Green AI: How technology can drive sustainability without sacrificing the planet

Artificial intelligence is playing an increasingly vital role in sustainability efforts, offering innovative solutions to some of the world's most pressing environmental challenges.

This is because AI-powered systems can analyze vast amounts of data in real time, helping to detect and mitigate issues across various industries.

For instance:

In urban planning, AI enables smarter traffic management, reducing congestion and lowering emissions through predictive modeling and intelligent transportation networks.

In the energy sector, AI enhances efficiency by optimizing power grids, forecasting energy demand, and integrating renewable sources like wind and solar more effectively.

Similarly, in agriculture, AI-driven precision farming techniques help monitor soil conditions, predict weather patterns, and reduce water and pesticide use, leading to more sustainable food production.

AI is also a cornerstone of the smart cities movement, where it supports the development of more efficient urban environments, helping optimize public transportation routes, manage waste collection, improve energy use in buildings, and monitor air quality.

AI supports sustainability in tourism by tracking environmental impacts, promoting eco-friendly options, and optimizing resource use. It helps reduce waste, improve energy efficiency, and encourage responsible travel behaviors. Additionally, AI can guide travelers toward sustainable destinations and experiences.

Overall, AI enables businesses and governments to measure and improve their sustainability initiatives, using data-driven insights to lower carbon footprints and make more responsible decisions.

And yet, there's an undeniable paradox: while AI can drive sustainability, it can also be inherently unsustainable, meaning it comes with its own set of sustainability challenges that need careful attention.

The balance between leveraging AI's potential for environmental good and addressing its inherent environmental costs is a pressing concern.

• Energy consumption: AI models, especially deep learning ones, require substantial computational power, leading to high energy consumption. This contributes to a significant carbon footprint, especially when data centers rely on fossil fuels. As AI models become more complex, the energy demand increases, further expanding their environmental impact.

• Water usage: Cooling systems in data centers consume large amounts of water to manage the heat generated by AI systems. This places a strain on local water resources, particularly in regions already facing water scarcity.

• Resource-intensive data centers: Data centers, which power AI, consume significant electricity, often sourced from non-renewable energy. The growing demand for AI services adds pressure on the grid, worsening the carbon footprint of the technology.

• Complexity of models: As AI models become more complex, they require increasingly powerful hardware and larger datasets, leading to higher energy usage and storage needs. This amplifies the environmental impact, as these models consume more resources to train and deploy.

• Environmental footprint of data storage: Storing and processing the vast amounts of data used by AI models requires significant energy. Often, much of the data is rarely accessed, contributing further to the environmental burden.

• Balancing AI's impact with benefits: The key challenge is to balance AI's environmental benefits with its own resource consumption. Solutions like energy-efficient models, renewable-powered data centers, and localized computing can help mitigate AI's ecological footprint.

Green AI, also known as sustainability-friendly AI, is an approach that focuses on creating artificial intelligence systems that are both high-performing and environmentally responsible.

Unlike traditional AI, which has historically been driven by the primary goal of improving model accuracy and performance, Green AI emphasizes minimizing the environmental impact associated with developing and deploying AI technologies.

The core principle of sustainable AI is the reduction of resource consumption, such as energy, water, and computational power, without compromising the performance of the models.

Achieving this involves a variety of strategies, such as optimizing algorithms to reduce training time, using energy-efficient hardware, and applying techniques like model compression and transfer learning.

These methods can significantly reduce the computational demands of training AI models, leading to lower energy consumption and a smaller carbon footprint.

Sustainable AI also prioritizes the use of renewable energy sources to power data centers and AI infrastructure.

Traditional data centers, which are essential for storing and processing massive datasets, are responsible for a significant portion of global carbon emissions. By transitioning to cleaner energy sources like wind, solar, or hydropower, AI systems can greatly reduce their environmental impact.

Furthermore, carbon-aware computing is becoming an important consideration in AI development. This practice ensures that AI systems are optimized for energy efficiency throughout their operation, taking into account the energy consumption and environmental impact of the infrastructure at all stages.

Incorporating these sustainable practices into AI development helps to balance technological progress with environmental responsibility.

As AI continues to evolve and become an even more integral part of addressing global challenges, sustainable AI ensures that the benefits of AI are not only realized in terms of performance but also contribute positively to the planet's future.

As leaders in the AI field, particularly in Italy and across Europe, we take great pride in our contributions to technological innovation and advancement.

However, with this leadership comes an equally important responsibility: ensuring that our progress is sustainable.

As AI continues to evolve and reshape industries, it is crucial that we remain mindful of its environmental impact.

Balancing innovation with sustainability is not only an ethical obligation but also an opportunity to set a global example for integrating green practices into cutting-edge technologies.

By committing to responsible AI development, we can lead the way in shaping a future that prioritizes both technological advancement and environmental stewardship.

The 2023 Sustainability Report states that Almawave is deeply committed to supporting a responsible and inclusive digital transition. Last year, the Group decided to adopt a sustainability strategy with specific goals and targets across each pillar of its ESG model: Governance, Responsible Digital Transition, People and Environment.

Almawave has also formalized its commitment to promoting sustainable operations through environmental protection in its strategy. We carefully monitor our environmental impacts, measuring greenhouse gas emissions, energy and water consumption, and working to reduce waste production.

In 2023, Almawave earned ISO 14001 certification and launched new green solutions, including solutions for managing public green spaces, water networks, and gas distribution.

We also also focus on energy efficiency, renewable energy use, and reducing emissions.

By the end of 2025, Almawave aims to migrate all processes to environmentally efficient cloud systems and reduce its carbon footprint.

Other initiatives include electric vehicle policies, smart mobility, and circular economy efforts, such as donating decommissioned goods.

Almawave's sustainability offering

Almawave offers AI-driven solutions that support sustainability and inclusive development across various sectors. These solutions include platforms that help organizations assess and improve their social and environmental impact, both internally and within their supply chains.

We also provide tools to assist municipalities and urban stakeholders in evaluating and managing city environments, with a focus on public green spaces.

Additionally, our location-based analytics help businesses assess the perceived quality of services across various areas, including those related to sustainability.

These solutions empower businesses to enhance their sustainability efforts while aiding digital transformation in a way that supports both economic and environmental goals.

Velvet: Green AI with sustainability at its core

As sustainability becomes a key priority, AI must evolve responsibly, particularly since it's one of the most energy-intensive technologies today.

AI data centers, for example, are estimated to account for around 1% of Italy's total energy consumption.

With this in mind, Velvet, developed by Almawave, focuses on lighter, edge-based models and multi-agent architectures to reduce energy consumption and operating costs, improving overall system sustainability without compromising performance.

The goal is to run (and train) models on smaller, more efficient clusters, instead of massive, energy-hungry server farms. This reduces the need for transmitting large volumes of data, lowering carbon footprints and conserving resources like energy and water.

Velvet_14B and Velvet_2B models, with the latter serving as the foundation for Italy's first Mixture of Experts model, combines specialized sub-models that activate selectively to tackle complex tasks with efficiency and precision.

Moreover, Velvet is integrated into AIWave, Almawave's multi-agent platform, which distributes tasks across agents to optimize system requests.

This uses various algorithms (not necessarily generative) depending on the task at hand. By ensuring AI is applied only where necessary, Velvet enhances both operational efficiency and scalable data management.

In this context, the indiscriminate use of generative AI is not only inefficient, but unnecessary.

Velvet embodies sustainable AI by reducing environmental impact and improving operational efficiency, all within a Green AI framework.