- Human imitation: AI should behave in a way that is indistinguishable from that of a human being.
- Intelligence Augmentation (IA): AI should augment human capacity to think, communicate, and create.
- Intelligence Infrastructure (II): AI should manage a network of computing and communicating agents, markets, and repositories of information in a way that is efficient, supportive of human and societal needs, as well as economically and legally viable.
Human-imitative AI is often singled out by technologists, academicians, journalists and venture capitalists as the real aspiration and end goal of AI research. However, most progress in “AI” to date has not concerned high-level abstract thinking machines, but rather low-level engineering solutions with roots in operations research, statistics, pattern recognition, information theory and control theory.
In fact, as argued by Jordan, the single-minded focus on human-imitative AI has become a distraction from the more useful endeavor of addressing IA and II. To this end, the post calls for the founding of a new engineering discipline building on
“ideas that the preceding century gave substance to — ideas such as “information,” “algorithm,” “data,” “uncertainty,” “computing,” “inference,” and “optimization.” Moreover, since much of the focus of the new discipline will be on data from and about humans, its development will require perspectives from the social sciences and humanities.”
The development of such a discipline would call not only for large-scale targeted research efforts, but also for new higher education programs. As proposed in “Robot-proof“, the new programs should impart data literacy, technological literacy, and human literacy:
“Students will need data literacy to manage the flow of big data, and technological literacy to know how their machines work, but human literacy–the humanities, communication, and design–to function as a human being. Life-long learning opportunities will support their ability to adapt to change.“