Title: Can recent advances in machine learning help feed the world? Speaker: Professor Ben Hayes (University of Queensland) Abstract: Machine learning has enabled step changes in progress in some fields recently, most notably in predicting 3D structure of proteins and generative text on very diverse topics. These applications use deep learning models trained on colossal data sets to make their predictions. Crop and livestock breeders are now using phenotypic, genomic and omic data sets with billions of data points to breed higher yielding, more adapted varieties and animals. Given this explosion of data, and the ability of machine learning to analyse large data sets, it would seem useful to explore how machine learning can assist crop and livestock breeders. in this presentation, the potential application of machine learning to contribute to crop and livestock tasks is assessed and compared to existing methods. The conclusion is that for some tasks, machine learning could make a major contribution, for other tasks, existing methods outcompete machine learning methods. About the speaker: Professor Hayes has extensive research experience in genetic improvement of livestock, crop, pasture and aquaculture species, with a focus on integration of genomic information into breeding programs, including leading many large scale projects which have successfully implemented genomic technologies in livestock and cropping industries. Author of more than 300 journal papers, including in Nature Genetics, Nature Reviews Genetics, and Science, contributing to statistical methodology for genomic, microbiome and metagenomic profile predictions, quantitative genetics including knowledge of genetic mechanisms underlying complex traits, and development of bioinformatics pipelines for sequence analysis. Highly cited researcher 2015 - 2022.