We present an emerging research project in our laboratory to extend ambient intelligence (AmI) by what we refer to as “extreme personalization” meaning that an instance of ambient intelligence is focused on one or at most a few individuals over a very long period of time. Over a lifetime of co-activity, it senses and adapts to a person’s preferences and experiences, and crucially, his or her (changing) special needs; needs that differ significantly from the normal baseline. We refer to our agent-based cyber-physical system as Ambient Personal Environment eXperiment (APEX). It aims to serve as a Companion , a Coach , and a Caregiver : crucial support for individuals with mental, physical, and age-related disabilities and those other people who help them. We propose that an instance of APEX, interacting socially with each of these people, is both a social actor as well as a cyber-human prosthetic device . APEX is an ambitious integration of multiple technologies from Artificial Intelligence (AI) and other disciplines. Its successful development can be viewed as a grand challenge for AI. We discuss in this paper three research thrusts that lead toward our vision:  robust intelligent agents, semantically rich human-machine interaction, and reasoning from comprehensive multi-modal behavior data.

The central thesis of this paper is that the technology of intelligent, autonomous machines gives rise to novel fault modes that are not seen in other types of automation. As a consequence, autonomous systems provide new vectors for cyber-attack with the potential consequence of subversion, degraded behavior or outright failure of the autonomous system. While we can only pursue the analogy so far, maladaptive behavior and the other symptoms of these fault modes in some cases may resemble those found in humans. The term “psychopathology” is applied to fault modes of the human mind, but as yet we have no equivalent area of study for intelligent, autonomous machines. This area requires further study in order to document and explain the symptoms of unique faults in intelligent systems, whether they occur in nominal conditions or as a result of an outside, purposeful attack. By analyzing algorithms, architectures and what can go wrong with autonomous machines, we may a) gain insight into mechanisms of intelligence; b) learn how to design out, work around or otherwise mitigate these new failure modes; c) identify potential new cyber-security risks; d) increase the trustworthiness of machine intelligence. Vigilance and attention management mechanisms are identified as specific areas of risk.

This Blue Sky presentation focuses on a major shift toward a notion of “ambient intelligence” that transcends general applications targeted at the general population.  The focus is on highly personalized agents that accommodate individual differences and changes over time.  This notion of Extended Ambient Intelligence (EAI) concerns adaptation to a person’s preferences and experiences, as well as changing capabilities, most notably in an environment where conversational engagement is central.  An important step in moving this research forward is the accommodation of different degrees of cognitive capability (including speech processing) that may vary over time for a given user—whether through improvement or through deterioration. We suggest that the application of divergence detection to speech patterns may enable adaptation to a speaker’s increasing or decreasing level of speech impairment over time. Taking an adaptive approach toward technology development in this arena may be a first step toward empowering those with special needs so that they may live with a high quality of life.  It also represents an important step toward a notion of ambient intelligence that is personalized beyond what can be achieved by mass-produced, one-size-fits-all software currently in use on mobile devices.

Our speech research focuses on the detection of dialectal Most physiological assessments commonly used to determine variations by identifying speech language divergences the functional status of patients with Amyotrophic along a range of different dimensions. We borrow the notion lateral sclerosis (ALS) require trained clinical personnel to of divergence from the study of cross-linguistic variations administer and interpret the results. Speech impairments (Dorr, 1993) and apply it towards developing an assessment eventually affect 80-95% of patients with ALS (Beukelman, of bulbar function in patients with ALS, to improve 2011). Initial impairments include reduced speaking upon existing assessments (Green et al., 2013).

Teamwork requires mutual trust among team members. Establishing and maintaining trust depends upon alignment of mental models, an aspect of shared awareness. We present a theory of how maintenance of model alignment is integral to fluid changes in relative control authority (i.e., adaptive autonomy) in human-robot teamwork.

Painters write about their what artists say about their own work, using motivations; photographers, about details of the concept mapping as a conceptual capture tool (Eskridge et.

We describe CARDIAC, a prototype for an intelligent conversational assistant that provides health monitoring for chronic heart failure patients. CARDIAC supports user initiative through its ability to understand natural language and connect it to intention recognition. The natural language interface allows patients to interact with CARDIAC without special training. The system is designed to understand information that arises spontaneously in the course of the interview. If the patient gives more detail than necessary for answering a question, the system updates the user model accordingly. CARDIAC is a first step towards developing cost-effective, customizable, automated in-home conversational assistants that help patients manage their care and monitor their health using natural language.