The recent breakthroughs in Artificial Intelligence (AI) have allowed individuals to rely on automated systems for a variety of reasons. Some of these systems are the currently popular voice-enabled systems like Echo by Amazon and Home by Google that are also called as Intelligent Personal Assistants (IPAs). Though there are raising concerns about privacy and ethical implications, users of these IPAs seem to continue using these systems. We aim to investigate why users are concerned about privacy and how they are handling these concerns while using the IPAs. By utilizing the reviews posted online along with the responses to a survey, this paper provides a set of insights about the detected markers related to user interests and privacy challenges. The insights suggest that users of these systems irrespective of their concerns about privacy, are generally positive in terms of utilizing IPAs in their everyday lives. However, there is a significant percentage of users who are concerned about privacy and took further actions to address the related concerns. Some percentage of users expressed that they do not have any privacy concerns but when they learned about the "always listening" feature of these devices, their concern about privacy increased.
Intelligent systems are becoming part of our lives as more and more individuals are relying on them for driving @cite_7 , work management @cite_13 @cite_12 , time scheduling @cite_5 @cite_16 , information and email organization @cite_9 @cite_1 , and responding to factual questions @cite_10 @cite_15 . These systems leverage AI to learn and reduce the mistakes made by users due to cognitive overload @cite_12 . In this context, there are multiple debates about whether AI that is primarily used by these intelligent systems is a blessing or curse to the society. One of our previous works @cite_0 investigated the public perceptions of individuals about AI. These individuals comprise both AI experts and common users on Twitter (a popular micro-blogging platform) who are sharing their personal opinions and information about AI. However, we believe that researching the interactions of individuals with the intelligent systems has greater potential to understand the impact of AI on the society to a certain level especially the privacy concerns that come along with that @cite_11 .
@cite_7 @cite_10 @cite_9 @cite_1 @cite_0 @cite_5 @cite_15 @cite_16 @cite_13 @cite_12 @cite_11
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As the complexity of software for Cyber-Physical Systems (CPS) rapidly increases, multi-core processors and parallel programming models such as OpenMP become appealing to CPS developers for guaranteeing timeliness. Hence, a parallel task on multi-core processors is expected to become a vital component in CPS such as a self-driving car, where tasks must be scheduled in real-time. In this paper, we extend the fork-join parallel task model to be scheduled in real-time, where the number of parallel threads can vary depending on the physical attributes of the system. To efficiently schedule the proposed task model, we develop the task stretch transform. Using this transform for global Deadline Monotonic scheduling for fork-join real-time tasks, we achieve a resource augmentation bound of 3.73. In other words, any task set that is feasible on m unit-speed processors can be scheduled by the proposed algorithm on m processors that are 3.73 times faster. The proposed scheme is implemented on Linux RK as a proof of concept, and ported to Boss, the self-driving vehicle that won the 2007 DARPA Urban Challenge. We evaluate our scheme on Boss by showing its driving quality, i.e., curvature and velocity profiles of the vehicle. We are in the midst of a technological revolution whereby, for the first time, researchers can link daily word use to a broad array of real-world behaviors. This article reviews several computerized text analysis methods and describes how Linguistic Inquiry and Word Count (LIWC) was created and validated. LIWC is a transparent text analysis program that counts words in psychologically meaningful categories. Empirical results using LIWC demonstrate its ability to detect meaning in a wide variety of experimental settings, including to show attentional focus, emotionality, social relationships, thinking styles, and individual differences. 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Most information retrieval technologies are designed to facilitate information discovery. However, much knowledge work involves finding and re-using previously seen information. We describe the design and evaluation of a system, called Stuff I've Seen (SIS), that facilitates information re-use. This is accomplished in two ways. First, the system provides a unified index of information that a person has seen, whether it was seen as email, web page, document, appointment, etc. Second, because the information has been seen before, rich contextual cues can be used in the search interface. The system has been used internally by more than 230 employees. We report on both qualitative and quantitative aspects of system use. Initial findings show that time and people are important retrieval cues. Users find information more easily using SIS, and use other search tools less frequently after installation. With the recent advancements in Artificial Intelligence (AI), various organizations and individuals started debating about the progress of AI as a blessing or a curse for the future of the society. This paper conducts an investigation on how the public perceives the progress of AI by utilizing the data shared on Twitter. Specifically, this paper performs a comparative analysis on the understanding of users from two categories -- general AI-Tweeters (AIT) and the expert AI-Tweeters (EAIT) who share posts about AI on Twitter. Our analysis revealed that users from both the categories express distinct emotions and interests towards AI. Users from both the categories regard AI as positive and are optimistic about the progress of AI but the experts are more negative than the general AI-Tweeters. Characterization of users manifested that London' is the popular location of users from where they tweet about AI. Tweets posted by AIT are highly retweeted than posts made by EAIT that reveals greater diffusion of information from AIT. Creating a good schedule for a large conference such as CHI requires taking into account the preferences and constraints of organizers, authors, and attendees. Traditionally, the onus of planning is placed entirely on the organizers and involves only a few individuals. Cobi presents an alternative approach to conference scheduling that engages the entire community to take active roles in the planning process. The Cobi system consists of a collection of crowdsourcing applications that elicit preferences and constraints from the community, and software that enable organizers and other community members to take informed actions toward improving the schedule based on collected information. We are currently piloting Cobi as part of the CHI 2013 planning process. In this paper we explore the power of surface text patterns for open-domain question answering systems. In order to obtain an optimal set of patterns, we have developed a method for learning such patterns automatically. A tagged corpus is built from the Internet in a bootstrapping process by providing a few hand-crafted examples of each question type to Altavista. Patterns are then automatically extracted from the returned documents and standardized. We calculate the precision of each pattern, and the average precision for each question type. These patterns are then applied to find answers to new questions. Using the TREC-10 question set, we report results for two cases: answers determined from the TREC-10 corpus and from the web. One subclass of human computation applications are those directed at tasks that involve planning (e.g. tour planning) and scheduling (e.g. conference scheduling). Interestingly, work on these systems shows that even primitive forms of automated oversight on the human contributors helps in significantly improving the effectiveness of the humans crowd. 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To these ends, we describe the implementation of AI-MIX, a tour plan generation system that uses automated checks and alerts to improve the quality of plans created by human workers; and present a preliminary evaluation of the effectiveness of steering provided by automated planning. We discuss what constitutes an integrated system in AI, and why AI researchers should be interested in building and studying them. Taking integrated systems to be ones that integrate a variety of components in order to perform some task from start to finish, we believe that such systems (a) allow us to better ground our theoretical work in actual tasks, and (b) provide an opportunity for much-needed evaluation based on task performance. We describe one particular integrated system we have developed that supports spoken-language dialogue to collaboratively solve planning problems. We discuss how the integrated system provides key advantages for helping both our work in natural language dialogue processing and in interactive planning and problem solving, and consider the opportunities such an approach affords for the future. We describe an intelligent personal assistant that has been developed to aid a busy knowledge worker in managing time commitments and performing tasks. The design of the system was motivated by the complementary objectives of (1) relieving the user of routine tasks, thus allowing her to focus on tasks that critically require human problem-solving skills, and (2) intervening in situations where cognitive overload leads to oversights or mistakes by the user. The system draws on a diverse set of AI technologies that are linked within a Belief-Desire-Intention (BDI) agent system. Although the system provides a number of automated functions, the overall framework is highly user centric in its support for human needs, responsiveness to human inputs, and adaptivity to user working style and preferences. Assisting users in performing their tasks is an important issue in Human Computer Interaction research. A solution to deal with this challenge is to build a personal assistant agent capable to discover the user's habits, abilities, preferences, and goals, ever more accurately anticipating the user's intentions. In order to solve in an intelligent manner this problem, the assistant agent has to continuously improve its behavior based on previous experiences. By endowing the agent with the learning capability, it will become able to adapt himself to the user's behavior. This paper proposes an intelligent personal assistant agent that learns by supervision to assist users in performing specific tasks. For evaluating the performance of the agent a case study is considered, and a neural network is used by the agent to learn by supervision from its experience. We also provide a comparison of our approach with other similar existing work.