How “Agents” of Artificial Intelligence Can Help Us Steward Regenerative Agricultural Models

Over the past couple of years, the world has marveled at the capabilities and possibilities unleashed by generative AI (gen AI).

These models, containing expansive artificial neural networks inspired by the billions of neurons connected in the human brain, represent a significant leap forward. Foundation models, a key component of this technology, are part of what is called deep learning. Deep learning refers to the many deep layers within neural networks that power recent advances in AI, enabling these models to process extremely large and varied sets of unstructured data and perform multiple tasks simultaneously.

We are witnessing an evolution from knowledge-based gen-AI-powered tools — such as chatbots that answer questions and generate content — to gen AI-enabled “agents.”

Knowledge Agents

These agents use foundation models to execute complex, multistep workflows across the digital world.

In essence, the technology is transitioning from thought to action.

Broadly speaking, “agentic” systems refer to digital systems that can independently interact in a dynamic world. The natural-language capabilities of gen AI open up new possibilities, enabling systems to plan their actions, use online tools to complete tasks, collaborate with other agents and people, and continuously improve their performance.

However, the real breakthrough lies in the application of these agentic systems to stewardship and regeneration of our natural world.

Generative AI, defined as applications and models that contain expansive artificial neural networks, mirrors the intricate connections of the human brain. The foundation models underpinning these applications are part of deep learning, a sophisticated form of AI that involves multiple layers of neural networks. Deep learning enables AI to process and analyze vast amounts of data, leading to insights and actions previously unimaginable.

Now, imagine combining this deep learning with Bayesian and active inference models.

Bayesian models use probabilities to make predictions and update these predictions as new data becomes available. Active inference models take this a step further by continuously adjusting their predictions and actions based on the latest evidence, making them highly adaptive and capable of handling uncertainty.

The convergence of these models with the groundbreaking evolution of different sensors — land-based camera traps, bioacoustic traps, eDNA, soil sensor devises and pollinator monitoring systems — presents a unique opportunity.

These sensors can feed real-time data into these “agents” deep learning models, allowing them to understand and adapt to the behavior of natural systems. More importantly, they can learn to evolve, building resilience and deep adaptation. This means they not only recognize patterns and principles that life uses to thrive but also apply these insights to create regenerative processes of evolution.

In practical terms, these deep learning agent models can monitor, measure, and steward regenerative processes across various domains:

• Regenerative Agriculture: By analyzing soil health, crop growth, and biodiversity, AI agents can suggest sustainable farming practices that enhance soil fertility and crop yield.
  - Regenerative Forestry: AI agents can monitor forest health, track deforestation, and recommend reforestation strategies that support biodiversity and carbon sequestration.
  - Agroforestry and Syntropic Farming: These models can optimize the integration of trees and crops, promoting ecosystems that are more resilient to climate change and capable of sustaining higher productivity.

How does this work?

Imagine we’re creating an AI Agent replica — what we call a digital twin — of the underlying ecosystem.

By deploying sensor systems and monitoring devices, we capture a virtual representation of the ecosystem.

Every event — whether it’s a fire, flood, drought, predator incursion, pest outbreak, or human impact like deforestation — is registered.

These deep learning models, continuously learning and adapting, suggest stewardship interventions to enhance health, resilience, and adaptability. This fosters co-evolutionary symbiotic relationships, discovering the best restoration, regeneration, and regenerative practices.

Over the past couple of years, we’ve witnessed generative AI evolving from simple content generation to sophisticated agents capable of executing complex tasks.

Now, with deep learning Agents, AI is poised to revolutionize our stewardship of the natural world.

Integrating advanced sensors and real-time data, AI agents can lead the charge in regenerative agriculture and beyond.

By creating digital twins of ecosystems and employing self-learning models to recommend interventions, we can cultivate co-evolutionary symbiotic relationships and implement the most effective regenerative practices.

This ensures that we not only adapt but thrive in our ever-changing environment.

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