AI will save the world
Only if it becomes green
Daring? Absolutely, but behind this futuristic vision lies a tangible reality. While the title might appear provocative, by exploring the potential of Green AI, we can uncover how artificial intelligence is indeed playing a significant role in the pursuit of sustainable solutions to address global environmental challenges. However, caution is warranted; it's essential to view this reality as a two-faced Janus entity. In fact, AI can - at the same time - both save and endanger the environment we inhabit.
AI can play a vital role in searching for solutions to support sustainability and the environment
From a positive point of view, AI can play (and is already doing so!) a vital role in searching for solutions to support sustainability and the environment. For example, by leveraging data collected from machinery and IoT devices, it is possible to reduce energy consumption or material waste. In the context of smart cities, historical mobility and behavior prediction data provide a fundamental contribution to making our cities more livable.
The use of AI, however, can pose risks to the environment. For instance, the implementation of AI infrastructure requires a significant amount of energy and resources, especially for data processing in large data centers. Additionally, widespread use of AI could lead to an increase in the production and disposal of electronic devices, contributing to the rise in electronic waste.
- High Energy Consumption in Data Centers: training and running artificial intelligence models require a significant amount of energy, especially in data centers. This can have a substantial impact on the environment, contributing to carbon emissions and the use of non-renewable energy resources. It is important to explore solutions to reduce energy consumption, such as model optimization, the adoption of energy-efficient hardware, and the use of renewable energy sources to power data centers.
- Training machine learning models requires a vast amount of data, and maintaining a historical record of this data over time can consume a significant amount of resources, including storage space, energy, and processing capacity. This can have an environmental impact, as data centers hosting these resources consume energy and generate waste. In the development of new models and algorithms, it is important to question which and how much information is actually valuable: when it comes to data, it's easy to accumulate too much of low-added-value data.
- Electronic Waste Generation: artificial intelligence relies on sophisticated hardware, such as servers, storage devices, and sensors, which can contribute to the growing production of electronic waste. It is essential to explore strategies to address this issue, such as responsible recycling of electronic components, extending the useful life of devices, and adopting modular design to facilitate the replacement and upgrading of obsolete components.
- Use of Unsustainable Materials: Some components of the hardware used in artificial intelligence may require the use of expensive, rare, or unsustainable materials. This raises concerns about resource availability and the environmental impact associated with the extraction and production of such materials.
Are there solutions to these problems?
Green AI focuses on creating energy-efficient machine learning models, optimizing data collection processes, and utilizing adaptation and model evolution techniques that are environmentally responsible in terms of resource management.
The traditional lifecycle of a machine learning algorithm involves starting from scratch whenever you want to update the model or train it for a new task. Typically, in this process, you either expand the training dataset with new data or modify the model's structure, allowing it to evolve over time. This process presents two major challenges. On one hand, it's necessary to maintain a memory of all the data over time, leading to resource usage and privacy risks. On the other hand, starting the training process from scratch consumes a significant amount of energy, especially for more advanced models.
The aspect of greater impact is to directly intervene in the development of innovative algorithms and training paradigms that require fewer computational resources
Large technology companies like Google and Microsoft are working to optimize the energy efficiency of their data centers, which house the servers used for training and executing artificial intelligence models. This includes using more efficient cooling systems, optimizing infrastructure, and adopting low-energy consumption technologies.
Some companies are developing specialized hardware for artificial intelligence, such as high-performance graphics processing units (GPUs) or artificial intelligence accelerator processing units. These devices are designed to perform AI operations more energy-efficiently compared to general-purpose computing solutions.
The most important aspect, and potentially of greater impact, is to directly intervene in the development of innovative algorithms and training paradigms that require fewer computational resources. In this regard, the most promising methodologies belong to the so-called Continual Learning. These techniques allow models to evolve over time without starting from scratch each time, but by retaining memory of what has already been learned, thus avoiding the need for lengthy retraining or maintaining overly deep data history.
In summary, awareness of the environmental issues caused by AI urges us to act responsibly, promoting the research and development of Green AI. As Luciano Floridi states, "Green and blue are two colors that will save the world." This phrase emphasizes the importance of combining the environment (represented by green) and digital technologies (represented by blue) to address the challenges of the 21st century. Green AI, which merges artificial intelligence and environmental sustainability, will be crucial for humanity. By harnessing the potential of digital technologies in a responsible and sustainable manner, we can work towards a future where AI is an ally in solving environmental problems and improving people's lives.