Augmenting Human Capabilities

Beyond the ability of AI to automate processes, augmenting human capabilities would open the door for AI to impact human wellbeing in a broad spectrum of new areas.

However, augmentation demands new skillsets for AI: AI systems today have a limited ability to perform complex cognitive tasks like designing, planning, and adaptation to unknown events. AI also lacks the agility, dexterity, and regenerative capacity intrinsic to the human body, and rarely adapts rapidly to subtle social and environmental cues.

Achieving these key characteristics for AI-driven human augmentation can improve the quality of human life at all stages and levels (individual, family, and societal). Example applications range from real-time decisions on the infusion rate of oxygen for a newborn in the ICU and/or augmented breathing, optimizing the workout regime and strike speed for an elite athlete, and designing a smart-health house whose floors notify family members of the wellbeing of an elderly relative.

We hypothesize that integration of diverse data-streams from passive and active sensors, real-time social and biomedical feedback, and biologically-inspired mergers of AI with living systems will position AI systems to augment human capabilities. In this research thrust, we will develop

• experimental frameworks/testbeds to deploy models that integrate multiple, heterogeneous data-streams from biological sensors to optimize decision-making;
• reduce human cognitive load by automatically accessing and organizing data and providing scenarios with probabilities of outcomes;
• bio-inspired, biocomputing, and bio-augmented computational designs that are flexible, agile and/or self-repairing; and
• a comprehensive approach to integrating diverse sources of health data including electronic health records, social data, and data from biological sensors, in order to improve health and healthcare.

In pursuing this research, we envision the integration of multiple disciplines and tools, including sensor technology, telemedicine, machine learning, bio-inspired computing, bioengineering, soft robotics, neuroscience, health information technology, ethics and precision health. How can AI technologies lead to innovations and efficiencies in domains where intelligent systems or robots collaborate in future workplaces?