Automated knowledge-acquisition systems have focused on embedding a cognitive model of a key knowledge worker in their software that allows the system to acquire a knowledge base by interviewing domain experts just as the knowledge worker would. Two sets of research questions arise: (1) What theories, strategies, and approaches will let the modeling process be facilitated; accelerated; and, possibly, automated? If automated knowledge-acquisition systems reduce the bottleneck associated with acquiring knowledge bases, how can the bottleneck of building the automated knowledge-acquisition system itself be broken? That is, humans are known to be subject to errors and cognitive biases in their judgment processes. How can an automated system critique and influence such biases in a positive fashion, what common patterns exist across applications, and can models of influencing behavior be described and standardized?

For humans and automation to effectively collaborate and perform tasks, all participants need access to a common representation of potentially relevant situational information, or context. This article describes a general framework for building context-aware interactive intelligent systems that comprises three major functions: (1) capture human-system interactions and infer implicit context; (2) analyze and predict user intent and goals; and (3) provide effective augmentation or mitigation strategies to improve performance, such as delivering timely, personalized information and recommendations, adjusting levels of automation, or adapting visualizations. Our goal is to develop an approach that enables humans to interact with automation more intuitively and naturally that is reusable across domains by modeling context and algorithms at a higher-level of abstraction. We first provide an operational definition of context and discuss challenges and opportunities for exploiting context. We then describe our current work towards a general platform that supports developing context-aware applications in a variety of domains.

As humans, we're forced to make lots of decisions on a daily basis. And while we may think we make every single decision based on facts, logic, and reasoning, there's a lot more to it than that. As it turns out, we kind of suck at the whole decision-making process due to our own cognitive biases. Discovered by two dudes named Daniel Kahneman and Amos Tversky in the 1970s, cognitive biases are basically like mental shortcuts or rules that simplify the decision-making process. Sure, we try to rationalize them after the fact through logic and reasoning, but the choice itself is pre-determined by the unconscious part of the mind.

Artificial intelligence technology can tell doctors when a scan reveals a tumor, can help the military distinguish between a truck and a school bus as a target, and can answer a high volume of college students' questions. Sectors of our economy such as the military, health care, and higher education are much further along than the K-12 system in incorporating artificial intelligence systems and machine learning into their operations. And many of those uses--even when they are not specifically for education--can spark ideas for applications in K-12 that may be more pertinent than ever imagined. With the coronavirus upending traditional ways of delivering education, AI technologies--which are designed to model human intelligence and solve complex problems--may be able to help with logistical challenges such as busing and classroom social distancing, provide support to overwhelmed teachers, and glean new information about remote learning. AI techniques and systems are "like the internal combustion engine--you can use them to power a lot of different things," said David Danks, a professor of philosophy and psychology at Carnegie Mellon University in Pittsburgh, who studies cognitive science, machine learning, and how AI affects people.

Recently, I was reading Rolf Dobell''s The Art of Thinking Clearly, which made me think about cognitive biases in a way I never had before. I realized how deeply seated some cognitive biases are. In fact, we often don't even consciously realize when our thinking is being affected by one. For data scientists, these biases can really change the way we work with data and make our day-to-day decisions, and generally not for the better. Data science is, despite the seeming objectivity of all the facts we work with, surprisingly subjective in its processes.

Recently, I was reading Rolf Dobell''s The Art of Thinking Clearly, which made me think about cognitive biases in a way I never had before. I realized how deeply seated some cognitive biases are. In fact, we often don't even consciously realize when our thinking is being affected by one. For data scientists, these biases can really change the way we work with data and make our day-to-day decisions, and generally not for the better. Data science is, despite the seeming objectivity of all the facts we work with, surprisingly subjective in its processes.

Developing cognitive agents with human-level natural language understanding (NLU) capabilities requires modeling human cognition because natural, unedited utterances regularly contain ambiguities, ellipses, production errors, implicatures, and many other types of complexities. Moreover, cognitive agents must be nimble in the face of incomplete interpretations since even people do not perfectly understand every aspect of every utterance they hear. So, once an agent has reached the best interpretation it can, it must determine how to proceed – be that acting upon the new information directly, remembering an incomplete interpretation and waiting to see what happens next, seeking out information to fill in the blanks, or asking its interlocutor for clarification. The reasoning needed to support NLU extends far beyond language itself, including, non-exhaustively, the agent's understanding of its own plans and goals; its dynamic modeling of its interlocutor's knowledge, plans, and goals, all guided by a theory of mind; its recognition of diverse aspects human behavior, such as affect, cooperative behavior, and the effects of cognitive biases; and its integration of linguistic interpretations with its interpretations of other perceptive inputs, such as simulated vision and non-linguistic audition. Considering all of these needs, it seems hardly possible that fundamental NLU will ever be achieved through the kinds of knowledge-lean text-string manipulation being pursued by the mainstream natural language processing (NLP) community.

People around the world enjoy "virtual human" characters, whether in Hollywood films, Japanese anime, or video games. In recent years, AI-powered virtual humans have increasingly insinuated themselves into our daily lives. The virtual pop icon Teresa Teng has performed songs with Taiwanese singer Jay Chou, achieving huge success. The popular Chinese debate show "I CAN I BB" hosted a spirited episode on whether "Falling in love with an AI human can be considered true love or not," where many people argued it is possible for a human to fall in love with an AI. Are there limits to such human-machine relationships?

Autonomous agents (AA) will increasingly be interacting with us in our daily lives. While we want the benefits attached to AAs, it is essential that their behavior is aligned with our values and norms. Hence, an AA will need to estimate the values and norms of the humans it interacts with, which is not a straightforward task when solely observing an agent's behavior. This paper analyses to what extent an AA is able to estimate the values and norms of a simulated human agent (SHA) based on its actions in the ultimatum game. We present two methods to reduce ambiguity in profiling the SHAs: one based on search space exploration and another based on counterfactual analysis. We found that both methods are able to increase the confidence in estimating human values and norms, but differ in their applicability, the latter being more efficient when the number of interactions with the agent is to be minimized. These insights are useful to improve the alignment of AAs with human values and norms.

Our central goal is to quantify the long-term progression of pediatric neurological diseases, such as a typical 10-15 years progression of child dystonia. To this purpose, quantitative models are convincing only if they can provide multi-scale details ranging from neuron spikes to limb biomechanics. The models also need to be evaluated in hyper-time, i.e. significantly faster than real-time, for producing useful predictions. We designed a platform with digital VLSI hardware for multi-scale hyper-time emulations of human motor nervous systems. The platform is constructed on a scalable, distributed array of Field Programmable Gate Array (FPGA) devices.