What is Green AI? And Why You Should Prioritize It.

As industries embrace artificial intelligence, the demand for more sophisticated and powerful AI models is increasing. And this demand will only grow intense in the future. A large amount of energy and computational cost is required to train AI models, raising concerns about its carbon footprint. In the read forward, we’ll discuss the concepts of Green AI, and you should consider it.

According to a recent finding, the amount of computing power required for advanced AI training has increased 300,000-fold since 2012, and the cost of training one machine translation model is estimated to emit 626,000 tonnes of CO2. That’s a big deal!

So what can be done?

We need to create sustainable AI models to satisfy the existing demand. To maximize the positive impact that AI can deliver without harming the environment, AI enthusiasts came up with the concept of Green AI.

What is Green AI?

Green AI refers to artificial intelligence that is environment friendly. It aims to achieve sustainability through environmentally sustainable AI models with lower computational costs and fewer carbon emissions. 

Green AI can bring positive impacts in many sectors. For example, Al can be used to predict demand and supply in the energy sector. This can facilitate an intelligent grid system that can reduce energy wastage. Google, for example, uses AI to improve energy efficiency, leveraging DeepMind’s machine learning capabilities to reduce the amount of energy required to cool its data centers by 40%. Sustainable AI can broadly impact the transportation industry as well. Here, AI can assist in reducing traffic congestion and improving logistics.

Related article: Role of AI in business optimization 

How to move your industry toward Green AI?

Going green isn’t a bad idea. Research and developments are going around to develop AI models that consume less energy. When technology is ready to offer its more sustainable options, you should be prepared to embrace it. Here are a few ways you can do it.

• Improvise reproducibility

Reproducibility and sharing of intermediate information are critical to increasing AI development efficiency. Too often, AI research is published without a source code. Usually, researchers cannot reproduce the results even if they get the source code. Furthermore, researchers may face internal obstacles in making their work open source. These factors are drivers of Red AI because they can cause duplication of efforts and prevent efficient sharing.

• Improve hardware performance

There is currently a plethora of advanced hardware that provides improved performance on deep learning tasks and enhances reliability. Having or upgrading to performance-based hardware can reduce the usage and cost of energy.

• Democratize and understand deep learning

Deep learning is efficient. Uncovering the fundamental science of deep learning and formally defining its strengths and shortcomings will aid in the creation of more reliable and practical models. Extending the precision of deep learning remains a promising field of study. Existing models are now precise enough to be used in various applications. Deep learning tools can support almost any business and science domain. If more people from many industries work on the technology, we will be more likely to see unexpected improvements in performance and energy consumption.

Related article on How to implement Artificial Intelligence in your business.

Conclusion 

We have seen remarkable strides in artificial intelligence in recent years. But in terms of sustainability, we are only in the early stages. It is essential to focus on the best systems, methods, and methodologies for constructing efficient AI models. With the right approach and technology consultation, it is possible to build AI tools that are powerful as well as sustainable. Need help? Feel free to check out our AI development services.