Correction to: Intelligent Assistants

Intelligent assistants can serve many purposes, including entertainment (e.g. playing music), home automation, and task management (e.g. timers, reminders). The role of these assistants is evolving to also support people engaged in work tasks, in workplaces and beyond. To design truly useful intelligent assistants for work, it is important to better understand the work tasks that people are performing. Based on a survey of 401 respondents' daily tasks and activities in a work setting, we present a classification of work-related tasks, and analyze their key characteristics, including the frequency of their self-reported tasks, the environment in which they undertake the tasks, and which, if any, electronic devices are used. We also investigate the cyber, physical, and social aspects of tasks. Finally, we reflect on how intelligent assistants could influence and help people in a work environment to complete their tasks, and synthesize our findings to provide insight on the future of intelligent assistants in support of amplifying personal productivity.

Intelligent assistants have proliferated across various settings spurring research and ideas. However, there is a lack of clarity on how they can be defined and identified. This definitional and terminological incoherence manifests in narrow and misinformed understanding of technology and thus, poses threats to theoretical and empirical endeavors in AI. To overcome these challenges, this paper presents a definitional framework that defines intelligent assistants and distinguishes them from other technologies. This framework is informed by tracing the historical evolution of this technology in the past 70 years which saw the rise of three constitutive features in machines: AI, interaction, and assistance. A review of technology’s definitions published since 2010 and associated terminologies was also conducted to unpack their conceptual limitations and support the framework. A new definition is then presented which contends that a technology can only be characterized as an intelligent assistant if it is AI-enabled, interactive, and assistive.

The Intelligent Assistant (IA) is an integrated system of intelligent software agents that helps the user with communication, information and time management. The IA includes specialist assistants for e-mail prioritization and telephone call filtering (communication management), Web search and Yellow Pages® lookup (information management), and calendar scheduling (time management). Each such assistant is designed to have a model of the user and a learning module for acquiring user preferences. In addition, the IA includes a toolbar providing a graphical interface to the system, a multimodal interface for accepting spoken commands and tracking the user’s activity, and a co-ordinator responsible for managing communication from the system to the user and for initiating system activities on the user’s behalf. A primary design objective of the IA is that its operation is as transparent as possible, to enable the user to control the system as far as is practicable without incurring a heavy overhead when creating and modifying the system’s behaviour. Hence each specialist assistant is designed to represent its user model in a way that is intuitively understandable to non-technical users, and is configured to adaptively modify its user model through time to accommodate the user’s changing preferences. However, in contrast to adaptive interface agents built under the behaviour-based paradigm, the assistants in the IA embrace complex AI representations and machine learning techniques to accomplish more sophisticated behaviour.

The design of Brain-Machine Interface (BMI) neural decoders that have robust performance in changing environments encountered in daily life activity is a challenging problem. One solution to this problem is the design of neural decoders that are able to assist and adapt to the user by participating in their perception-action-reward cycle (PARC). Using inspiration both from artificial intelligence and neurobiology reinforcement learning theories, we have designed a novel decoding architecture that enables a symbiotic relationship between the user and an Intelligent Assistant (IA). By tapping into the motor and reward centers in the brain, the IA adapts the process of decoding neural motor commands into prosthetic actions based on the user's goals. The focus of this paper is on extraction of goal information directly from the brain and making it accessible to the IA as an evaluative feedback for adaptation. We have recorded the neural activity of the Nucleus Accumbens in behaving rats during a reaching task. The peri-event time histograms demonstrate a rich representation of the reward prediction in this subcortical structure that can be modeled on a single trial basis as a scalar evaluative feedback with high precision.

With the latest technological advances, intelligent personal assistants are becoming part of our daily lives. In the future, with improvements in artificial intelligence techniques, it is foreseen that individuals’ life management will be greatly assisted by intelligent personal assistants. Today, it seems that intelligent personal assistants have not yet reached their full potential. For more efficient use, more technical developments are needed, especially in terms of natural language processing. In order to that, areas which need improvement must be determined based on which challenges are faced. In order to develop more efficient solutions for problems, an initial fundamental evaluation is necessary. In this work, features of intelligent personal assistants are examined and existing applications are referred to. For a better understanding of what features an intelligent personal assistant must contain, a literature review was completed. In-use features which should be improved in the future, challenges challenges which need to be overcome, areas which could be made more efficient, and how to increase usage are determined. 

Can people experience romantic love for artificial intelligence? An empirical study of intelligent assistants

An intelligent assistant can support the execution of the software process based on artificial intelligence techniques. This paper presents the application of planning techniques to support the process of developing software based on the fusion method. The first level of the designed library of operators as well as the features of the algorithms for intelligent assistance are presented. The lessons learned are discussed based on the evaluation of the prototype developed to validate the proposed techniques.

Intelligent assistants are becoming widespread. A popular method for creating intelligent assistants is modeling the domain (and thus the assistant's capabilities) as Active Ontology. Adding new functionality requires extending the ontology or building new ones; as of today, this process is manual.We describe an automated method for creating Active Ontologies for arbitrary web forms. Our approach leverages methods from natural language processing and data mining to synthesize the ontologies. Furthermore, our tool generates the code needed to process user input.We evaluate the generated Active Ontologies in three case studies using web forms from the UIUC Web Integration Repository, namely from the domains airfare, automobile, and book search. First, we examine how much of the generation process can be automated and how well the approach identifies domain concepts and their relations. Second, we test how well the generated Active Ontologies handle end-user input to perform the desired actions. In our evaluation, Easier automatically generates 65% of the Active Ontologies' sensor nodes; the generated ontology for airfare search correctly answers 70% of the queries.

Voice-activated intelligent assistants, such as Siri, Google Now, and Cortana, are prevalent on mobile devices. However, it is challenging to evaluate them due to the varied and evolving number of tasks supported, e.g., voice command, web search, and chat. Since each task may have its own procedure and a unique form of correct answers, it is expensive to evaluate each task individually. This paper is the first attempt to solve this challenge. We develop consistent and automatic approaches that can evaluate different tasks in voice-activated intelligent assistants. We use implicit feedback from users to predict whether users are satisfied with the intelligent assistant as well as its components, i.e., speech recognition and intent classification. Using this approach, we can potentially evaluate and compare different tasks within and across intelligent assistants ac-cording to the predicted user satisfaction rates. Our approach is characterized by an automatic scheme of categorizing user-system interaction into task-independent dialog actions, e.g., the user is commanding, selecting, or confirming an action. We use the action sequence in a session to predict user satisfaction and the quality of speech recognition and intent classification. We also incorporate other features to further improve our approach, including features derived from previous work on web search satisfaction prediction, and those utilizing acoustic characteristics of voice requests. We evaluate our approach using data collected from a user study. Results show our approach can accurately identify satisfactory and unsatisfactory sessions.

As autonomous systems become more complicated, humans may have difficulty deciphering autonomy-generated solutions and increasingly perceive autonomy as a mysterious black box. The lack of transparency contributes to the lack of trust in autonomy and suboptimal team performance. In response to this concern, researchers have proposed various methods to enhance autonomy transparency and evaluated how enhanced transparency could affect the people’s trust and the human-autonomy team performance. However, the majority of prior studies measured trust at the end of the experiment and averaged behavioral and performance measures across all trials in an experiment, yet overlooked the temporal dynamics of those variables. We have little understanding of how autonomy transparency affects trust, dependence, and performance over time. The present study, therefore, aims to fill the gap and examine such temporal dynamics. We develop a game Treasure Hunter wherein a human uncovers a map for treasures with the help from an intelligent assistant. The intelligent assistant recommends where the human should go next. The rationale behind each recommendation could be conveyed in a display that explicitly lists the option space (i.e., all the possible actions) and the reason why a particular action is the most appropriate in a given context. Results from a human-in-the-loop experiment with 28 participants indicate that by conveying the intelligent assistant’s decision-making rationale via the display, participants’ trust increases significantly and becomes more calibrated over time. Using the display also leads to a higher acceptance of recommendations from the intelligent agent.

In this modern age of information, the answer to any question is readily available on the internet. The chronology of the solution to open-domain question-answering capabilities is primitive manual question mining, followed by search engine revolution. Later, intelligent assistants became a robust method, which is being used for a long time. However, it has been noticed that the intelligent assistant responds to simple questions but not to the complicated ones that demand comprehension of the question's abstract. This paper introduces an innovative, dynamic technique called CATAQ, i.e., Concise Answer to Any Question that responds to complicated questions by providing accurate answers. Furthermore, CATAQ introduces a solution for dynamic information retrieval, which involves the selection of the most appropriate webpage links, based on the link parsing the content of the webpage irrespective of its structure, pre-processing the content, extracting the pertinent information with semantic search and summarizing by prioritizing question's keywords. It is observed that CAT AQ outperformed the present intelligent assistants while answering complex questions which include anunusual and large variety of topics and keeping the performance consistent while answering simple questions. Concluding, CATAQ can answer any question concisely, assuming the related information is available on the internet.

Human-computer interaction with people whose visual perception is limited is possible only with tactile and voice interfaces, the latter are being used more and more recently. The aim of the work is to create an intelligent assistant for an indoor navigation system designed for blind and visually impaired people. The development of an intelligent assistant is based on a semantic user model and a four-level ontological model of the subject domain. To build a dialogue between an intelligent assistant and a user, we use the theory of speech acts, argumentation theory and case-based reasoning. The developed software system is aimed at identifying the desires and user needs and proposing possible user actions aimed at achieving them. The system allows for the decomposition of user tasks and the formation of a sequence of their execution based on semantic models of the user and the subject domain.

Statitsical relational models have been successfully used to model static probabilistic relationships between the entities of the domain. In this talk, we illustrate their use in a dynamic decison-theoretic setting where the task is to assist a user by inferring his intentional structure and taking appropriate assistive actions. We show that the statistical relational models can be used to succintly express the system's prior knowledge about the user's goal-subgoal structure and tune it with experience. As the system is better able to predict the user's goals, it improves the effectiveness of its assistance. We show through experiments that both the hierarchical structure of the goals and the parameter sharing facilitated by relational models significantly improve the learning speed.

A tremendous amount of effort is spent by scientists to develop smart wearable devices that can monitor and track a patient's health status 24/7. Such smart wearable devices need to be equipped with intelligence in order to make decisions as humans do. Development of such an intelligent virtual medical assistant would be a better solution for monitoring physical and mental health. In addition, it can assist those who have movement and transportation issues in physical diagnosis and checkups. Older people and those who have movement-related diseases could benefit; such intelligent virtual medical assistants would be a boon either indoors or outdoors. A simple loT-enabled wearable device is an loT device with sensors to monitor health status on some basic parameters such as temperature, blood pressure, oxygen level, etc. However, in order to provide smart healthcare, intelligence needs to be embedded in these kinds of virtual assistants. This article discusses the application of Machine Intelligence (ML) algorithms to intelligent virtual medical assistants to provide improved solutions by tracking patients' historical data along with current data. Such assistants can then provide suggestions, notifications, and medical prescriptions for self improvement.

Abstract. Map verification plays a crucial role in ensuring the accuracy and reliability of map data, which in turn establishes the foundation for map transparency. In China, the number of approved map inspections has been increasing annually, with a diverse range of map products being applied for inspection. Taking the example of the 'number of maps accepted for inspection' by the Ministry of Natural Resources of the People’s Republic of China, there has been a consistent upward trend in the number of accepted inspections. From 2017 to 2021, the average annual growth rate of accepted inspections exceeds 18.85%. Not only are new types, uses, and formats of map products emerging, but there is also an increase in the utilization of electronic global maps. However, challenges such as the inefficient information transfer during the inspection application process and the limited automation of technical inspections persist. Furthermore, issues related to the variability of inspection results based on individual experiences need to be addressed. To tackle these problems, this paper proposes a comprehensive technical framework for map verification based on the principles of "data sharing, business collaboration, and intelligent assistance". The objective is to establish a map verification service mechanism that promotes cross-departmental coordination and intelligent cooperation. Implementing this framework will facilitate the smooth operation of national map verification services and expedite the sharing and openness of geospatial data.