7. AI - Artificial Intelligence

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:

• Reactive machines – rely solely on input for decision-making and lack learning capabilities.
• Limited memory – can learn and self-improve from past experiences, including many of the AI applications existing today.
• Theory of mind – considered the next major milestone for AI development, capable of understanding and modelling the mental states, emotions and beliefs of humans, enabling social interactions and decision-making based on perceived needs.
• Self-aware AI – possesses consciousness, self-awareness and introspective abilities, understanding not only the mental states of others but also its own existence, goals and emotions.

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.

¹³ What is black box AI?

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.”]

1. Project CETI is a multidisciplinary initiative that employs cutting-edge AI, robotics and linguistics to understand and communicate with sperm whales. Assembling a team of cryptographers, roboticists, linguists, AI experts, technologists and marine biologists, CETI employs advanced machine learning, natural language processing and data science to decode recorded whale sounds and behaviour. The project envisions that their discoveries will provide a foundation for understanding the communication of other animals, both in the ocean and on land.
   
   
2. Forest Foresight is an AI-driven tool developed to prevent illegal deforestation by predicting forest loss up to six months in advance with 80% accuracy. The tool leverages machine learning models to analyse historic satellite images, forest cover changes and other relevant data sets to identify high-risk areas and facilitate timely interventions. Forest Foresight transforms data into actionable insights, with the aim of empowering local communities, law enforcement and policymakers to take action.
   
   
3. iNaturalist is a social media platform for nature enthusiasts that promotes engagement, stewardship and biodiversity data collection by leveraging AI-based species identification. With over a million registered users, iNaturalist's app allows individuals to document and share plant and animal observations, contributing to a global scientific repository. The team is now working on incorporating spatiotemporal information to better distinguish visually similar species and has launched Seek, a kid-friendly app designed for younger observers.
   
   
4. Hack the Poacher is a project that modifies existing camera traps with machine-learning capabilities for remote wildlife monitoring and anti-poaching efforts. The system analyses photos in real time, detecting animals and humans, and sends alerts to rangers if needed.
    
5. ARIES (Artificial Intelligence for Environment & Sustainability) is an open-source, web-based platform that aims to make sustainability assessment faster, and more credible and user-friendly. It utilises AI-driven decision-making rules to quantify and map all aspects of ecosystem service provision, including biophysical generation, flow and extraction. ARIES supports a broad range of physical, social and economic contexts, offering access to a library of sustainability models and spatial datasets across various scales. 
   
   
6. ABSES (Agent-Based Simulation of Ecological Systems) is a project that combines artificial intelligence, ecology, autonomous agents, simulation and machine learning to create a comprehensive multi-agent simulation system for coastal ecological systems.
    
7. Wildlife Insights is a global platform that utilises artificial intelligence and advanced analytics to enable users to share and analyse wildlife data for effective population management. The platform allows users to upload images for automatic species identification, saving time and facilitating data-driven conservation action. Wildlife Insights aims to connect wildlife ‘big data’ to decision-makers, such as land managers, governments, companies, scientists, indigenous communities, citizen scientists, nonprofits and the general public.
   
   
8. Wild Me supports conservationists with the goal of replacing time-consuming manual data-gathering efforts with rapid computation. It utilises AI-based image recognition for individual wildlife identification, enabling data collection from sources like camera traps, aerial surveys, social media and citizen science contributions. Its latest innovation, an autonomous AI agent, scans YouTube nightly for new whale shark videos, often posted by vacationing tourists and divers. This intelligent agent extracts still images, analyses the shark's unique spot pattern and identifies it at the individual level. It also gathers the sighting's date and location, or requests it from the uploader, before submitting everything to a database. Since its inception, the AI agent has located 1900 videos and is continually improving, already outperforming productive human spotters.
   
   
9. The BearID project develops software tools that use deep learning and machine learning techniques to identify individual bears through facial recognition. By applying this technology to remote camera trap imagery, the project aims to provide a new survey technique for the research and monitoring of wild bears. This software tool offers a replicable approach that can be extended to other species, ultimately supporting global conservation efforts at the individual level.
   
   
10. GainForest uses artificial intelligence, machine learning and blockchain technology to combat deforestation and promote sustainable forest management. Developed by AI researcher David Dao, the platform enables anyone to invest in forest conservation and receive rewards based on their conservation outcomes. It utilises satellite imagery and AI image analysis to monitor forests and assess deforestation, machine learning algorithms to predict forest areas at risk, and blockchain-based smart contracts for transparent and secure fund transfers. By offering financial incentives to local communities and stakeholders, GainForest aims to foster widespread participation in forest conservation and fight climate change.
    
    
11. Researchers at IIIT Delhi, led by Ankita Shukla, are developing an intelligent system to monitor and control urban monkey populations using Microsoft cloud and AI tools. The system detects and identifies individual monkeys in images from camera traps and photographers, aiding researchers in locating monkeys that require sterilisation and distributing contraceptive-laden food. The tool uses AI and deep machine learning for facial recognition and identification of age and sex characteristics, allowing for efficient population monitoring and control. The team is also working on a mobile app for the public to contribute location-tagged images of monkeys.

1. Is AI fit for purpose?: Before opting for AI, assess the technology’s suitability for your specific conservation challenge. AI excels at processing large data volumes, recognising patterns and making predictions. It is ideal for tasks requiring pattern recognition, data-driven decision-making and real-time analysis, such as classification, optimisation, anomaly detection and natural language understanding. To evaluate if AI is fit for your purpose, consider the following questions: What are the conservation challenges and the desired outcomes? Is the available data sufficient and accurate enough for an AI system to train on? Can the challenge be framed in terms of classification, prediction, optimisation or pattern recognition? Answering these questions will help determine if AI is a good choice.
   
   
2. Explainability and transparency: Many AI models, particularly deep learning systems, can act as ‘black boxes’, making it difficult to understand how they arrive at their conclusions. This lack of transparency can create challenges in terms of trust, accountability, and decision-making. To address this issue, consider using more explainable AI models or incorporating explainability techniques. For example, Local Interpretable Model-Agnostic Explanations (LIME) is a method that provides an interpretable explanation for individual predictions by approximating the complex model with a simpler, more interpretable one. Engaging with stakeholders to communicate the rationale behind AI-driven decisions and maintaining a transparent development process can also help build trust and foster better collaboration.
   
   
3. The alignment problem: Ensuring that AI models align with your conservation goals as well as ethics is crucial to avoid causing undesirable outcomes or behaviours. The ‘alignment problem' arises from the fact that AI systems are optimised to pursue a specific task without understanding the broader context, human ethical considerations or potential consequences. This optimisation may lead to AI pursuing its goals in harmful or dangerous ways, causing misalignment with human values. For instance, if not carefully designed and monitored, an AI model which is optimised to do whatever it can to collect online data and contact details, may breach privacy regulations or utilise unethical methods to perform its task. Addressing the alignment problem is particularly vital when utilising autonomous AI agents, as they are designed to independently pursue and execute tasks with little supervision. To mitigate this risk, define clear, measurable targets and monitor the model's performance. Additionally, provide autonomous AI agents with clear boundaries regarding permissible actions to ensure they operate within acceptable ethical and practical parameters, and maintain human supervision at all stages.
   
   
4. Hallucination issues: AI models typically work by learning patterns and relationships in the training data and then generalising those patterns to make predictions or generate outputs on new, unseen data. However, when models overfit the data, make inferences based on insufficient or biased information or encounter ambiguous inputs, they can produce seemingly plausible but incorrect or irrelevant outputs – a phenomenon known as hallucination. To detect and mitigate the risks associated with hallucination, implement thorough validation and monitoring processes, use diverse and representative training data and incorporate human expertise to review and validate AI-generated outputs.
   
   
5. Bias and fairness: Bias in AI is a pressing challenge that can arise from various sources, including biased data, algorithm design and societal and cultural factors that influence both data and algorithm development. To mitigate bias, ensure diversity in data collection, monitor and balance datasets, design algorithms with transparency and fairness in mind, and involve a diverse group of stakeholders in the AI’s development. While the adverse social consequences of AI on human inclusivity may be more intuitive, keep in mind that there may be other types of biases, such as under-representation of non-charismatic species or data-insufficient populations, which can affect the AI’s outputs.
   
   
6. Training and capacity building: For organisations to effectively incorporate AI into their conservation efforts, training and capacity building are crucial. As highlighted in this chapter, AI has the potential to reshape conservation practices in the near future, making it essential for staff to have the skills to understand, interpret and effectively use AI applications and outputs. Invest in training and capacity building to equip your staff with the necessary skills to navigate the complexities of AI and leverage its capabilities for conservation outcomes. Seek collaborations with AI experts, researchers, and other conservation organisations to share knowledge, resources, and experiences in implementing AI solutions.
   
   
7. Data quality and availability: Assess the quality, quantity and representativeness of the data available for training AI models. Inadequate or biased data can lead to poor performance and unintended negative consequences.
   
   
8. Generative AI and copyright: When using generative AI to generate content, images or videos, carefully consider the copyright implications of the technology. With recent discussions highlighting the potential legal issues surrounding AI-generated content, organisations should ensure that they have the necessary legal permissions to use such outputs and that they are not infringing on the intellectual property rights of others.
   
   
9. Incorporating Indigenous knowledge in AI: If you consider incorporating traditional ecological knowledge and Indigenous perspectives within AI systems, ensure to stay informed about best practices and various indigenous perspectives with regards to the use of such knowledge in AI.
   
   
10. Accountability and responsibility: Determining who is responsible for the actions and decisions made by AI systems can be challenging. Establish clear lines of accountability and responsibility for AI outcomes and integrate ethical considerations into AI development and deployment processes, and communicate potential limitations and uncertainties associated with AI outcomes to stakeholders.
    
    
11. Balancing automation with human judgement: It is essential to determine the appropriate level of automation versus human intervention in AI systems, striking a balance between efficiency and maintaining human expertise and oversight. While AI can provide valuable insights and analysis in some cases, it should always be used as a supporting tool rather than a sole source of ‘expert’ advice. Misclassification errors, such as falsely flagging people as poachers or labelling a species as harmful to its ecosystem, could lead to serious unintended negative consequences. Thus, balance the use of AI with human judgement and ensure that AI-generated recommendations are carefully evaluated before being acted upon.

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