Michael I. Jordan, a renowned machine learning pioneer and professor at the University of California, Berkeley, has emphasized the potential of machine learning to enhance human intelligence while expressing concern over the confusion surrounding the meaning of artificial intelligence (AI). In an article published by IEEE Spectrum, Jordan argued that the focus should shift towards building large-scale machine learning systems that effectively deliver value to humans and avoid exacerbating inequalities.
According to Jordan, the fascination with science-fiction discussions about AI can be diverting from addressing the real challenge at hand. His article, titled “Artificial Intelligence: The Revolution Hasn’t Happened Yet,” stresses the need for caution. Jordan highlights that current AI advancements, particularly in public discourse, are largely encompassed by machine learning, and the perception of computers possessing intelligent thought competing with humans is misleading.
Jordan’s article, originally published in July 2019 and updated earlier this year, delves into the development of societal-scale systems involving machines, humans, and the environment. Drawing an analogy to early civil engineering, he acknowledges that early systems are exposing conceptual flaws akin to buildings or bridges collapsing unexpectedly. Jordan emphasizes the importance of prioritizing human well-being throughout technology development, moving beyond a mere afterthought. He refers to the shift in language from social science to social engineering and asserts that engineering disciplines have played a pivotal role in significantly increasing human happiness, despite the contributions of science.
