How Can Eco-Conscious AI Help us Save Planet Earth?

Lea Skapetze1

Vrije Universiteit Amsterdam, Netherlands

Abstract

This paper explores the potential of Artificial Intelligence (AI) in advancing a sustainable future for our planet. It posits that with ecologically sensitive development and proper tooling, AI can significantly contribute to addressing our environmental challenges. The discussion unfolds by paralleling AI's fundamental components to nature and nurture—its heuristics and data, respectively—emphasizing their significance in AI’s complex decision-making processes. The concept of eco-conscious heuristics is introduced, advocating for the application of advanced AI models, such as those employing fuzzy logic and quantum logic gates, to reflect the complex nature of ecological issues more accurately. Additionally, the paper emphasizes the role of diverse and purpose-driven data in equipping AI to devise solutions grounded in desired sustainable outcomes. Ultimately, the paper suggests that while AI alone cannot rescue our planet, it stands as a critical ally in the collective endeavor to nurture a healthier Earth.

Keywords: Artificial Intelligence, Ecology, Environment, Computer Science

Introduction

What will this planet look like in 100 years from now? In the year 2124, will humanity have laid the groundwork for a path towards a sustainable Earth that our grandchildren can still inhabit? My generation, the so called “Gen Z”, is perhaps the first generation that is facing these existential questions at a time when we are supposed to make life-defining decisions, such as whether to have children. It is very likely that either our direct offspring, or at least their children, will experience disruptions to their daily lives that is caused by climate change. In other parts of the world, especially in countries less fortunate than Europe in terms of climate, this is already a reality. The immediate effects of climate change are becoming increasingly visible each year. Flooded cities, burning forests, starving children, and dying species may not yet be observable in European countries, but they are occurring right now as I write and as you read. It is unquestionable that we—all living human beings currently inhabiting Earth—must act to mitigate climate change.

The growing attention of climate activists and the rising number of individuals dedicating their lives to protect our planet indicate the issue’s urgency among the public over the past decade. As Charles Darwin put it, the only thing we can be sure of is change. Although there are many different approaches to how change can be induced, we should choose the path that causes the least harm to all living beings on Earth and is the most effective in mitigating the effects of climate change. It must be a realistic approach that succeeds for more than eight billion individuals with eight billion different histories, needs, thoughts, lives.

Evolution has shaped mankind to resist change, as we find comfort and safety in the familiarity of our longstanding habits. It is what brought us here, made us survive, why should we stop doing things we have done and start doing them differently? It is simply not practical to try to convince eight billion people not to eat meat, to forbid all cars and planes, to regulate one hundred percent of industrial production, to cut off any environmentally harming energy sources. We need to utilize the attribute that distinguishes us from any other species: intelligence. If anything can save us and the planet it is ourselves, the collective use of our humanly innate and our artificially created intelligent systems. But how can we intentionally make use of these biological and mechanical intelligent algorithms and networks? Will we be able to create eco-conscious intelligent systems capable of saving our environment and preventing the extinction of humanity on Earth? To answer these questions and explore potential technological solutions, we need as much human brainpower as possible to think, collaborate, create, and induce change.

Taking the greatest strength of Homo Sapiens and attempting to replicate it in non-biological hardware has been a challenge computer scientists and engineers have been working on for many decades. The progress of Artificial Intelligence is exponential and allows hope to arise that it might be what can help us survive and secure a future for my generation’s children and their children. We need to explore and discuss biological and machine algorithms, investigate what they are, to understand how we can use them for the good, to alleviate suffering and preserve the highly fragile ecosystem that allows us to exist.

What is Artificial Intelligence?

Artificial Intelligence (AI) is a multifaceted field of computer science that is primarily defined by two factors: heuristics and data. Heuristics are the backbone of AI decision-making processes, constituting mathematical functions that calculate the probabilities of various events. These functions guide AI as it searches for the correct answer to select, the right path to follow, or the desired action to undertake. Think of heuristics as the 'nature' aspect of AI, equipping it with an intrinsic set of tools to navigate through complex problems and uncertainties.

On the other hand, data represent the 'nurture' aspect of AI, akin to the accumulation of experiences that shape an individual’s view of the world. For AI, data are gathered from its interactions with the environment, including the input it receives and the feedback from its actions. This collection of information is what AI uses to perceive the world and is crucial for learning and adapting. Data allow AI to refine its heuristics, improve its predictions, and make more accurate decisions over time.

The interplay between heuristics and data is what enables AI systems to perform tasks that would typically require human intelligence. Heuristics provide the initial 'instinct' or 'intuition'—the innate capability to process information and make preliminary judgments. In contrast, data offer the empirical evidence and experiential learning that allow AI to grow and evolve beyond its initial programming. Together, they form the foundation upon which AI systems can understand, learn, and ultimately, operate autonomously in a manner that mimics human cognition.

In essence, AI is the emulation of human intelligence processes by machines, especially computer systems. These processes include learning (the acquisition of information and rules for using the information), reasoning (using the rules to reach approximate or definite conclusions), and self-correction. By leveraging both heuristics and data, AI has the potential to transform industries, enhance our decision-making, and push the boundaries of what is technologically possible.

State-Space Environments. A state-space environment is a conceptual landscape where every problem an AI encounters is transformed into a series of potential states that it can exist in. This environment consists of all conceivable conditions or configurations of the problem at hand, where each state represents a unique configuration. In AI, creating a state-space environment is the first step in problem-solving, as it delineates the boundaries within which the AI must operate and defines the transition rules from one state to another, effectively mapping out the problem in a structured, navigable form.

Intelligent Agent. An intelligent agent is an autonomous entity which uses sensors to perceive its environment and effectors to manipulate it. The agent operates within a state-space environment and works towards achieving specific goals. By perceiving the environment through sensors, the agent obtains valuable information that informs its decision-making processes. The effectors allow the agent to perform actions that alter the state of the environment, thereby moving closer to achieving its objectives. The intelligent agent is programmed to maximize its performance measure based on the input from its sensors and its knowledge of the environment.

Search Algorithms. Search algorithms in AI are specialized forms of heuristics that determine the decision-making pathway of an intelligent agent. These algorithms navigate through the state-space environment to find a sequence of actions or a path that leads to a desired goal state. They are essential in problem-solving as they dictate how an agent searches for solutions efficiently. Common search algorithms include breadth-first search (BFS), depth-first search (DFS), A* (A star), hill-climbing, and others. Each algorithm has its own strategy for exploring the state-space, whether it’s expanding nodes level by level as in BFS, delving deep into paths as in DFS, using heuristics to predict the cost to a goal state as in A*, or incrementally improving the current state as in hill-climbing.

The core idea in AI problem-solving is the transformation of any problem an intelligent agent needs to solve into a mathematical function. By mathematically structuring problems, an AI can apply search algorithms and heuristics to navigate the state-space environment systematically. This transformation is pivotal as it translates real-world issues into a language that AI can understand and manipulate, thus enabling the application of algorithmic logic to find solutions.

Machine Learning. Machine Learning (ML) is a subfield of artificial intelligence that involves training a neural network to process and analyze large sets of data. These neural networks are equipped with certain functions and capabilities, such as those derived from search algorithms or mathematical translations of decision-making strategies. The essence of machine learning lies in feeding these networks with data, effectively providing them with experience from which they can learn. The learning process can be supervised or unsupervised. In supervised learning, the neural network is trained with labeled data. The 'labels' guide the network by providing it with clear examples of the input-output mapping it needs to learn. The network integrates this labeled data into its knowledge base, refining its functions to accurately predict or classify new data points. Common applications of supervised learning include classification tasks where data is categorized into groups, regression tasks that involve predicting numerical values, ranking for ordering entities, and collaborative filtering used in recommendation systems. Unsupervised learning, on the other hand, involves training the neural network with data that has no preassigned labels. The network must find patterns and structures in the data on its own. A classic unsupervised learning task is clustering, where the network groups data points into clusters based on their similarities without any prior knowledge of what these groups should be. In both supervised and unsupervised learning, the end goal is to develop a model that can generalize from its training and make accurate predictions or decisions when exposed to new, unseen data.

Large Language Models. As an application of Machine Learning and Artificial Intellligence, Large language models have gained public attention recently with OpenAI launching their Generative Pre-trained Transformer 3.5 (GPT) model, namely Chat-GPT, in November 2022. Large language models are more advanced AI systems that are trained on vast amounts of textual data and process this information through neural networks with potentially billions of parameters (the machine equivalent of synapses). By that these models identify patterns and relationships within the text and learn to understand and generate human language. Language is to this date principal medium for explicit communication among humans, the most important way of building semantic memory and exchanging information derived from it. It is through language that we articulate thoughts, encode what we think in a way that other people are able to decode it and understand what we mean. Sharing knowledge across generations and cultures works by utilizing language. Moreover, language shapes our thought processes, influencing how we perceive and interact with the world around us. It is the importance of language in human evolution that makes artificially intelligent large language models the powerful tools they are.

How to Realize Eco-Conscious AI?

To build AI that is eco-conscious, we must first consider what eco-friendly heuristics entail. These heuristics should enable AI to seek solutions to environmental issues by considering a spectrum of potential impacts and outcomes, rather than a binary good-or-bad approach. The goal is to create AI systems that can navigate the complexities of ecological problems, evaluating the various shades of gray to arrive at the most sustainable outcomes. But how can we implement these nuanced heuristics, and what tools does computer science offer to cope with the complexity of reality?

Traditional heuristics and classification strategies such as k-Nearest Neighbors (kNN), and measures like Euclidean and Hamming distance, are typically employed in machine learning to categorize and predict based on similarity and proximity. However, these methods seem not be sophisticated enough to represent the intricacies of complex environmental issues. These basic algorithms operate under the assumption that closeness in feature space equates to similarity in class or outcome, an assumption that often falls short when applied to the multifaceted nature of any decision.

Take, for example, the seemingly simple problem of determining whether two individuals like each other, depending on how close they are to each other in terms of physical distance but also considering how much their characters differ (Nahemow & Lawton, 1975). While physical proximity can be quantified effectively using Euclidean distance, gauging the similarity of their personalities or values—their "character distance"—requires a more intricate model that can process and evaluate abstract human qualities.

Upon closer examination, the challenge appears to be rooted in a lack of clear definitions rather than in methodology. It is relatively straightforward to train AI models to make decisions when there are clear definitions of right and wrong. However, the waters become muddied when we face decisions for which there is no consensus on the definitions of right and wrong. The concept of similarity in character between individuals, for instance, is nebulous at best. Human intuition or 'gut feeling'—elusive qualities not easily quantified—often guide such assessments.

A potential approach to this conundrum might involve feeding AI models with EEG scans or other representations of human cognitive processes, thus allowing them to 'learn' from our neurological patterns. This could, in theory, enable AI to approximate human intuition and apply it to complex problems. However, the feasibility of this method is not guaranteed. Human cognition is not only immensely complex itself but also subjective and varied across individuals and cultures. The “training data” would be immensely biased.

Let’s therefore make it a methodology problem for now because this we can address within a ten pages opinion paper, whereas defining the right and wrong of any decision affecting the wellbeing of humanity and the environment might be a bit out of scope. The two cornerstones of any AI system, as explained above, would be the heuristics (“the nature”) and the date (“the nurture”). Both can be designed in a way to represent complex problems and weigh in ideological ideas as well as realistic implications.

Eco-Conscious Heuristics

An eco-conscious backbone of AI refers to a system designed with the inherent capability to understand and prioritize environmental sustainability. To embody eco-consciousness, AI heuristics must be sophisticated enough to encapsulate the complexity of ecological systems and the varied impact of human activities on the environment.

Fuzzy Logic. More advanced heuristics can include concepts like fuzzy logic, which allows for reasoning that reflects the ambiguity of real-world situations. Unlike traditional binary logic that classifies inputs into strict true or false categories (“crisp sets”), fuzzy logic introduces degrees of truth (“fuzzy sets”). This is akin to how humans process information; rarely is our reasoning absolute. Instead, we operate in gradients and nuances, which fuzzy sets attempt to capture. A fuzzy set is characterized by a membership function that assigns to each object a degree of membership ranging from zero to one, representing the "fuzziness" of the concept. In environmental applications, this could allow for more nuanced decision-making that better represents the spectrum of environmental health, rather than a simplistic healthy/unhealthy dichotomy. Also, fuzziness could allow an AI system to weigh in individual interests against what is good for society and the environment.

Quantum Logic Gates. Quantum logic gates offer another avenue for enhanced heuristics. These gates form the building blocks of quantum computing, harnessing the principles of quantum mechanics to process information in ways that traditional computers cannot. Quantum logic gates can handle operations on complex superpositions of states, enabling the processing of a vast number of potential outcomes simultaneously. This could be particularly useful in modeling ecological phenomena, which often involve complex interactions and probabilistic events.

In the realm of eco-conscious AI, other considerations might include genetic algorithms that simulate the process of natural selection to arrive at optimal solutions, and agent-based modeling, which can simulate the actions and interactions of autonomous agents to assess their effects on the system as a whole.

Eco-Conscious Data

Goal-oriented programming. A basic idea of AI engineering that could be utilized to design eco-conscious AI systems is feeding the desired state as training data. Hereby the end goal is defined first, and the system works backwards to determine the necessary steps to achieve it. This could be paired with reinforcement learning, where an AI learns to make certain decision through repeated trial and error. The outcome would be an AI system that might know exactly what has to be done at what place by which time to mitigate the effects of climate change on a global as well as individual level.

Diversity. When it comes to training data, diversity is crucial for developing a well-rounded and robust AI system. By utilizing the vast swath of data available on the internet, AI can be exposed to the widest possible range of scenarios, variables, and outcomes. This comprehensive dataset would include information from various geographic locations, socioeconomic backgrounds, and ecological systems, ensuring the AI's decisions are informed by a full spectrum of environmental data. This diversity helps to mitigate biases and overfitting, leading to a more adaptable and generalizable model that can make more accurate predictions and decisions in a wider variety of circumstances.

The success of an eco-conscious AI system is determined by both, the mathematical functions it is based on as well as the data it gains experience through to make the best decision. The best training data set is worth nothing with an insufficient fundament of cognitive infrastructure and optimal heuristics will never lead to the right decision when trained on the wrong data.

The abyss between human and machine intelligence

Artificial Intelligence, while extraordinary, is not a superhuman entity. It shares with the human brain certain limitations, processing the concept of infinity being one of them. AI excels in calculating probabilities and navigating through a vast, albeit finite, array of possibilities. This is why it will always outperform humans in playing any card game, reading (and writing!) any text, listening, and producing any patterns of sound waves that we call music – due to its speed in analyzing a finite number of potential states and choosing the one that is most appropriate to the task it is given.

But what about infinity? AI will try to attempt going through an infinite number of possible states, but it will never return a result. What programmers say when they talk about their code getting stuck in an infinite loop, is some version of a conditional statement “while xyz is true” where xyz always is true. Thus, what distinguishes machine algorithms from biological (neuronal) algorithms is not so much the input and output, the heuristics, and the data, but the speed of traversing through a finite number of possibilities.

Consider a hypothetical scenario: We ask someone to think about every conceivable state of planet Earth at this moment, considering all historical events that have ever taken place, variations of human existence, and every decision ever made, to identify a version of Earth where humanity has made the right choices to solve the climate crisis. This task implies contemplating an infinite number of “parallel universes”. In mathematics, this concept relates to the ergodic theory which says that to any system over a very long time span, the time spent by the system in a microstate with the same energy is proportional to the volume of this region, which means that all possible (“accessible”) microstates of this system are equally probable over this long period of time (Walters, 1982). Whoever we are giving the task to iterate over all these possible microstates of planet Earth, it is impossible for them to solve this insurmountable challenge. But what about machine intelligence? The computational power of AI continues to grow exponentially, surpassing every other intelligent system we know to this day when it comes to the speed of processing information. It is likely that we will create an artificially intelligent system that by traversing over all possible states of planet Earth will identify those scenarios where humanity has made the right choices for environmental preservation and the species’ survival.

It is therefore crucial to look beyond AI’s current manifestations, which predominantly revolve around improving commerce through smart algorithms, ChatGPT improving our academic writing, or the powering of self-driving cars. While these applications are impressive, they barely scratch the surface of AI’s potential. The real power lies in the externalization of human-like intelligence into machines and the utilization of this artificial intelligence not just as a tool for convenience, but as a collaborator in scientific research, a mediator in global conflicts, a key player in sustainable development, guiding humanity to make the right choices. The future of AI Is not just about creating smarter tools, but about building smarter societies, where machine intelligence and human intelligence work in a symbiosis to create a better world for all.

Conflict of Interest

The author declares that there are no conflicts of interest.

Acknowledgment

Generative AI has been used to refine parts of the text presented in this paper.

References

Nahemow, L., & Lawton, M. P. (1975). Similarity and propinquity in friendship formation. Journal of Personality and Social Psychology, 32(2), 205–213. https://doi.org/10.1037/0022-3514.32.2.205

Walters, P. (1982). An Introduction to Ergodic Theory (1st ed.). Springer New York, NY. (Original work published 2000)

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