Data sharing challenges and privacy concerns are only starting to fully emerge.
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It mandates a sustained productive interaction of experts in a multitude of disciplines (cognitive, social, and health sciences, human-centered design, computing, and engineering more broadly) that as yet do not know how to converse and collaborate effectively. Working with and helping people beyond physical interactions requires a much more profound understanding of people (a long-standing research pursuit) and of human-machine interaction (a recent one). Human-machine collaboration presents promising avenues for leveraging human knowledge, skills, and qualities in ways that complement those of machines, thereby conserving human purpose. Working well with people is a complex challenge, one that humanity continues to study, struggle with, and argue about. Collaboration, on the other hand, requires adjusting to the given context and environment, and in particular to the collaborators. Removing humans from a context allows the automation process to redefine and adapt a given problem to solve it differently, in the interest of efficiency and reduced cost. SAR systems aid in challenging contexts including rehabilitation, behavior therapy, and skill training involving interactions with already large and rapidly-expanding populations spanning the age and ability spectrum, including autism, stroke, aging, and dementia, among many others.ĭisembodied technologies (e.g., screens) are also applicable in these domains, but a growing body of evidence shows that interactions with physically embodied co-present social partners result in stronger and more sustained effects on behavior change, learning, and enjoyment.Īnother challenging area of human augmentation is human-machine collaboration, which requires richer interactions and adaptation than automation alone. One of the newest and fastest-growing areas of robotics, SAR focuses on developing intelligent socially interactive machines that assist people through motivation and companionship rather than through physical means. The field of socially assistive robotics (SAR) provides an existence proof. This broad concept encompasses cognitive, physical, emotional, and social augmentation machines that were originally invented to do work may prove helpful to people by not doing work at all, instead motivating people to do the work themselves.
Instead of doing work for people, intelligent machines that augment human abilities aim to empower and enhance people to enable them to do meaningful work themselves.
Human augmentation is a complementary but fundamentally different use of AI, ML, and robotics than automation. In a future in which AI does our thinking work, and robots do our physical work, we may stop having reasons to get out of bed in the morning. The overarching vision is that of replacing human work with machines in every possible context, but if in the future machines are doing all the work, what are people doing? Debates about the future of work currently focus on labor economic implications, but health and wellness implications are equally concerning. The current focus in research, development, and deployment of AI, machine learning (ML), and robotics is on automation. Therefore, we may need very different types of AI from those currently in development to truly improve human quality of life at the individual and societal levels. Evidence from neuroscience, cognitive science, health sciences, and gerontology shows that human wellbeing and longevity, our health and wellness, fundamentally hinge on physical activity, social connectedness, and a sense of purpose. The very premise that intelligent machines doing our work will make our lives better may be flawed. Michael Jordan’s article on artificial intelligence (AI) eloquently articulates how far we are from understanding human-level intelligence, much less recreating it through AI, machine learning, and robotics.
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