摘要
由一名新闻记者-机器人与机器学习的工作人员新闻编辑每日新闻-人工智能的新数据在一份新的报告中呈现。根据NewsRx编辑在塔斯马尼亚大学的新闻报道，研究表明，“主角声称，Artif Intelligence(AI)正在彻底改变同时代的思维面貌。”这项研究的资金支持包括谷物研发公司。新闻记者从塔斯马尼亚大学的研究中获得了一句话：“在这里，我们权威地回顾了人工智能和机器学习在灌溉农业中的应用现状，评估了广泛存在的人工智能方法的潜力和相关挑战。我们认为，随着人工智能灌溉系统的开发人员可能从以人为中心的人工智能中受益，这是一种捕捉不同最终用户观点的新生算法。”行为和行动，通过反复的利益相关反馈，有可能促进对拟议系统的完善。人工智能引导的人机协作可以简化用户需求的整合，允许针对情景农场管理的调整进行定制。以直观、清晰和可操作的形式向特定人员和外行人展示大数据也迫切需要注意：在这里，人工智能可解释的可解释性可能有助于利用人类的专业知识。使最终用户能够在人工智能管道中为定制输出贡献经验。转移学习在将基于场所的人工智能应用到农业生态地区、生产系统或企业组合方面有希望，即使在有限的数据输入下也是如此。我们发现，近年来人工智能科学和软件开发的速度已经超过了充分的法律和制度法规的发展速度，而且往往是社会性的。"经营的道德和道德许可,揭示与数据所有权、合法性和信任相关的消费者问题."
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Fresh data on artificial intelligence are presented in a new report. According to news reporting out of the University of Tasmania by NewsRx editors, research stated, "Protagonists allege that artif icial intelligence (AI) is revolutionising contemporaneous mindscapes." Financial supporters for this research include Grains Research And Development C orporation. The news journalists obtained a quote from the research from University of Tasma nia: "Here, we authoritatively review the status quo of AI and machine learning application in irrigated agriculture, evaluating the potential of, and challenge s associated with, a wide range of existential AI approaches. We contend that as piring developers of AI irrigation systems may benefit from human-centred AI, a nascent algorithm that captures diverse end-user views, behaviours and actions, potentially facilitating refinement of proposed systems through iterative stakeh older feedback. AI-guided human-machine collaboration can streamline integration of user needs, allowing customisation towards situational farm management adapt ation. Presentation of big data in intuitive, legible and actionable forms for s pecialists and laypeople also urgently requires attention: here, AI-explainable interpretability may help harness human expertise, enabling end-users to contrib ute their experience within an AI pipeline for bespoke outputs. Transfer learnin g holds promise in contextualising place-based AI to agroecological regions, pro duction systems or enterprise mixes, even with limited data inputs. We find that the rate of AI scientific and software development in recent times has outpaced the evolution of adequate legal and institutional regulations, and often social , moral and ethical license to operate, revealing consumer issues associated wit h data ownership, legitimacy and trust."
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