This wonderful summary of class discussions in my TO433 AI for Business course is from my brilliant student Greg Cervenak . Greg was bored in quarantine and decided to make this course reflection look and feel like a Morning Brew article. Introducing, TO 433 Brew, a newsletter that boils down the 4 coolest components of the course interwoven with some current events.
AI4Business BREW: The Highlights
#1: Benefitting from COVID-19 unemployment??
If you are a machine — yes! These past few weeks have been rough, with recent weekly jobless claims exceeding 22 million individuals in the United States due to COVID-19 financial burdens. For companies that are managing to maintain employment in these times, they will be looking to automation in the near future to decrease payroll expenses even slightly. For those who had to let a massive number of employees go, they are likely already resorting to AI and machines to inexpensively replace the jobs that they had to eliminate. Further, as the job market recovers at the end of the current recession, will companies be as willing to immediately return to the job market, or will they attempt to fill “open” roles with computers for a fraction of the long-term cost to hedge their payroll ahead of the next economic downturn. Chances are, even if not widespread, some companies will have this mentality, making it more challenging for low-skilled labor to return to the workforce. It is a great time to be a machine “looking for work” — or a machine looking to be built, for that point.
Earlier in the semester, I strongly advocated for the point that the rise of artificial intelligence would cause upskilling of the workforce rather than replacement. In fact, to quote my first discussion argument:
it’s a virtuous cycle in my opinion: increased prevalence of AI —> more education ability and fewer low skilled jobs needed —> low-skilled workers learn faster and transition from jobs that can be automated to jobs that help drive the future of artificial intelligence —> more AI —> repeat.
In these unprecedented times, I am less convinced, given that companies simply do not have the cash to upskill their workforce, and it makes more sense to decrease payroll by simply replacing workers rather than training them. This boils down to one point that we have explored a lot throughout this course: nobody knows for sure what will happen, and events can trigger major changes in the trajectory for the industry. In just 3.5 short months, my entire outlook on the future of machine learning’s impact on employment drastically changed, and I am confident as more events or technologies develop, that will continue to change.