Hypothetical “value learning” AIs learn human values and then try to act according to those values. The design of such AIs, however, is hampered by the fact that there exists no satisfactory definition of what exactly human values are. After arguing that the standard concept of preference is insufficient as a definition, I draw on reinforcement learning theory, emotion research, and moral psychology to offer an alternative definition. In this definition, human values are conceptualized as mental representations that encode the brain’s value function (in the reinforcement learning sense) by being imbued with a context-sensitive affective gloss. I finish with a discussion of the implications that this hypothesis has on the design of value learners.

Hashtags are useful in many applications, such as tweet classification, clustering, searching, indexing and social network analysis. This study seeks to recommend relevant Twitter hashtags for health-related keywords based on distributed language representations, generated by the state-of-the-art Deep Learning technology. The word embeddings are built from billions of tweet words without supervision. To the best of our knowledge, this is the first study of applying distributed language representations to recommending hashtags for keywords. The experiment showed that this approach outperformed the baseline approach that is based on keyword and hashtag co-occurrence in tweets.

This study investigates the public health intelligence utility of Yik Yak, a social media platform that allows users to anonymously post and view messages within precise geographic locations. Our dataset contains 122,179 “yaks” collected from 120 college campuses across the United States during 2015. We first present an exploratory analysis of the topics commonly discussed in Yik Yak, clarifying the health issues for which this may serve as a source of information. We then present an in-depth content analysis of data describing substance use, an important public health issue that is not often discussed in public social media, but commonly discussed on Yik Yak under the cloak of anonymity.

The capability of collaboration is critical in the design of symbiotic cognitive systems. To obtain this functional capability, a cognitive system should possess evaluative and communicative processes. Emotions and their underlying processes provide such functions in social and collaborative environments. We investigate the mutual influence of affective and collaboration processes in a cognitive theory to support the interaction between humans and robots or virtual agents. We have developed new algorithms for these processes, as well as a new overall computational model for implementing collaborative robots and agents. We build primarily on the cognitive appraisal theory of emotions and the SharedPlans theory of collaboration to investigate the structure, fundamental processes and functions of emotions in a collaboration context.

To enable effective collaborations between humans and cognitive robots, it is important for robots to continuously acquire task knowledge from human partners. To address this issue, we are currently developing a framework that supports task learning through visual demonstration and natural language dialogue. One core component of this framework is the integration of language and vision that is driven by dialogue for task knowledge learning. This paper describes our on-going effort, particularly, grounded task learning through joint processing of video and dialogue using And-Or-Graphs (AOG).

Home environments constitute a main target location where to deploy robots, which are expected to help humans in completing their tasks. However, modern robots do not meet yet user's expectations in terms of both knowledge and skills. In this scenario, users can provide robots with knowledge and help them in performing tasks, through a continuous human-robot interaction. This human-robot cooperation setting in shared environments is known as Symbiotic Autonomy or Symbiotic Robotics. In this paper, we address the problem of an effective coexistence of robots and humans, by analyzing the proposed approaches in literature and by presenting our perspective on the topic. In particular, our focus is on specific contexts that can be embraced within Symbiotic Autonomy: Human Augmented Semantic Mapping, Task Teaching and Social Robotics. Finally, we sketch our view on the problem of knowledge acquisition in robotic platforms by introducing three essential aspects that are to be dealt with: environmental, procedural and social knowledge.

It is well known that the title of an article impacts how well it is discovered by potential readers and read. With both people and search engines, acting on behalf of people, accessing papers from digital libraries, it is important that the paper titles should promote discovery. In this paper, we investigate the characteristics of titles of AI papers and then propose au- tomatic ways to augment them so that they can be better in- dexed and discovered by users. A user study with researchers shows that they overwhelmingly prefer the augmented titles over the originals for being more helpful.

The general public shows great potential for utilizing scientific research. For example, a singer discovered her ectopic pregnancy by looking up clinical case reports. However, an exorbitant paywall impedes the public’s access to scientific literature. Our case study on a social network demonstrates a growing need for non-open access publications, especially for biomedical literature. The challenge is that non-open access papers are protected by copyright licenses that bar free distribution. In this paper, we propose a technical framework that leverages the doctrine of "idea-expression dichotomy" to bring ideas across paywalls. Idea-expression dichotomy prevents copyright holders from monopolizing ideas, theories, facts, and concepts. Therefore facts may pass through paywalls unencumbered by copyright license restrictions. Existing fact extraction methods (such as information extraction) require either large training sets or domain knowledge, which is intractable for the diverse biomedical scope spanning from clinical findings to genomics. We therefore develop a rule-based system to represent and extract facts. Social networkers and academics validated the effectiveness of our approach. 7 out of 9 users rated the paper’s information from the facts to be above average (≥6/10). Only 7% of the extracted facts were rated misleading.

Scientific charts in the web, whether as images or embedded in digital documents, contain valuable information that is not fully available to information retrieval tools. The information used to describe these charts is typically extracted from the image metadata rather than the information the graphic was initially designed to express. The problem of understanding digital charts found in scholarly documents, and inferring useful textual information from their graphical components is the focus of this study. We present an approach to automatically read the chart data, specifically bar charts, and provide the user with a textual summary of the chart. The proposed method follows a knowledge discovery approach that relies on a versatile graph representation of the chart. This representation is derived from analyzing a chart's original data values, from which useful features are extracted. The data features are in turn used to construct a semantic-graph. To generate a summary, the semantic-graph of the chart is mapped to appropriately crafted protoforms, which are constructs based on fuzzy logic. We verify the effectiveness of our framework by conducting experiments on bar charts extracted from over 1,000 PDF documents. Our preliminary results show that, under certain assumptions, 83% of the produced summaries provide plausible descriptions of the bar charts.

Temporal logics have been used in autonomous planningto represent and reason about temporal planning problems.However, such techniques have typically been restricted toeither (1) representing actions, events, and goals with temporalproperties or (2) planning for temporally-extended goalsunder restrictive conditions of classical planning. We introduceMixed Propositional Metric Temporal Logic (MPMTL),where formulae in MPMTL are built over mixed binary andcontinuous real variables. MPMTL provides a natural, flexibleformalism for representing and reasoning about temporalproblems. We analyze the complexity of MPMTL formulaesatisfiability and model checking, and identify MPMTLfragments with lower complexity. We also introduce an approachto world modeling using a timeline vector, relevant totemporal planning with continuous change (as opposed to theuse of discrete states). Our model supports retroactive actionprogression, concurrent and overlapping actions with discreteand continuous changes, and concurrent effects to the samevariable. For reasoning about this temporal planning problem,we define a progression function for actions with thenew temporal properties and a solution to this temporal task.