What Machine Learning Can and Cannot Do
As its domain of applications continues to expand, machine learning (ML) is raising serious concerns on its impact on automation and the future of work. In What can machine learning do? Workforce implications, an article recently published in Science, MIT professor Erik Brynjolfsson and CMU professor Tom Mitchell explore this question by analyzing which tasks are particularly suitable for ML, as well as its expected impacts on the workforce and the economy. [Read the full Research Paper here, and watch Brynjolfsson explain the concept in this video].
Here is a recap of the Science article:
Which tasks are most suitable for machine learning?
Machine learning systems are not equally suitable for all tasks. It’s been most successful when applied with supervised learning and deep learning algorithms, which require very large amounts of carefully labelled data to be used for training,— e.g. cat, not-cat. While very effective in such domains, the authors remind us that ML systems are significantly narrower and more specialized than humans. There are many tasks for which they’re completely ineffective given the current state-of-the-art.
Brynjolfsson and Mitchell identify eight key criteria that help distinguish tasks that are suitable for ML, from those where ML is less likely to be successful.
Continue reading the full article on Medium, here.