AI for Sustainability refers to the application of Artificial Intelligence (AI) technologies to address and solve environmental and societal challenges. By leveraging AI’s capabilities, such as data analysis, predictive modeling, and automation, this approach aims to drive ecological preservation, resource efficiency, and social well-being for a more sustainable future.
A research group from Politecnico di Milano has developed a new computing circuit that can execute advanced operations, typical of neural networks for artificial intelligence, in one single operation. The circuit performance in terms of speed and energy consumption paves the way for a new generation of artificial intelligence computing accelerators that are more energy-efficient and more sustainable on a global scale. The study has been recently published in the prestigious Science Advances.
Face or Object Recognization
Recognizing a face or an object, or correctly interpreting a word or a musical tune are operations that are today possible on the most common electronic gadgets, such as smartphones and tablets, thanks to artificial intelligence. For this to happen, complicated neural networks need to be appropriately trained, which is so energetically demanding that, according to some studies, the carbon footprint that derives from the training of a complex neural network can equal the emission of 5 cars throughout their whole life cycle.
To reduce the time and energy consumption of the training, one should develop circuits that are radically different from the conventional approach and that are able to mimic more accurately the structure of the neural networks and the characteristics of the biological synapses.
A typical example is the concept of in-memory computing, where data are processed directly within the memory, exactly like in the human brain. Based on this analogy, the research group at Politecnico di Milano has developed a novel circuit that can execute a mathematical function known as regression in just one operation. For this purpose, they use a resistive memory, also known as a memristor, a device that can memorize any datum (for example the value of a share at a certain time) in the value of its resistance.
By arranging these memory elements within an array with the size of a few micrometers (a few millionths of a meter), the group at Politecnico di Milano has been able to execute a linear regression on a group of data. This operation is capable of determining the straight line that best describes a sequence of data, allowing, for instance, to predict the trend in the stock market based on a simple linear model.
Logistical regression, which allows classifying data within a database, has also been demonstrated. This function is essential for the so-called recommendation systems, which are crucial marketing tools for online purchases.
AI and Environmental Sustainability
Aside from its financial and societal benefits, AI is also set to revolutionize environmental sustainability.
Driving progress in most areas of ecology and biodiversity research as well as environmental ecosystem management.
AI was successful in automating animal identification for 99.3% of the 3.2 million animals. With the same level of accuracy as the crowdsourced groups of human volunteers. Moreover, drones equipped with AI technology can help reduce deforestation and poaching.
AI Sustainability for Organizations
Data provides visibility into an organization’s operations and allows those in charge to see where the business is reaching the bar and where performance has fallen behind. AL works by accelerating the conversion of data into relevant and reliable sustainability insights. To drive real improvement, sustainability data and metrics need to be embedded into core operations processes, functions, and workflows to inform real-time decision-making.
It’s through these thousands, or millions, of daily actions that an organization’s sustainability strategy is brought to life.