What is sustainable artificial intelligence A greener earth with SUSTAINABLE AI

Artificial intelligence (AI) is experiencing tremendous growth in both research and industry, revolutionizing fields as diverse as science, medicine, finance, and education. These advances are primarily due to the improved ability of artificial intelligence to effectively learn larger models using large data sets. While this development has enabled AI to make scientific discoveries, create new business opportunities, and boost industrial growth, it also unfortunately has implications for planet Earth. In order to prevent the negative effects of artificial intelligence on the earth, a topic called sustainable artificial intelligence (Sustainable AI) has been raised, which we are going to examine in this article from the Avir artificial intelligence website. Stay with us.

The negative impact of artificial intelligence on the earth

Since AI requires a significant amount of computing power and energy to train larger models, it has many negative impacts on the environment, leading to increased carbon footprints and greenhouse gas emissions.

A report published by the MIT Technology Review shows that the carbon emissions produced during the training of an artificial intelligence model are greater than the carbon emissions produced by the average American car over its entire lifetime. Google’s AlphaGo Zero artificial intelligence has produced 96 tons of carbon dioxide in just 40 days of training. This is equivalent to the greenhouse gas emissions from 1,000 hours of air travel or the annual carbon footprint of 23 US homes.

Facebook reports that training large AI models, such as Meena, can have a carbon footprint equivalent to driving a passenger vehicle for a distance of approximately 389,833 km. A recent study by MIT shows that the environmental impact of cloud computing now exceeds the entire airline industry.

Cloud computing, which enables the storage and processing of large amounts of data, contributes significantly to greenhouse gas emissions. Additionally, a single data center can consume as much electricity as 50,000 homes.

?What is sustainable artificial intelligence

Sustainable AI is the use of artificial intelligence (AI) systems that operate in ways conditioned by sustainable business practices. Many of the current AI systems used in organizations have a reputation for being harmful to the environment, but actions can be taken to mitigate this.

Those with first-hand knowledge of developing and running AI models know that the difficult process of training AI models requires large amounts of energy, leading to unsustainable emissions and air pollution. Some studies over the years have shown that the training process of only one machine learning (ML) model can match the carbon emissions of several cars in total.

Therefore, AI models should be made efficient so that the training process does not require too much energy for computing power. Also, all components of an AI system should be optimized as much as possible to limit energy consumption. By doing so, businesses will gain new AI modeling and optimization methods that will reduce the negative environmental impacts of their AI systems.

One of the most important techniques to do this is called sparse modeling, which is growing in popularity due to its speed and energy efficiency. Traditionally, machine learning algorithms try to parse every insight they glean from massive data sets. However, sparse models can work on small amounts of data with a narrower focus and are easier to train because they require less data.

When optimizing the hardware components used to develop and train AI models, companies should note that developers and researchers have been working on emerging devices that offer faster computations at lower power consumption rates. For example, researchers at MIT are developing a growing technology to do just that.

Current and future uses of sustainable artificial intelligence

In addition to sustainable operations, the power of AI systems can also be applied to many sustainability initiatives related to pollution, natural disasters, and climate change. Applications of sustainable artificial intelligence include the following:

• Weather Forecast
• precision agriculture guided by artificial intelligence;
• Preparing and strengthening infrastructure, such as electricity suppliers.

AI analytics can be used in conjunction with drones and other similar technologies to monitor and mitigate the effects of natural disasters such as floods. Similarly, weather and risk assessments can be performed with these AI tools, which work with IoT devices to protect products as well as other critical assets and infrastructure. Also, predictive AI is critical in predicting future greenhouse gas emissions and air pollution based on available data and trends.

Additionally, many of these AI-based technologies are being used in reforestation efforts in the Amazon. AI systems can analyze data from sensors, drones, and satellite imagery to monitor forests and how they evolve or shift over time. Machine learning algorithms with their predictive capabilities can examine past data to determine possible future deforestation scenarios.

When properly implemented and implemented, sustainable AI will reduce the negative impacts of training and optimization methods of traditional AI and machine learning models on the environment, without compromising the quality of its predictions and outputs. In addition, sustainable AI is being used in a variety of ways to help people protect their communities and natural resources. Sustainable AI is considered synonymous with the term green AI, which describes efforts to ameliorate negative impacts and ensure that the future of AI brings more benefits than disadvantages.

Implementing Sustainable Artificial Intelligence Practices: Challenges and Solutions

To make AI sustainable, it is critical to prioritize energy efficiency at all stages of its lifecycle, including data storage, model training, and infrastructure deployment.

In this section, we discuss the main challenges in achieving sustainable artificial intelligence and appropriate solutions to overcome them.

1. Prioritizing data quality

Over the past decade, there has been a significant increase in both the amount of data used to train AI systems and the size of AI models. With the increase in data size, the demand for data acquisition bandwidth has increased significantly.

:Challenge

As a result, data storage and data transmission have become major infrastructure components that consume significant energy and resources compared to AI system deployment.

:solution

One way we can address this growing need for data storage is by prioritizing data quality over quantity during the data collection process. This primarily involves carefully selecting high-quality samples and avoiding unnecessary duplication of data samples.

By using a smaller amount of high-quality data, we can reduce storage requirements and reduce power consumption while still achieving excellent AI performance.

• Balancing model size and efficiency

The continuous growth of data has led to a rapid increase in the size of models.

:Challenge

While larger models improve accuracy by using rich contextual information in the data, training them requires more powerful computational resources. For example, the memory capacity of GPU-based accelerators, such as the NVIDIA V100 (2018) with 32 GB and the NVIDIA A100 (2021) with 80 GB, doubles less than every two years.

:solution

One way to reduce the need for powerful computers is to create smaller models that perform similarly to larger models. Some of the existing works in this direction include methods such as model compression, knowledge distillation and network pruning. By sharing and reusing trained models, we can save energy and avoid redundant training.

However, to make this happen, we need to support open source frameworks and platforms that enable model sharing and encourage collaborative research within the AI ​​community.

Development of efficient energy infrastructure

The rapid expansion of AI applications has led to a significant increase in the capacity of AI training infrastructure in recent years. The growing demand for artificial intelligence inference has also led industries to increase their infrastructural capacity.

:Challenge

This increased use of artificial intelligence helps to expand the carbon footprint.

:solution

To reduce this effect, it is important to develop energy-efficient hardware specifically for AI tasks. This type of hardware can greatly reduce energy consumption during training and inference processes. This includes optimizing processors, memory systems and other components to achieve maximum performance per watt.

In addition, the use of renewable energy sources such as solar or wind power for data centers can help reduce the environmental impact of artificial intelligence computing. In addition, by using efficient cooling systems and optimizing infrastructure, we can reduce energy consumption and carbon emissions.

Create policies and regulations

:Challenge

In order to implement technological solutions, it is very necessary to design and implement policies and regulations for the sustainable development of artificial intelligence. This includes creating laws that promote sustainable AI practices.

solution

In this regard, awards can be made for energy-efficient AI systems, supporting sustainable AI research and setting targets for reducing carbon emissions. These measures can help make AI advances more sustainable.

Increase awareness and education

Awareness and education can play a key role in promoting sustainable AI practices.

:Challenge

It is important to inform the public, including AI researchers, developers, and policy makers, about the environmental impacts of AI and the need for sustainable AI.

:solution

We can launch training programs to teach AI professionals energy saving techniques. By raising awareness and providing knowledge, we can enable individuals and organizations to develop sustainable AI practices.

And in the end that…

Advances in artificial intelligence have positive effects but also contribute to environmental concerns such as increased carbon footprints. To solve this problem, sustainable AI focuses on reducing energy consumption and greenhouse gas emissions. This can be achieved through prioritizing data quality, balancing model size, developing energy efficient infrastructure, implementing policies and raising awareness through education.

By implementing these measures, AI can be applied in a more environmentally responsible manner, benefiting society and the planet.