Are We Becoming ChatGPT Farmers?

 

My stance on AI in agriculture isn't a simple "for" or "against." Four years ago, when I first raised concerns about AI's potential downsides for agricultural innovation (related post: "How Artificial Intelligence Is Convoluting Agricultural Engineering"), many agreed. Today, it seems everyone's immersed in AI, and critical voices are harder to find. This shift is concerning.

The perception of AI in agriculture has changed dramatically. What was once a focus on machine learning and deep learning has broadened, with everything now labeled simply as "AI." The emergence of generative AI, particularly large language models (LLMs), has further complicated things, leaving many behind.

While some are still focused on traditional machine learning or the intricacies of Bayes' theorem, a quiet revolution is underway. This shift, driven by generative AI, is both exhilarating and terrifying, reminiscent of the impact of dynamite.

 

Are We Becoming ChatGPT Farmers?

The reality is, the game has changed. With tools like ChatGPT, we're seeing a rapid increase in the use of generative AI for code generation, synthetic data generation, and algorithm development within agricultural research. While this might seem like progress, I worry about the potential for a decline in genuine innovation.

In five to ten years, we risk having agricultural "experts" who are essentially just ChatGPT users, lacking a deep understanding of the underlying agricultural principles.

My message to the agricultural community is this: If you're going to engage with AI, do it right. Generative AI is more than just a chatbot. Understand its capabilities and leverage its potential, especially through its API, rather than simply using it for code or image generation. Don't try to become an AI developer. Computer scientists and AI specialists can develop algorithms far better than we can.

Our value lies in our agricultural expertise. We need to leverage AI, not try to become AI developers ourselves. It's about cultivating "authentic expertise" – a deep understanding of agriculture combined with the ability to effectively utilize these powerful new tools. Master prompt engineering, understand API parameters, and learn how to refine the output of these models to solve real-world agricultural problems.

To my fellow agriculturists: AI, IoT, computer vision, software, and robotics are complex, specialized fields. Are the AI projects in our agricultural institutions truly addressing the real challenges?

And to engineers from other disciplines: Agriculture is a complex ecosystem. Developments without deep agricultural knowledge are likely to be irrelevant to farmers and researchers. Know your limits, and when expertise is needed.

If we become hyper-focused on becoming AI experts, we risk becoming obsolete. This is the core message I explore in my articles, and it's a conversation we urgently need to be having in the agricultural community. I understand this might be an unpopular opinion, but I believe it's a necessary one.

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