Identifying the underlying time-delayed latent causal processes in sequential data is vital for grasping temporal dynamics and making downstream reasoning. While some recent methods can robustly identify these latent causal variables, they rely on strict assumptions about the invertible generation process from latent variables to observed data. However, these assumptions are often hard to satisfy in real-world applications containing information loss. For instance, the visual perception process translates a 3D space into 2D images, or the phenomenon of persistence of vision incorporates historical data into current perceptions. To address this challenge, we establish an identifiability theory that allows for the recovery of independent latent components even when they come from a nonlinear and non-invertible mix. Using this theory as a foundation, we propose a principled approach, CaRiNG, to learn the CAusal RepresentatIon of Non-invertible Generative temporal data with identifiability guarantees. Specifically, we utilize temporal context to recover lost latent information and apply the conditions in our theory to guide the training process. Through experiments conducted on synthetic datasets, we validate that our CaRiNG method reliably identifies the causal process, even when the generation process is non-invertible. Moreover, we demonstrate that our approach considerably improves temporal understanding and reasoning in practical applications.

Each summer, high school students are seen in labs across the Georgia Tech Research Institute (GTRI) as part of the STEM@GTRI summer internship program. These students have the opportunity to work on a variety of projects, gaining real-world, hands-on experience from experts currently developing the next personalized technology. Senior Research Scientist Jeff Hurley and Principal Research Engineer Reggie Ratcliff, both housed in the Electronic Systems (ELSYS) Laboratory, hosted four high school students for GTRI's summer program.

While some predict mass unemployment or all-out war between humans and artificial intelligence, others foresee a less bleak future. A future looks promising, in which humans and intelligent systems are inseparable, bound together in a continual exchange of information and goals, a "symbiotic autonomy." It will be hard to distinguish human agency from automated assistance -- but neither people nor software will be much use without the other. In the future, I believe that there will be a co-existence between humans and artificial intelligence systems that will be hopefully of service to humanity. These AI systems will involve software systems that handle the digital world, and also systems that move around in physical space, like drones, and robots, and autonomous cars, and also systems that process the physical space, like the Internet of Things.

The International Classification of Functioning, Disability, and Health for Children and Youth (ICF-CY) is a scaffold for designating and systematizing data on functioning and disability. It offers a standard semantic and a theoretical foundation for the demarcation and extent of wellbeing and infirmity. The multidimensional layout of ICF-CY comprehends a plethora of information with about 1400 categories making it difficult to analyze. Our research proposes a predictive model that classify self-care problems on Self-Care Activities Dataset based on the ICF- CY. The data used in this study resides 206 attributes of 70 children with motor and physical disability. Our study implements, compare and analyze Random Forest, Support vector machine, Naive Bayes, Hoeffding tree, and Lazy locally weighted learning using two-tailed T-test at 95% confidence interval. Boruta algorithm involved in the study minimizes the data dimensionality to advocate the minimal-optimal set of predictors. Random forest gave the best classification accuracy of 84.75%; root mean squared error of 0.18 and receiver operating characteristic of 0.99. Predictive analytics can simplify the usage of ICF-CY by automating the classification process of disability, functioning, and health.

As Mick Jagger famously sang, "What a drag it is getting old". Though with a net worth of about $360 million and holding court in the pantheon of rock-n-roll gods, Mick Jagger is probably greying about as well as anyone could at age 74. He certainly will have nothing to worry about when the day comes that he can no longer do the rooster strut; Jagger can afford the best elder care money can buy. Most of us probably won't have the financial means to hire a team of 20-something-year-old nurses in short, black latex skirts to take care of us when we become dirty old men. The best most of us can hope for is not to be locked in a closet and left to stew in our own feces.

In Japan, famous for the longevity of its people, their endearing use of engrish, and their fetish for girls in Catholic school uniforms, the care of the elderly is a particularly acute problem. A third of the Japanese population is reportedly above the age of 60, and the number of people over 90 years of age just topped two million for the first time. Add in a rapidly shrinking population, and you have a country where you have more people eating the early bird special than not. So it's no surprise that Japan is leading the world in robotic elder care, offering a glimpse into our geriatric future.

Improved collaboration techniques for tasks executed collectively by multiple agents can lead to increased amount of information available to the agents, increased efficiency of resource utilization, reduced interference among the agents, and faster task completion. An example of a multiagent task that benefits from collaboration is Collective Transport with Obstacle Avoidance: the task of multiple agents jointly moving an object while navigating around obstacles. We propose a new approach to sharing and aggregation of information among the transporting agents that entails (1) considering all available information instead of only their own most pressing concerns through establishing objectively valued system needs and (2) being persuadable instead of stubborn, through assessing how these needs compare to the needs established by their peers. Our system extends and improves upon the work in (Ferrante et al. 2013), leading to better informed agents making efficient decisions that cause less inter-agent interference and lead to faster and more reliable completion of the collective task.

We present a Companion-based Ambient Robust INtelliGence (CARING) system, for communication with, and support of, clients with Traumatic brain injury (TBI) or Amyotrophic Lateral Sclerosis (ALS). A central component of this system is an artificial companion, combined with a range of elements for ambient intelligence. The companion acts as a personalized intermediary for multi-party communication between the client, the environment (e.g. a Smart Home), caregivers and health professionals. CARING is based on tightly coupled systems drawing from natural language processing, speech recognition and adaptation, deep language understanding and constraint-based knowledge representation and reasoning. A major innovation of the system is its ability to adapt and accommodate different interfaces associated with different client capabilities and needs. The system will use, as a proxy, different interaction requirements of clients (e.g., Brain-Computer Interfaces) at different stages of ALS progression and with different types of TBI impairments. Ultimately, this technology is expected to improve the quality of life for clients through conversation with a computer.