Rapid development in machine learning and artificial intelligence has allowed previously complex consumer applications and jobs to be automated at an ever increasing pace. But make no mistake, machine learning will also displace academic jobs with only those remaining which wield the right tools combined with the best and brightest ideas.
Voice assistance allow you to order anything online, translation services process natural language ever better, ordered packages are picked by robots, and soon a drone might deliver them. All these advances in automation are due to recent software and hardware developments which result in job loss.
Yet, in academia the rise of automation and ‘robots’ stirs less of a concern. The general feeling remains that core functions of academic jobs are too complex to be sufficiently automated. This core function is the creation of new hypothesis to be tested. However, a large part of academic work, especially in ecology, still relies on encyclopedic knowledge, collection management and often tedious work. Encyclopedic knowledge and many of these mundane academic tasks can be reduced to harder classification or sorting problems (of samples stored in collections and data sets).
As with the soon to be obsolete Uber driver, harder classification problems, such as driving a car, become easier to solve using advanced machine learning techniques. One can argue that automation will free up time to do more hypothesis testing, and increases the pace of science. This arrival of machine learning into mainstream research can be seen in a number of recent high profile publications from classifying leaves in paleobotany to mapping poverty.
I argue that in the near future it will be a requirement to wield the tools to implement (large scale) machine learning approaches to remain competitive in most academic fields. With austerity and budget cuts, a failure to do so might lead to some academics being replaced by evermore sophisticated algorithms and not necessarily lead to a shift in their job content. A urgent shift is needed in the skill sets of many scientists, extending past statistics into the fields of computer science and machine learning.