Can AI Help Improve the Environment?


The field of Artificial Intelligence (AI) is flourishing thanks to large investments, and big companies with heavy ecological footprints can use it to make their activity more sustainable. This article focuses on multiple areas where AI can be helpful in achieving such goals.

Google uses an AI model to reduce the energy load of its resource-hungry data centres, reducing the energy cost of cooling by 40%.

The very basic definition of machine vision is the automatic extraction of information from digital images. Essentially, sensors, digital imagery and computer analysis replace human eyes and judgement from manufacturing processes. And while this has massive advantageous implications on a border scale, machine vision is a particularly useful asset when it comes to recycling.

Despite having eco-friendly, socially responsible connotations, the world of recycling is still, largely, a figurative and literal mess. By now, most people recycle at home and you see various inviting green receptacles all over the place, and while those are all steps in the right direction, the logistics of handling recycling and plastic waste on a global scale are, in many ways, atrocious.

There are a total of 17 Sustainable Development Goals as defined by the United Nations, that can be grouped under three pillars: Environment, Economy and Society. A study published in Nature Communications looked at how the development of AI could enable many advances, while also hampering others. 

Carbon Tracker, a climate advocacy think-tank, uses AI to track emissions from coal plants using satellite imagery. Using satellite data they help guide investments toward lower-footprint ventures. 

Countries that can afford it simply ship their plastic waste to far off destinations, adopting the “out of sight, out of mind” philosophy that it’s no longer their problem. Meanwhile, underdeveloped countries, often overpopulated, raise little to no awareness on the importance of reducing waste and encouraging sustainable practices, which of course can be understood when in the context of seemingly larger, more immediate concerns. 

But machine vision can seriously revolutionise our commitment to sustainability and ethical business practices. Besides its applications in manufacturing, which significantly reduces waste and surplus industry, machine vision can make recycling a vastly more efficient and profitable industry. Click here for more interesting info and fun articles on subjects like tech and automation in business practices.

Xcel Energy, a coal-burning and nitrous oxide-emitting utility company, is using AI to better predict energy consumption patterns and adapt its operating systems, thus significantly boosting efficiency (~20%).

When you look at your shampoo bottle or plastic food container, you’ll probably notice a bunch of tiny or faintly embossed symbols. A number in a triangle, the arrow “ying-yang”, maybe even occasional instructions on whether or not you can recycle the item with your other household waste. But what do those symbols even mean? Some, believe it or not, mean absolutely nothing. 

Residual markers from the production process, nothing more. And there we were, thinking the arrow circle was the universal symbol for recyclable materials. Most people at home have no idea what is ACTUALLY recyclable and what is not, and even then, they can be careless and throw inappropriate items into their recycling bin – not knowing that very often, if an entire batch of recycling contains one or two non-recyclable items, the whole thing gets dumped in a landfill or incinerator. 

The sheer volume of waste and plastics that we, as a society, produce on a daily basis, makes it impossible for the sorting and sustainability processes to be performed efficiently by a human labour force. Machine vision on the other hand, can be programmed to instantly recognise not just bizarre industry stamps and codes, but recognise and analyse the composition of an item, thus instantly knowing how and where it should be sorted. From shape recognition to OCR reading to the sheer speed of complex analysis, machine vision can vastly outstrip human inspection in terms of accuracy, consistency, speed and volume. 

Machine vision won’t get tired or bored, so it can sort through tons of plastic and vastly improve our rates of recycling, while we can make more and more effort at home to help end the plastic crisis. 

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