"Many scientists, even those who have relevant backgrounds, don't always know where to begin," Yu said. "We have been receiving feedback that the new paper is very timely and helpful."
Recently, the College of Agriculture and Life Sciences at Iowa State asked Yu and Negus to review highlights of their new publication and reflect on the uses and implications of AI tools in their field.
Yu: One thing we do in this paper is to briefly sketch AI's historical context. It has been developing since the 1940s, and what is considered the third AI summer is underway. Deep learning systems have defined the early years of this era.
For crop improvement, AI has largely been deployed to help process and make sense of very large, high-throughput data sets. Large-scale data has become a new challenge in agronomic research and many other areas of science, and AI tools are already providing diverse solutions.
Negus: The field of AI has been rapidly changing in recent years. It can be difficult to know what methods are relevant for specific uses. To streamline this learning process for areas related to crop improvement, we describe more than 15 types and subtypes of AI and give insights on how they are being used in these fields. These methods are not exhaustive, but I think this provides a good introduction to what's out there today and the building blocks of tools we can expect to be developed in the near future.
While the newsworthy AI of today is most often very sophisticated neural networks, other examples of AI range from comparatively simple robotic process automation, which uses an AI "agent" capable of conducting repetitive processes that have enough variability to prevent the use of standard process automation, to relatively complex expert and fuzzy systems that attempt to replicate the problem-solving capabilities of human experts, to other types of highly advanced machine learning.
Machine learning (ML) is a type of AI that uses large data sets to improve through experience, or learn, and then uses the outcomes to solve problems or make predictions. ML is being put into practice widely in the crop improvement field. ML methods using genomic, enviromic, phenomic and other multi-omic approaches are helping researchers capture environmental and genetic variations to better understand their influences on crop breeding and management.
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