An accurate problem list plays the key role of a problem-oriented medical record, which plays a significant role in improving patient care. However, the multi-author, multi-purpose nature of problem list makes it a challenge to maintain, and a single list is difficult, if not impossible, to satisfy all the needs of different practitioners. In this paper, we propose using machine generated problem list to assist a medical practitioner to review a patient’s chart. The proposed system scans both structured and unstructured data in a patient’s electronic medical record (EMR) and generates a ranked, recall-oriented problem list grouped by body systems. Details of each problem are readily available for the user to assess the correctness and relevance of the problem. The user can then provide feedback to the system on the trustworthiness of each evidence passage retrieved, as well as the validity of the problem as a whole. The user-specific feedback provides new information the system needs to perform active learning to learn the user’s preference and produce personalized, and/or domain-specific problem lists.

Timely, relevant, and accurate intelligence analysis is critical to national security, but it is astonishingly complex. This paper provides an intuitive overview of Cogent, a cognitive assistant that facilitates a synergistic integration of analyst's imaginative reasoning with agent's critical reasoning to draw defensible and persuasive conclusions from masses of evidence, in a world that is changing all the time. It presents Cogent's design goals characterizing a new generation of structured analytical tools, introduces the evidence-based analysis concepts on which it is grounded, illustrates a sample session with its current version, and summarizes the cognitive assistance provided to its user.

We describe a cognitive assistant in early-stage development for the United States Air Force as an aid to contracting officers and potential commercial offerors for navigating the government-contracting process. The goal is easing compliance and affording flexibility and transparency so as to support an innovative and rapid acquisition process. The motivation, use cases, and technical approach for MICA, a Machine Interface for Contracting Assistance, are discussed here along with the technical challenges posed.

With advancements in technology, smartphones can already serve as memory aids. Electronic calendars are of great use in time-based memory tasks. In this project, we enter the mixed reality realm for helping dementia patients. Dementia is a general term for a decline in mental ability severe enough to interfere with daily life. Memory loss is an example. Here, mixed reality refers to the merging of real and virtual worlds to produce new episodic memory visualisations where physical and digital objects co-exist and interact in real-time. Cognitive models are approximations of a patient's mental abilities and limitations involving conscious mental activities (such as thinking, understanding, learning, and remembering). External representations of episodic memory help patients and caregivers coordinate their actions with one another. We advocate distributed cognition, which involves the coordination between individuals, artefacts and the environment, in four main implementations of artificial intelligence technology in the Kognit storyboard: (1) speech dialogue and episodic memory retrieval; (2) monitoring medication management and tracking an elder's behaviour (e.g., drinking water); (3) eye tracking and modelling cognitive abilities; and (4) serious game development towards active memory training. We discuss the storyboard, use cases and usage scenarios, and some implementation details of cognitive models and mixed reality hardware for the patient. The purpose of future studies is to determine the extent to which cognitive enhancement technology can be used to decrease caregiver burden.

Today’s businesses, government and society work and services are centered around interactions, collaborations and knowledge work. The pace, amount and veracity of data generated and processed by a worker has accelerated significantly to the level that challenged human cognitive load and productivity. On the other hand, big data has provided an unprecedented opportunity for AI to tackle one of the main challenges hindering the AI progress: building models of world in a scalable, adaptive and dynamic manner. In this paper, we describe the technology requirements of building cognitive assistance technologies that assists human workers, and present a cognitive work assistant framework that aims at offering intelligence assistance to workers to improve their productivity and agility. We then describe the design and development of a set of cognitive services offered by the framework, based on advanced NLP and machine learning methods. The cognitive services help workers in processing and linking information and identifying and tracking work items over interactions in communication channels such as email, social conversations and media, chats and messaging and calendar applications. These cognitive services are designed to be adaptive, online and personalized so that over time adapt to changing environment and knowledge, and the models become personalized through learning preferences and working language and style of the subject worker.

Exploring web and in particular social media data is an essential task to many of the subject matter experts in order to discover content around their subject of interest. It is important to provide them with a tool to define their scope of vocabulary, i.e what to search for, and suggest them commonly used terms besides the serendipitous terms allowing them to define their scope of explorations. This paper presents methods on constructing ``domain models" which are families of keywords and extractors to enable focus on social media documents relevant to a project using multiple channels of information extraction.

We describe an experiment in using IBM’s Watson cognitive system to teach about human-computer co-creativity in a Georgia Tech Spring 2015 class on computational creativity. The project-based class used Watson to support biologically inspired design, a design paradigm that uses biological systems as analogues for inventing technological systems. The twenty-four students in the class self-organized into six teams of four students each, and developed semester-long projects that built on Watson to support biologically inspired design. In this paper, we describe this experiment in using Watson to teach about human-computer co-creativity, present one project in detail, and summarize the remaining five projects. We also draw lessons on building on Watson for (i) supporting biologically inspired design, and (ii) enhancing human-computer co-creativity.

The updating is accomplished using Bayes'

The legal relationship between government and citizens is mediated by documents. This paper identifies four classes of cognitive assistants that could improve the experience of citizens and government officials in using and understanding government documents: self-filling forms; error-detecting forms; proactive information search; and deductive document synthesis. Each of these classes of cognitive assistants has the potential to significantly improve access to justice and delivery of information, services, and other benefits to citizens by improving the ability of citizens to understand and correctly fill out forms and to comprehend informational documents.

We are interested in the development of an autonomous social robot whose goal is to promote collaborative behaviors in children while playing an interactive game.