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 theory of mind is an important human capability that allows us to understand and predict the goals, intents, and beliefs of other individuals. We present an approach to designing intelligent communicative agents based on modeling theories of mind. This can be tricky because other agents may also have their own theories of mind of the first agent, meaning that these mental models are naturally nested in layers. So, to look for intuitive communicative acts, we recursively apply a planning algorithm in each of these nested layers, looking for possible plans of action as well as their hypothetical consequences, which include the reactions of other agents; we propose that truly intelligent communicative acts are the ones which produce a state of maximum decision theoretic utility according to the entire theory of mind. We implement these ideas using Java and OpenCyc in an attempt to create an assistive AI we call MARTHA. We demonstrate MARTHA's capabilities with two motivating examples: helping the user buy a sandwich and helping the user search for an activity. We see that, in addition to being a personal assistant, MARTHA can be extended to other assistive fields, such as finance, research, and government.

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.

This abstract is proposing a challenging problem: to infer a human's mental state — intent and belief — from an observed RGBD video for human-robot interaction. The task is to integrate symbolic reasoning, a field well-studied within A.I. domains, with the uncertainty native to computer vision strategies. Traditional A.I. strategies for plan inference typically rely on first-order logic and closed world assumptions which struggle to take into account the inherent uncertainty of noisy observations within a scene. Computer vision relies on pattern-recognition strategies that have difficulty accounting for higher-level reasoning and abstract representation of world knowledge. By combining these two approaches in a principled way under a probabilistic programming framework, we define new computer vision tasks such as actor intent prediction and belief inference from an observed video sequence. Through inferring a human's theory of mind, a robotic agent can automatically determine a human's goals to collaborate with them.

Natural language dialogue provides the opportunity for truly natural human-robot interaction. A robot participating in natural language dialogue must identify or create new representations for referenced entities if it is to discuss, reason about, or perform actions involving that entity, a capability known as reference resolution. In previous work we presented algorithms for resolving references occurring in definite noun phrases. In this paper we propose an algorithm for resolving references in a wider array of linguistic forms, using the Givenness Hierarchy.

Natural Language Understanding (NLU) studies machine language comprehension and action without human intervention. We describe an implemented system that supports deep semantic NLU for controlling systems with multiple simulated robot agents. The system supports bidirectional communication for both human-agent and agent-agent inter-action. This interaction is achieved with the use of N-tuples, a novel form of Agent Communication Language using shared protocols with content expressing actions or intentions. The system’s portability and flexibility is facilitated by its division into unchanging “core” and “application-specific” components.

Assessing the quality of robot social navigation is a challenging problem fraught with human obstacles. From preconceived notions to perspective or point of view, evaluations can differ from person to person. Most work in the field of robot navigation is focused on creating algorithms that produce efficient robot trajectories. We posit that the evaluation of trajectories in a social context is essential and distinct to trajectory generation. In this work we recorded a manually driven powered wheelchair through different scenarios and asked expert evaluators to assess the quality of the powered wheelchair's movement. These evaluations were then compared to post-experiment assessments from trajectory generation algorithms and social navigation concepts. Our results show that it is possible to build a simple model to predict expert evaluators' responses. Unfortunately, there is no clear consensus amongst these experts on what quality behaviour is. This suggests that while current navigation algorithms offer strong heuristics for the generation of smooth trajectories in well-defined environments, their efficacy in evaluating social navigation is less obvious. We believe that more emphasis must be put on dynamic and reactive navigation algorithms as any heuristic approach will be limited due to variance in people's behaviours and expectations.