In the vast and intricate world of plant care, the future is blossoming with incredible potential - and a large portion of that potential is being fueled by advancements in artificial intelligence (AI) and Machine Learning (ML).
Imagine having an on-demand botanical or horticultural expert that's available 24/7, capable of answering your questions from the basic to the complex, and continuously learning about the latest research and plant care techniques. This isn't a dream of the future – it's largely a reality today, thanks to large language AI models like GPT-4 and Claude 2.
It is even possible to run your own local large language model with surprisingly decent performance now with a modern "Gaming" PC, thanks to major advancements in open source AI models like Mistral and the inferencing software used to chat with them (I sense a future blog post about this here!).
The Challenge of Information Overload
In a time when information overload is prevalent, finding trustworthy and unbiased guidance about plant care can be like searching for a needle in a haystack. The internet, with its vast array of content, is a double-edged sword. While it provides us with an ocean of knowledge, it also harbors biases and misinformation. These can often be driven by competing interests like corporate agendas, the pursuit of viral fame, or simply a lack of accurate knowledge. In this landscape, the quality and reliability of content can become dubious.
The challenge is further compounded by the sheer volume of information available. This glut of information makes discerning high-quality, evidence-based advice exceptionally difficult. Well-intentioned but underinformed influencers and social media users often further propagate or perpetuate myths. From blog posts and academic articles to social media posts and videos, the volume of plant care and horticultural advice online is staggering, making it difficult for individuals, especially those new to plant care, to discern which advice is reliable and which is not. AI may, in fact, not only help with generating content and data, but possibly curating and selecting from it in a more efficient way than humans can alone, as well.
AI: A (Potential) Beacon of Reliability
We may find AI really shines for certain use cases over others - horticulture may very well be one of the premier fields for the emerging technology. With AI-powered plant care assistance, you could theoretically bypass the noise and bias of the internet at large, and gain access to information that is free from the influence of ulterior motives, assuming the dataset the AI was trained on is free from these negative influences or polluted data. AI researches are finding interesting and novel ways to incorporate new information and extend the knowledge and expertise of models for certain esoteric fields.
The algorithms driving most AI models are designed to sift through extensive troves of data, that itself has come from either humans or other bots. One would hope that an AI offering horticultural advice would be trained on a plethora of plant care resources, scientific studies, and expert advice so that it could provide you with accurate and impartial insights - transparency in training techniques, training data, and development of tools to verify information accuracy in this regard will be critical for the future of integrating AI technology into our world and workplaces, in my opinion.
It's important to remember that the quality of AI advice is only as good as the data it's trained on. If the training data is biased or inaccurate, the AI's advice will reflect those flaws. This is why it's crucial to use high-quality, unbiased data when training a base model or extending the knowledgebase an AI model has access to, and that the right model for the right task is used.
The Human vs AI Margin of Error
At this point in time, it's definitely worth mentioning that these large language AI models still occasionally get it wrong (and I mean, convincingly and confidently wrong), but the results are incredibly interesting especially when compared to the human margin of error in advising on similarly complex techniques (like horticultural tasks).
The gap is not extremely wide, as many may suspect - we are not far off from a revolution in knowledge where an "expert" is available "on-demand" to answer any question in extremely helpful detail. This is not to say that AI will replace human experts or teachers, but rather that it can serve as a valuable tool to supplement human knowledge and expertise. I can't tell you how many situations I've ran into, where I would have gladly paid $50 or more to just pick an expert's brain on a chosen subject for a little while - and AI is exceptional for this, in my opinion.
The Limitations of Traditional Platforms
Platforms like YouTube, although brimming with plant care content, can be a minefield of biased, incorrect, or false information. Content creators may be driven by sponsorship deals, advertising revenue, or personal motivations, which can cloud their objectivity. People may just be flat out factually incorrect a surpringly large amount of the time. However, AI models are not swayed by these external pressures. Their purpose is to equip you with the most factual and well-rounded advice available.
However, AI is not immune to limitations of the data it learns from. An AI model trained on YouTube videos may propagate the same biases.
Using diverse, high-quality sources is crucial during initial training or extending or "fine-tuning" an existing AI model's knowledge (without retraining the whole model). Additionally, filtering irrelevant information from datasets improves model performance. Without careful data cleaning and curation, large language models can become polluted by irrelevant context. This is important because AI models parse prompts into segments called tokens in order to understand context - but they have a limited token capacity before performance declines or their working memory becomes overloaded. Employing experimental techniques to expand context also introduces new challenges that can negatively impact the model. Therefore, quality datasets and judicious data selection helps AI models focus on relevant tokens and improves results.
The Limitations of AI
However, It is very important to approach AI-generated insights (and really any content being passed around today as fact) with a healthy dose of critical thinking and verification. With advancements in AI technology, such as the continuous development of models like GPT-4, we may have a tool that is able to more easily distinguish between troublesome "advice" and more accurate and reliable information, or collaborate with us humans on many projects or topics in place of hiring an expert.
But even the most advanced AI models have their limitations. They can't replace the nuanced understanding and experience of a human expert, and they can sometimes make mistakes or oversimplify complex topics. They're also generally bad at interpreting subtle queues, emotions, or in some cases, the way they interpret information given to them is too literal. AI and Large Language Models also generally fail at providing the pragmatic personal touch and insight that comes more natural for us through human communication.
Moreover, AI models can generally only provide advice based on the data they've been trained on. They can't "think" creatively or outside the box, like a human can, and they can't consider factors that weren't included in their training data. This is why it's crucial to always use AI advice as a tool, not a definitive answer, and to consult with human experts when necessary.
While AI has the potential to greatly enhance our access to reliable plant care advice, it's not a magic solution. It's a tool that, when used correctly, can help us navigate the vast ocean of information and find reliable, unbiased advice. But like any tool, it needs to be used with care and understanding. As we continue to develop and refine AI technology, we must also continue to critically evaluate its advice and use it responsibly.
The Future of AI and Plant Care
As the integration of AI technology with plant care, equipment, and sensors deepens, we can anticipate even greater benefits. Picture a future where AI interacts seamlessly with your smart garden, analyzing data from IoT sensors, and offering personalized recommendations for optimizing growth conditions, and making changes to care routines on-the-fly using data gleaned from successes. The potential for an interconnected ecosystem where AI is constantly improving while acting as a knowledgeable guide or system controller is truly remarkable.
In the spirit of showcasing the progress of AI technology, I'm delighted to also share that I have developed a simple gardening advice bot called "Garrden" on the Poe platform for readers to experience firsthand. Its purpose is to demonstrate how far the field has come and to provide you with a glimpse of the potential of AI in plant care.
You can try it yourself at poe.com/Garrden - It's important to clarify that this example serves as a demonstration only, and I do not earn anything from it. It's offered to you as a tool to explore and enjoy.
The Impact of AI on Labor
The rise of AI in plant care also has significant implications for labor. Physical AI laborers, such as robotic harvesters, are already being used in large-scale farming operations. These machines can work around the clock, unaffected by harsh weather conditions or fatigue, and in some cases on GPS auto-pilot. While this can lead to increased efficiency and productivity, it also raises concerns about job displacement. As AI continues to advance, it's crucial to consider how we can balance technological progress with job security and fair labor practices. I'll probably be discussing this in a future blog post, as the potential impact on the world, and its food and crop production, can't be overstated.
AI and Migrant Workers
In many countries, agriculture relies heavily on manual and migrant workers, who often work under challenging conditions for low pay. As AI becomes more prevalent in this sector, these workers could face job loss. However, there is also potential for AI to improve working conditions by taking over the most physically demanding tasks, allowing workers to focus on less strenuous and potentially more rewarding roles. It's a delicate balance that requires careful thought and policy-making.
The Environmental Impact of AI in Plant Care
AI also has the potential to significantly impact the environment. On one hand, AI can help us manage resources more efficiently, reducing waste and minimizing the environmental footprint of agriculture. For instance, AI can analyze soil and weather data to optimize irrigation, reducing water usage, or AI powered drones may be able to spot-apply pesticides or herbicides for minimal ecological collateral damage.
On the other hand, the production and operation of AI technologies overall requires significant energy, and the environmental impact of this energy use must be considered.
Trust, but Verify
Furthermore, as we explore the possibilities of AI in plant care, it is crucial to remember the age-old adage: "trust, but verify". While AI can provide a deluge of potential recommendations and collaboration at this point in time, the responses and data given over by AI should be validated against known best practices and information, and the judgement of veteran gardeners. The risks of blindly following AI guidance without input from plant biology experts, given AI's limitations in reasoning and judgment, seems a similar pitfall to blindly trusting any source of internet information. When lives are at stake, as with medicinal plants, verification is especially prudent.
Conclusion
In conclusion, please feel free to explore the AI-powered gardening advice bot, experiment with its recommendations, and witness firsthand the progress that technology has made (or laugh at the progress we're yet to make still!).
Realizing the benefits of AI in agriculture requires responsible development and collaboration between technologists, domain experts, farmers, and other stakeholders. While AI technologies can enhance efficiency and sustainability, rigorous testing and oversight are crucial.
As research continues, priorities like personalized crop care and farming AI could further optimize agriculture while reducing environmental impacts. But AI is no panacea – proactive governance and ethics standards are vital to address risks and limitations. With responsible implementation, AI can be part of the toolkit, along with human ingenuity, that creates a more sustainable future.
Article edited 11/17/2023