A new research study from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) challenges the prevailing narrative about the potential job-displacing effects of AI. Rather than reinforcing fears of widespread automation, the study delves into three key questions: Will AI automate human jobs? If so, which jobs are at risk? And when might this occur?
In contrast to predictions from various sources, such as Goldman Sachs and McKinsey, the MIT researchers argue that the majority of jobs deemed susceptible to AI displacement may not be economically viable to automate at present. Neil Thompson, a research scientist at MIT CSAIL and a co-author of the study, suggests that the anticipated AI disruption might unfold more slowly and less dramatically than commonly believed.
The study focused specifically on jobs involving visual analysis, leaving the impact of text- and image-generating models for follow-up studies. By surveying workers and modeling the cost of building AI systems capable of replacing their tasks, the researchers found that, for many jobs, humans remain the more economical choice.
Using the example of a baker, the study illustrates that only 23% of wages paid for vision tasks would be economically attractive to automate with AI. The researchers also considered self-hosted, self-service AI systems but found that even with a system costing as little as $1,000, many low-wage and multitasking-dependent jobs wouldn’t make economic sense to automate.
However, the study acknowledges several limitations, including its focus on vision tasks and the exclusion of cases where AI augments rather than replaces human labor. The researchers also address potential bias concerns, asserting that the study’s conclusions were not influenced by the MIT-IBM Watson AI Lab, which backed the research.
While cautioning policymakers to prepare for AI job automation, the study emphasizes that the process will take years, or even decades, to unfold. It suggests that there is ample time for policy initiatives to be implemented and calls for efforts to decrease the costs of AI deployments and broaden their scope for economic attractiveness in automation.”
Read More On: Thestartupscoup.Com
