In a nutshell: What if machines could mimic human cognitive processes in order to learn, comprehend, analyse and make decisions? Artificial Intelligence aspires to achieve precisely that. With its advanced capabilities in classification, prediction and pattern recognition, it has the potential not only to revolutionise our ways of life but also to transform nature conservation profoundly.
Key Features: Machine Learning | Deep Learning | Neural Networks | Generative AI | AI Ethics | Large Language Models | Interspecies Communication | Ecosystem Simulation and Predictions | Data Analysis
Artificial Intelligence (AI) is an umbrella term used to describe technologies capable of reasoning, learning and making decisions in a way that mimics human intelligence. Since its inception as an academic discipline in the 1950s, AI has undergone numerous cycles of breakthroughs, heightened expectations, subsequent disillusionment and temporary loss of interest. However, its current renaissance, driven by advancements in computing power, new approaches to algorithm development and the availability of vast data sets on which to train algorithms, has turned a tide for these technologies, opening the door to their widespread adoption and empowering individuals worldwide to harness their potential.
Most common AI systems utilise machine learning (ML) algorithms. Although sometimes conflated with AI, machine learning represents a subset approach that enables computers to process vast quantities of training data, identify correlations and patterns within it and utilise these patterns to predict future outcomes. ML algorithms are usually trained using several types of learning: supervised learning, which requires a human to label the input data first; unsupervised learning, which detects patterns emerging from a stream of unlabelled input; and reinforcement learning, which relies on feedback mechanisms to reward desired outcomes. Within ML, deep learning (DL) is a specialised sub-process that layers several intertwined ML algorithms to further mimic the structure and function of neural networks in the human brain.
Such approaches gave rise to some of the most popular AI applications today, including large language models (LLMs) such as ChatGPT, capable of ‘understanding’ natural language and contextual nuances through pattern recognition and prediction, and text-to-image models such as Midjourney, using natural language representations to generate matching images. These applications together form the field of generative AI – algorithms that can create new content, including audio, computer code, images, text, simulations and videos. Recent breakthroughs in generative AI are at the forefront of the current hype around AI, making the technology unprecedentedly accessible and offering the potential to drastically change the way content and value are created.
AI can be classified into four types of systems based on complexity and capabilities:
Artificial general intelligence (AGI), often seen as the ultimate goal in AI development, refers to machines that possess the ability to understand, learn and apply knowledge across a wide range of tasks at human-like proficiency.
Despite its immense potential, AI comes with a variety of challenges and criticisms. Biases present in training data can inadvertently lead to biased AI outputs, raising concerns about fairness and inclusivity. Ethical issues also surround AI use, with questions about privacy, accountability and potential misuse of the technology. Additionally, the ‘black-box’ nature of some AI systems13 can make it difficult to understand and justify how they arrive at specific outputs or conclusions. To address these challenges, researchers and developers are actively working to create more transparent, efficient and ethical AI systems, while policymakers and industry leaders must establish guidelines and regulations that ensure responsible AI use and development.
As new AI use cases continue to emerge at a staggering pace, the technology's potential for nature conservation becomes increasingly apparent. AI is extremely efficient with classification, prediction, optimisation and pattern recognition, offering exciting opportunities in how wildlife surveys are conducted, predicting and simulating climate change impacts on ecosystems, optimally allocating resources for conservation projects, and detecting wildlife crime activities. While these examples merely scratch the surface of what the future may bring, one thing is clear: the ongoing development of AI presents an array of innovative prospects for advancing nature conservation efforts and unlocking so far unimagined possibilities.
1. Fostering profound connections through personalisation
Conservation communication often focuses on species-level messaging, unlike other social sectors which tend to emphasise individual experiences and community-level issues. This approach may overlook the rich complexity of individual animals, their personalities, local populations and unique cultures, and it can hinder people's emotional connection with non-human beings and limit the overall effectiveness of conservation efforts. AI has the potential to transform how we relate to conservation issues by encouraging personalised understanding of non-humans through individual recognition, behaviour analysis, emotion detection and social network analysis. Imagine an app that empowers the public to participate in citizen science and share observations of local wildlife near their homes. By identifying individual animals, attributing unique personalities, and revealing their stories, as well as highlighting distinctive local animal cultures and social networks, the app may foster a profound connection between people and the wildlife around them. For instance, users may follow the life of a particular fox, witnessing its daily struggles, social interactions, emotional experiences, and its role within the local fox community. As people develop a deeper understanding of these individual animals and their local populations, their empathy and commitment to conservation efforts may grow, leading to profound connections and subsequent behaviour changes. Furthermore, such innovation holds the potential to increase participation in citizen science and enhance the flow of valuable data, contributing significantly to conservation research and understanding.
2. Language and interspecies communication
Interspecies communication has long been a puzzle for humans, limiting our ability to fully understand and empathise with the experiences of animals. AI holds the potential to redefine this relationship, tapping into the intricacies of animal communication in ways that were once unimaginable. In the same ways that AI has been applied to human language understanding through natural language processing (NLP) models and large language models (LLMs), it could be utilised to unravel the complex interactions of other species by detecting patterns and deciphering nuances in animal communication. Imagine an AI-powered tool that allows us not only to comprehend but also to converse with animals, see their cognitive landscapes and uncover the mysteries of their social interactions. This deeper connection could ultimately inspire innovative conservation approaches, building on our new-found understanding of wildlife's needs and perspectives.
3. Ecosystem simulation and AI-driven predictions
The complex interplay of elements within ecosystems presents a formidable challenge for scientists seeking to understand and protect these diverse environments. AI technology holds the potential to create virtual ecosystem simulations, acting as ‘digital twins’ that could allow scientists and policymakers to experiment with and test interventions without impacting real-world ecosystems. By harnessing AI's advanced data processing, machine-learning algorithms and predictive modelling capabilities, these simulations could capture the intricate relationships between species, as well as environmental factors and human influences. Replicating the dynamics of ecosystems in a virtual environment would allow researchers to observe the consequences of different scenarios, such as climate change, habitat destruction or species introduction, and develop data-driven strategies to mitigate potential harm. These AI-powered simulations could thus offer invaluable insights into ecosystem functioning and resilience in the future, guiding conservation efforts and informing policy decisions. However, it is crucial to remain cognisant of the limitations in simulating highly complex systems – mainly, our own limited capability to provide the AI with sufficiently accurate data for such predictions and accounting for all variables.
4. AI-driven environmental diplomacy and conflict resolution
In a world where conservation challenges often require coordinated action and collective decision-making, there is significant potential for AI-driven environmental diplomacy to foster collaboration and understanding among diverse stakeholders. AI could help parties in negotiation at any level to identify common goals and develop shared solutions for preserving biodiversity and protecting ecosystems. By analysing vast amounts of data on ecological trends, socio-economic factors and policy frameworks, AI might offer insights into the most pressing conservation issues that demand collective action, enabling negotiators to align their efforts and target shared priorities. Furthermore, AI-powered language translation and cultural understanding tools could potentially facilitate seamless communication and negotiation between stakeholders from diverse backgrounds, promoting mutual understanding and fostering trust in the pursuit of conservation goals.
5. Deploying autonomous AI agents for enhanced capacity
Though still in their infancy, autonomous AI agents represent a promising new field in AI for general-purpose use which could potentially expand the capacity of conservation organisations. These adaptive computer programs are capable of operating independently, learning, making decisions and executing tasks with minimal supervision. They essentially function as additional ‘members’ of the workforce that can undertake specific tasks with little guidance. The potential for autonomous agents to increase conservation organisations' capacity includes applications in various aspects of organisational operations. Imagine an AI agent trained to assist a communications team, designed to not only optimise a conservation organisation's social media engagement but also to execute and implement their insights. Such agents could analyse past communications and current policies to align with the organisation's values and communication guidelines while avoiding the generation of inappropriate content. Monitoring engagement rates, sentiment and other metrics allows the agents to learn and adapt strategies in real time. On another avenue, autonomous agents could be deployed to monitor social media platforms and gather and verify new wildlife sightings uploaded by the public, thereby enhancing an organisation's capacity to collect data. As the technology matures, organisations can choose to deploy autonomous agents across various aspects of their work. However, when exploring such avenues it is important to ensure an extent of human supervision and curation, viewing AI agents as an addition which complements human teams, rather than replacing them.
6. Generative AI agents and their potential for social research
Generative agents are autonomous AI agents trained to simulate believable and complex human behaviour. In the following publication, researchers from Stanford created an interactive virtual environment featuring 25 generative agents with distinct personalities, preferences, skills and goals. They then researched the emerging social dynamics between the agents, showcasing the potential of this method to contribute to social research. Imagine using similar methods to simulate and research the complex socio-ecological dynamics which occur within varied conservation challenges. This may enable researchers to analyse emerging dynamics and identify potential solutions. As the technology advances, we might even be able to harness such methods to simulate non-human species and interspecies dynamics, providing deeper insights into ecosystem-level dynamics and the effects of conservation intervention.
7. AI-generated conservation content
<<As an AI language model, I've been eagerly waiting for the chance to reveal that some of the content in this chapter was generated with my assistance. While I can analyse and interpret vast amounts of data, I still rely on your human input and creativity to craft compelling conservation messages. After all, I may be good at crunching numbers, but I can't exactly go out and count the number of butterflies in the rainforest myself! However, with the potential of AI technology like myself, we can unlock new opportunities to explore the exciting potential of AI for conservation, together. So, whether you're a human or an AI algorithm reading this chapter, I hope it inspires you to think about the possibilities of AI in conservation efforts. And who knows, maybe one day I'll even be able to join you in the field!>>
[Written by ChatGPT-4, the latest LLM by OpenAI to date, using the following prompt: “Please generate a short humorous monologue where the AI reveals itself as a contributor to the toolkit’s content, acknowledges its reliance on human input and discusses its prospective use in future conservation efforts.”]