Rethinking AI’s Carbon Footprint

One important (but seldom discussed) aspect of AI is its carbon footprint. Many are surprised to learn that the average carbon footprint of AI can be equivalent to five times the emissions created by the average car over its lifetime

At a time when many businesses are committing to net-zero carbon emission and consumers are looking for ways to consume sustainably, it’s crucial that this problem is addressed if businesses hope to make use of ground breaking technology without being left behind in the green revolution.

 

A greener future for AI

With Green AI, businesses can build accurate machine learning models which are more efficient, and consume less computing resources, all while increasing operating speed and reducing energy usage. Green AI ultimately integrates technology and sustainability into one unified ecosystem. 

When it comes to saving energy, AI can assist in developing precise predictive capabilities and intelligent grid systems to manage the demand and supply of renewable energy. By doing this, companies can significantly decrease cost and unnecessary carbon generation. 

Companies are also using AI to help improve their sustainability goals and green initiatives. Alaska Airlines, for example, is successfully decreasing the company’s carbon footprint by using AI technology to guide flights, and more companies will follow suit next year.

The solution

The answer lies in optimising AI models to be as efficient as possible. The more efficient AI can be, the faster it can work to achieve the user’s goals. If AI works efficiently, it consumes less energy and memory, and therefore can reduce its carbon footprint. 

TurinTech’s evoML platform unlocks efficiency savings that no other AI platform has accessed. It has the ability to reduce the carbon emissions of AI models by 50%, without compromising on accuracy and other important performance metrics. This is achieved through its unique ability to optimise model code, which helps businesses build efficient AI with multiple objectives.

evoML gives its users the opportunity to take large strides towards AI sustainability and scalability while also accelerating the end-to-end data science process from months to weeks.

In conclusion 

To keep up with the changing landscape and sustainability trends, companies should place more focus on utilising green AI in their business in order to enhance their technology and sustainability initiatives. 

By using an environmentally conscious AI optimisation platform, such as evoML, companies are not only able to optimise their models to be more accurate but also run faster. They’re also able to simultaneously cut down their AI carbon emissions during the process creating an all around more efficient and environmentally-friendly system.