The field of animal science, and especially animal nutrition, relies heavily on modeling to accomplish its day-to-day objectives. New data streams (‘big data’) and the exponential increase in computing power have allowed the appearance of ‘new’ modelling methodologies, under the umbrella of artificial intelligence (AI). However, many of these modelling methodologies have been around for decades. According to Gardner, technological innovation follows five distinct phases: technology trigger, peak of inflated expectations, trough of disillusionment, slope of enlightenment, and plateau of productivity. The appearance of AI certainly elicited much hype within agriculture leading to over-promised plug-and-play solutions in a field heavily dependent on custom solutions. The threat of failure can become real when advertising a disruptive innovation as sustainable. This does not mean that we need to abandon AI models. What is most necessary is to demystify the field and placing a lesser emphasis on the technology and more on business application. As AI becomes increasingly more powerful and applications start to diverge, new research fields are introduced, and opportunities arise to combine ‘old’ and ‘new’ modelling technologies into hybrids. However, sustainable application is still many years away, and companies and universities alike do well to remain at the forefront. This requires investment in hardware, software, and analytical talent. It also requires a strong connection to the outside world to test, that which does, and does not work in practice and a close view of when the field of agriculture is ready to take its next big steps. Other research fields, such as engineering and automotive, have shown that the application power of AI can be far-reaching but only if a realistic view of models as whole is maintained. In this review, we share our view on the current and future limitations of modelling and potential next steps for modelers in the animal sciences. First, we discuss the inherent dependencies and limitations of modeling as a human process. Then, we highlight how models, fueled by AI, can play an enhanced sustainable role in the animal sciences ecosystem. Lastly, we provide recommendations for future animal scientists on how to support themselves, the farmers, and their field, considering the opportunities and challenges the technological innovation brings.

中文翻译：

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Asas-Nanp 研讨会：动物营养中的数学建模：动物科学建模和建模者的局限性和潜在的后续步骤

动物科学领域，特别是动物营养领域，在很大程度上依赖于建模来实现其日常目标。新的数据流（“大数据”）和计算能力的指数级增长使得在人工智能（AI）的保护下出现“新”建模方法。然而，其中许多建模方法已经存在了几十年。加德纳认为，技术创新遵循五个不同的阶段：技术触发、期望膨胀的顶峰、幻灭的低谷、启蒙的斜坡和生产力的平台期。人工智能的出现无疑在农业领域引起了很大的炒作，导致在一个严重依赖定制解决方案的领域过度承诺即插即用的解决方案。当颠覆性创新被宣传为可持续时，失败的威胁就会变得真实。这并不意味着我们需要放弃人工智能模型。最需要的是揭开这个领域的神秘面纱，少讲技术，多讲商业应用。随着人工智能变得越来越强大，应用程序开始分化，新的研究领域被引入，并且出现了将“旧”和“新”建模技术结合成混合体的机会。然而，可持续应用还需要很多年的时间，公司和大学都很好地保持在前沿。这需要对硬件、软件和分析人才进行投资。它还需要与外界建立强有力的联系，以测试在实践中有效和无效的方法，并密切关注农业领域何时准备好采取下一个重大步骤。工程和汽车等其他研究领域已经表明，人工智能的应用力量可以是深远的，但前提是保持对整个模型的现实看法。在这篇综述中，我们分享了我们对建模当前和未来局限性的看法，以及动物科学建模者下一步可能采取的步骤。首先，我们讨论建模作为人类过程的固有依赖性和局限性。然后，我们重点介绍由人工智能推动的模型如何在动物科学生态系统中发挥增强的可持续作用。最后，考虑到技术创新带来的机遇和挑战，我们为未来的动物科学家提供如何支持自己、农民和他们的领域的建议。