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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Artificial intelligence (AI) has undergone a renaissance recently, making major progress in key domains such as vision, language, control, and decision-making. This has been due, in part, to cheap data and cheap compute resources, which have fit the natural strengths of deep learning. However, many defining characteristics of human intelligence, which developed under much different pressures, remain out of reach for current approaches. In particular, generalizing beyond one's experiences--a hallmark of human intelligence from infancy--remains a formidable challenge for modern AI. The following is part position paper, part review, and part unification. We argue that combinatorial generalization must be a top priority for AI to achieve human-like abilities, and that structured representations and computations are key to realizing this objective. Just as biology uses nature and nurture cooperatively, we reject the false choice between "hand-engineering" and "end-to-end" learning, and instead advocate for an approach which benefits from their complementary strengths. We explore how using relational inductive biases within deep learning architectures can facilitate learning about entities, relations, and rules for composing them. We present a new building block for the AI toolkit with a strong relational inductive bias--the graph network--which generalizes and extends various approaches for neural networks that operate on graphs, and provides a straightforward interface for manipulating structured knowledge and producing structured behaviors. We discuss how graph networks can support relational reasoning and combinatorial generalization, laying the foundation for more sophisticated, interpretable, and flexible patterns of reasoning. As a companion to this paper, we have released an open-source software library for building graph networks, with demonstrations of how to use them in practice. With the increasing commoditization of computer vision, speech recognition and machine translation systems and the widespread deployment of learning-based back-end technologies such as digital advertising and intelligent infrastructures, AI (Artificial Intelligence) has moved from research labs to production. These changes have been made possible by unprecedented levels of data and computation, by methodological advances in machine learning, by innovations in systems software and architectures, and by the broad accessibility of these technologies. The next generation of AI systems promises to accelerate these developments and increasingly impact our lives via frequent interactions and making (often mission-critical) decisions on our behalf, often in highly personalized contexts. Realizing this promise, however, raises daunting challenges. In particular, we need AI systems that make timely and safe decisions in unpredictable environments, that are robust against sophisticated adversaries, and that can process ever increasing amounts of data across organizations and individuals without compromising confidentiality. These challenges will be exacerbated by the end of the Moore's Law, which will constrain the amount of data these technologies can store and process. In this paper, we propose several open research directions in systems, architectures, and security that can address these challenges and help unlock AI's potential to improve lives and society. From smart homes that prepare coffee when we wake, to phones that know not to interrupt us during important conversations, our collective visions of HCI imagine a future in which computers understand a broad range of human behaviors. Today our systems fall short of these visions, however, because this range of behaviors is too large for designers or programmers to capture manually. In this paper, we instead demonstrate it is possible to mine a broad knowledge base of human behavior by analyzing more than one billion words of modern fiction. Our resulting knowledge base, Augur, trains vector models that can predict many thousands of user activities from surrounding objects in modern contexts: for example, whether a user may be eating food, meeting with a friend, or taking a selfie. Augur uses these predictions to identify actions that people commonly take on objects in the world and estimate a user's future activities given their current situation. We demonstrate Augur-powered, activity-based systems such as a phone that silences itself when the odds of you answering it are low, and a dynamic music player that adjusts to your present activity. A field deployment of an Augur-powered wearable camera resulted in 96 recall and 71 precision on its unsupervised predictions of common daily activities. A second evaluation where human judges rated the system's predictions over a broad set of input images found that 94 were rated sensible. Artificial Intelligence has become one of the most fundamental areas of computer science research. One line of research in artificial intelligence is associated with coordination of intelligent agents. Coordination takes place in multi-agent environments where distributed agents have limited knowledge about their surrounding environment; therefore, they continuously ask other agents to obtain required information. In general, the growing popularity of agents requires systematic coordination management and reputation system that enable agents to decide about their interacting partner and overall acting attitude. Using the reputation system, agents choose reliable agents to interact with. Here, the question arises that how the reputation mechanism helps agents to make the most prudent decisions that yield the best outcome. More specifically, agents need to use a decision-theoretic reasoning algorithm to optimize their decisions under uncertainty. In multi-agent systems, the reputation value is highly competitive because it is used as the beliefs of agents about one another. Thus, a carefully designed mechanism is required to maintain accuracy of this parameter.