Connecting the dots between discoveries in neuroscience(neuroplasticity), psychoneuroimmunology(the brain-immune loop) and user experience (gadget rub-off) indicate the nature of our time spent with gadgets is a vector in human health - mentally, socially and physically. The positive design of our interactions with devices therefore can have a positive impact on economy, civilization and society. Likewise, the absence of design that encourages positive interaction may encourage undesirable behaviors. Much like the architecture of physical spaces and buildings, the consequences of the architecture of the 21stcentury conversation between man and machine may last generations. AI and the Internet of Things are primary vectors for positive and negative impacts of technology.  We describe a growing body of co-discoveries occurring across a variety of disciplines that support the argument for human sciences in technology design.

For people constrained to picture based communication, the expression of interest in a question answering (QA) or information retrieval (IR)scenario is highly limited. Traditionally, alternative and augmentative communication (AAC) methods (such as gestures and communication boards) are utilised. But only few systems allow users to produce whole utterances or sentences that consist of multiple words; work to generate them automatically is a promising direction in the big data context.In this paper, we provide a dedicated access method for the open-domain QA and IR context. We propose a method for the user to search for additional symbols to be added to the communication board in real-time while using access to big data sources and context based filtering when the desired symbol is missing. The user can select a symbol that is associated with the desired concept and the system searches for images on the Internet - here, in Wikipedia - with the purpose of retrieving an appropriate symbol or picture. Querying for candidates is performed by estimating semantic relatedness between text fragments using explicit semantic analysis (ESA).

Wild Big Data (WBD) is data that is hard to extract, understand, and use due to its heterogeneous nature and volume. It typically comes without a schema, is obtained from multiple sources and provides a challenge for information extraction and integration. We describe a way to subduing WBD that uses techniques and resources that are popular for processing natural language text. The approach is applicable to data that is presented as a graph of objects and relations between them and to tabular data that can be transformed into such a graph. We start by applying topic models to contextualize the data and then use the results to identify the potential types of the graph's nodes by mapping them to known types found in large open ontologies such as Freebase, and DBpedia. The results allow us to assemble coarse clusters of objects that can then be used to interpret the link and perform entity disambiguation and record linking.

We describe the KELVIN system for extracting entities and relations from large text collections and its use in the TAC Knowledge Base Population Cold Start task run by the U.S. National Institute of Standards and Technology. The Cold Start task starts with an empty knowledge base defined by an ontology or entity types, properties and relations. Evaluations in 2012 and 2013 were done using a collection of text from local Web and news to de-emphasize the linking entities to a background knowledge bases such as Wikipedia. Interesting features of KELVIN include a cross-document entity coreference module based on entity mentions, removal of suspect intra-document conference chains, a slot value consolidator for entities, the application of inference rules to expand the number of asserted facts and a set of analysis and browsing tools supporting development.

We describe a novel system for discovering and characterizing emerging events. We define event emergence to be a developing situation comprised of a series of sub-events. To detect sub-events from a very large, continuous textual input stream, we use two techniques: (1) frequency-based detection of sub-events that are potentially entailed by an emerging event; and (2) anomaly-based detection of other sub-events that are potentially indicative of an emerging event. Identifying emerging events from detected sub-events involves connecting sub-events to each other and to the relevant emerging events within the event models and estimating the likelihood of possible emerging events. Each sub-event can be part of a number of emerging events and supports various event models to varying degrees. We adopt a coherent and compact model that probabilistically identifies emerging events. The innovative aspect of our work is a well-defined framework where statistical Big Data techniques are informed by event semantics and inference techniques (and vice versa). Our work is strongly grounded in semantics and knowledge representation, which enables us to produce more reliable results than would otherwise be possible with a purely statistical approach.

We all share the same world, but are free to formulate and argue for our own interpretations of this shared reality. For different agents will grant differing degrees of importance to the same facts and norms. We cannot experiment on human cultures the way scientists experiment on cell cultures, but we can construct thought experiments that imagine the consequences of otherwise impossible changes. Successful thought experiments do not change the world, but change the way we see the world. This paper describes Gedanken-style reasoning in an AI system that allows a computer to understand, or at least speculate on, the surprising causal interactions between apparently unrelated concepts. This system ponders alternate worlds in which the amount of a conceptual ingredient [X] is increased or decreased, to see what unexpected and apparently incongruous effects might arise from this change. Our goal is to construct a creative generator of novel what-if scenarios that can be used in the generation of perspective-shaping stories, poems and jokes.

In this paper we explain how IETAL agents learn their environment, and how they build their intrinsic, internal representation of it, which they then use to build their expectations when on quest to satisfy its active drives. As environments change (with or without other agents present in them), the agents learn to new and “forget” irrelevant, “old” associations made. We discuss the concept of emotional context of associations, and show a gallery of simulations of behaviors in small multiagent societies.

In this paper a method to integrate inference and machine learning is proposed. Execution of learning algorithm is defined as a complex inference rule, which generates intrinsically new knowledge. Such a solution makes the reasoning process more creative and allows to re-conceptualize agent's experiences depending on the context. Knowledge representation used in the model is based on the Logic of Plausible Reasoning (LPR). Three groups of knowledge transmutations are defined: search transmutations that are looking for the information in data, inference transmutations that are formalized as LPR proof rules, and complex ones that can use machine learning algorithms or knowledge representation change operators. All groups can be used by inference engine in a similar manner. In the paper appropriate system model and inference algorithm are proposed. Additionally, preliminary experimental results are presented.

We propose a learning agent that first learns concepts in an integrated, cross-modal manner, and then uses these as the semantics model to map language. We consider an abstract model for the action of throwing, modeling the entire trajectory. From a large set of throws, we take the trajectory images and and the throwing parameters. These are mapped jointly onto a low-dimensional non-linear manifold. Such models improve with practice, and can be used as the starting point for real-life tasks such as aiming darts or recognizing throws by others. How can such models can be used in learning language? We consider a set of videos involving throwing and rolling actions. These actions are analyzed into a set of contrastive semantic classes based on agent, action, and the thrown object (trajector). We obtain crowdsourced commentaries for these videos (raw text) from a number of adults. The learner attempts to associate labels using contrastive probabilities for the semantic class. Only a handful of high-confidence words are found, but the agent starts off with this partial knowledge. These are used to learn incrementally larger syntactic patterns, initially for the trajector, and eventually for full agent-trajector-action sentences. We demonstrate how this may work for two completely different languages - English and Hindi, and also show how rudiments of agreement, synonymy and polysemy are detected.

We are interested in the manipulation skills required by future service robots performing everyday tasks such as preparing food and cleaning in a typical home environment. Such robots must have a robust set of skills that can be applied in the unpredictable and varying circumstances that arise in everyday life.To succeed in such a setting, a service robot must have a strong ability to transfer old skills to new varied settings. We are inspired by the strong transfer ability demonstrated by infants and toddlers on simple manipulation activities, and we are motivated to try and replicate these abilities in an artificial system.We treat this as a problem of making analogies, and describe a theoretical framework which could account for it. We sketch the ideas of a computational model for implementing the required analogical reasoning.