Large language models can help boost food production, but be mindful of their risks

• URL: http://arxiv.org/abs/2403.15475v1
• Date: Wed, 20 Mar 2024 17:19:25 GMT
• Title: Large language models can help boost food production, but be mindful of their risks
• Authors: Djavan De Clercq, Elias Nehring, Harry Mayne, Adam Mahdi,
• Abstract summary: ChatGPT-style large language models (LLMs) can potentially enhance agricultural efficiency, drive innovation, and inform better policies. But challenges like agricultural misinformation, collection of vast amounts of farmer data, and threats to agricultural jobs are important concerns. The rapid evolution of the LLM landscape underscores the need for agricultural policymakers to think carefully about frameworks and guidelines.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Coverage of ChatGPT-style large language models (LLMs) in the media has focused on their eye-catching achievements, including solving advanced mathematical problems and reaching expert proficiency in medical examinations. But the gradual adoption of LLMs in agriculture, an industry which touches every human life, has received much less public scrutiny. In this short perspective, we examine risks and opportunities related to more widespread adoption of language models in food production systems. While LLMs can potentially enhance agricultural efficiency, drive innovation, and inform better policies, challenges like agricultural misinformation, collection of vast amounts of farmer data, and threats to agricultural jobs are important concerns. The rapid evolution of the LLM landscape underscores the need for agricultural policymakers to think carefully about frameworks and guidelines that ensure the responsible use of LLMs in food production before these technologies become so ingrained that policy intervention becomes challenging.

Related papers

• Persuasion with Large Language Models: a Survey [49.86930318312291]
  Large Language Models (LLMs) have created new disruptive possibilities for persuasive communication. In areas such as politics, marketing, public health, e-commerce, and charitable giving, such LLM Systems have already achieved human-level or even super-human persuasiveness. Our survey suggests that the current and future potential of LLM-based persuasion poses profound ethical and societal risks.
  arXiv  Detail & Related papers  (2024-11-11T10:05:52Z)
• Harnessing Large Vision and Language Models in Agriculture: A Review [3.6673562709926664]
  Large models can help farmers improve production efficiency and harvest by detecting a series of agricultural production tasks. After gaining a deeper understanding of multimodal large language models (MLLM), it can be recognized that problems such as agricultural image processing, agricultural question answering systems, and agricultural machine automation can all be solved by large models.
  arXiv  Detail & Related papers  (2024-07-29T03:47:54Z)
• Application of Machine Learning in Agriculture: Recent Trends and Future Research Avenues [6.0460261046732455]
  Food production is a vital global concern and the potential for an agritech revolution through artificial intelligence (AI) remains largely unexplored. This paper presents a comprehensive review focused on the application of machine learning (ML) in agriculture, aiming to explore its transformative potential in farming practices and efficiency enhancement.
  arXiv  Detail & Related papers  (2024-05-23T17:53:31Z)
• SMARD: A Cost Effective Smart Agro Development Technology for Crops Disease Classification [0.0]
  "SMARD" project aims to strengthen the country's agricultural sector. It provides farmers with information on crop care, seed selection, and disease management best practices. Farmers can also contact the expert panel through text message, voice call, or video call to purchase fertilizer, seeds, and pesticides at low prices.
  arXiv  Detail & Related papers  (2024-05-17T05:14:39Z)
• A Survey on Large Language Models for Critical Societal Domains: Finance, Healthcare, and Law [65.87885628115946]
  Large language models (LLMs) are revolutionizing the landscapes of finance, healthcare, and law. We highlight the instrumental role of LLMs in enhancing diagnostic and treatment methodologies in healthcare, innovating financial analytics, and refining legal interpretation and compliance strategies. We critically examine the ethics for LLM applications in these fields, pointing out the existing ethical concerns and the need for transparent, fair, and robust AI systems.
  arXiv  Detail & Related papers  (2024-05-02T22:43:02Z)
• Overcoming LLM Challenges using RAG-Driven Precision in Coffee Leaf Disease Remediation [0.0]
  This research introduces an innovative AI-driven precision agriculture system, leveraging YOLOv8 for disease identification and Retrieval Augmented Generation (RAG) for context-aware diagnosis. The system integrates sophisticated object detection techniques with language models to address the inherent constraints associated with Large Language Models (LLMs)
  arXiv  Detail & Related papers  (2024-05-02T14:19:25Z)
• Factuality Challenges in the Era of Large Language Models [113.3282633305118]
  Large Language Models (LLMs) generate false, erroneous, or misleading content. LLMs can be exploited for malicious applications. This poses a significant challenge to society in terms of the potential deception of users.
  arXiv  Detail & Related papers  (2023-10-08T14:55:02Z)
• AutoML in the Age of Large Language Models: Current Challenges, Future Opportunities and Risks [62.05741061393927]
  We envision that the two fields can radically push the boundaries of each other through tight integration. By highlighting conceivable synergies, but also risks, we aim to foster further exploration at the intersection of AutoML and LLMs.
  arXiv  Detail & Related papers  (2023-06-13T19:51:22Z)
• On the Risk of Misinformation Pollution with Large Language Models [127.1107824751703]
  We investigate the potential misuse of modern Large Language Models (LLMs) for generating credible-sounding misinformation. Our study reveals that LLMs can act as effective misinformation generators, leading to a significant degradation in the performance of Open-Domain Question Answering (ODQA) systems.
  arXiv  Detail & Related papers  (2023-05-23T04:10:26Z)
• Empowering Agrifood System with Artificial Intelligence: A Survey of the Progress, Challenges and Opportunities [86.89427012495457]
  We review how AI techniques can transform agrifood systems and contribute to the modern agrifood industry. We present a progress review of AI methods in agrifood systems, specifically in agriculture, animal husbandry, and fishery. We highlight potential challenges and promising research opportunities for transforming modern agrifood systems with AI.
  arXiv  Detail & Related papers  (2023-05-03T05:16:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.