignore our current way of conducting commerce is an essential reality that has been with us since before the 18th century. In today’s world, we are so vested in the current operating model that companies find it difficult to change unless it helps them grow efficiently.
“Earth provides enough to satisfy every man’s needs, but not every man’s greed.” ― Mahatma Gandhi
The answer to this global conundrum is to create a circular economy model that binds sustainability with economic growth that replaces the current linear one. In a circular economy the aim is to use as much renewable energy, reusable products and planet friendly methods of production as possible (make, repair, re-use, re-grow, recycle). Goods would be repaired rather than replaced, and at the end of their useful life will be recycled, so waste is largely eliminated.
How Can AI Technologies Benefit a Circular Economy?
Human intelligence over the past 200 years has been responsible for the industrial revolution. With increasing amounts of refinement our technologies have continued to develop to what they are today. This revolution has brought with it an enormous amount of prosperity and changed many lives forever. On the surface this all seems great, but it has come at a huge cost to our planet, which simply cannot sustain industrial or human growth at this current level.
The idea of a circular economy isn’t new, but the methods by which to make it a real feasibility have been a little trickier to realize. In a circular economy, growth is no longer reliant on our finite resources. Its very principles prevent waste pollution and stimulate a natural regeneration of materials whilst binding the economic realities that require real and available technologies, that are relevant within industries and that enable revenue enhancing business models to support growth.
Using a circular economy model will generate new jobs, encourage innovation, benefit our environment and create trillions of dollars of net benefit.
In order to achieve this ideal we will require new technologies. Learning processes that can keep up with demand and are capable of designing, prototyping and testing in a faster more agile way. We will need to redesign many key aspects of our current economy.
To facilitate this, AI could be the answer. Though AI is only a tool, it has the potential to start a whole new industrial revolution in itself, one based on the principals of a circular economy. AI technologies could provide us with the faster learning processes that can generate initial designs and prototypes faster than any human, allowing them to be put into production more swiftly. AI can learn just like a human, but it can do so many many times quicker. AI could present us with solutions to problems that take human minds too long to solve. AI can complement and expand the skills of a human brain generating suggestions to problems (cognitive computing). Or it can work out the best solutions for complex problems with no human input other than the initial programming of the problem to be resolved. AI can help us to:
● Learn faster
● Understand complex problems
● Sort and order abundant data
● Generate resolutions to problems
Artificial Intelligence could help us reach a circular economy in three main ways:
By designing products, materials and components using circular economy principles using iterative machine learning. Machine learning is when a computer program is designed to extrapolate information based on observed patterns. There are various different types of machine learning that could be used to help with this:
● Supervised learning – when the machine learning algorithms make predictions based on the values of responses. These use the training data given to the program where the response values, called ground truths, are already known. The algorithm makes iterations until it can reach an agreed result.
● Unsupervised learning – when the algorithm is only given input data that has no known ground truth. It is used for identifying trends or finding patterns within data.
● Reinforcement learning – is a machine learning technique, which enables the agent to learn interactively in its surroundings by trial and error. It uses feedback from its own actions and experiences to receive delayed rewards.
● Evolutionary algorithms – these computer applications mimic biological processes to solve complex problems.
2. Data Analysis.
AI can also be used to highlight competitive circular business models. Using a combination of historical and real-time data found from interrogating products and their users, AI is capable of increasing asset utilization and product circulation by predicting demand and using smart inventory management.
3. Recycling Strategies.
AI can help to determine the best ways to sort, disassemble, remanufacture and recycle products and materials. This helps to close the loop or the circular economy. By using AI to help design strategies for a circular economy, billions of dollars per year could be generated. In the food industry it could enhance opportunities in farming, processing, logistics and waste management. It is already being used to scan fruit, from the images produced it can determine the optimum time to pick them. It can also accurately match food demand with supply and further possibilities are virtually endless. In the electronics industry AI can design and select the best specialist materials to match requirements, extend the life of electronics by assessing predictive maintenance requirements and improve and automate the recycling of e-waste using image recognition and robotics.
Current Uses of AI in Industry
Waste management is already using AI successfully, using sensor-based systems on bins to make the collection and movement of waste more efficient. Robotic sensors can recognize and sort different waste products for recycling and AI systems can also generate better analytical data. Recognizing the opportunities of combining AI with the vision of a circular economy represents a huge and largely untapped opportunity. By using one of the greatest technological advancements of our time, we could fundamentally reshape the economy making is sustainable for future generations.
Three companies who are already selling AI solutions are:
1. Tomra.com are a company who create solutions for optimal resource productivity. One of its innovations is an algorithm that can analyze images to identify non-uniform produce that does not sell in grocery stores. It can sort the produce into grades so it can be put to best use, thus reducing waste.
2. Optoro.com create superior returns optimization platforms to help retailers manage and process inventory. Approximately 25% of customer returns end up in landfills because it is not economically viable for sorting of resalable goods to be done by hand. Optoro uses AI to process returns automatically this reduces waste and improves customer experience by making returns easy. Another upside is it gives customers a positive user experience and increases satisfaction, which strengthens brand loyalty and reduces operating costs. Their predictive machine learning can accurately route inventory to increase profitability. The portfolio tools allow real-time visibility that allow teams to control and optimize decisions at the touch of a button.
3. Stuffstr.com was created around the idea that there should be no unused stuff. Stuffstr allows consumers to sell their unwanted clothing back to retailers regardless of condition. Stuffstr uses AI to price the products it buys from consumers and sells it on to secondary markets using demand forecasts.
4. Zehnplus.ch uses AI data algorithms in its software development for clients to automate the touch points throughout the customer journey. Delivering real-time sales, billing and service that eliminates wasted material and human interactions in the entire process. Other current uses include finding plant-based meat alternatives and testing new recyclable alloys. Ultimately, AI could be used to redesign entire networks and systems such as supply chains, global logistics and much more.
Greed over Need
The sad fact is that many manufacturers see the throwaway mentality as a great way to boost their profits. Take for example smartphones, the manufacturers of this ever-growing market are purposefully limiting the lifespan of their products to increase profits. These phones contain valuable metals, which could be recycled, they also contain potentially dangerous toxic chemicals. It will take a complete overhaul of the industry to bring it around to a circular economy way of thinking. There are a couple of companies that have embraced the ideas, however. Fairphone and Puzzlephone both use smartphone design that is in line with circular economy concepts. They have facilities where their phones can be disassembled, and the components replaced when there is a fault. Other industries that are starting to see the benefits of using a circular economy are the automotive industries, computer, clothing and footwear manufacturers.
It is estimated that by 2020 over 21 billion devices will be connected to the internet. Information and communication systems are being invisibly embedded into everyday things around us, TVs, cars, phones, appliances, even buildings. Technology used in these things could also be used to enhance their performance through data analysis, fault recognition, recall and repair.
With the advancement of AI and the real possible uses it can offer us, there is no doubt that it will be a very great asset for helping us to achieve a truly circular economy. The endless data solutions that are relevant across all industries can enable us to see the fruition of our circular economy ideals become a reality in a much faster period of time. As more industries realize the importance of changing from a linear economy to a circular one, it is hoped that further development in this field will persuade these industries that not only does it make moral sense but also economic revenue growing sense to embrace the change. Here is to hoping that AI will be the solution to building a cleaner, less wasteful and successful new world.