Virtual assistant

Summary

A virtual assistant (VA) is a software agent that can perform a range of tasks or services for a user based on user input such as commands or questions, including verbal ones. Such technologies often incorporate chatbot capabilities to simulate human conversation, such as via online chat, to facilitate interaction with their users. The interaction may be via text, graphical interface, or voice - as some virtual assistants are able to interpret human speech and respond via synthesized voices. In many cases users can ask their virtual assistants questions, control home automation devices and media playback, and manage other basic tasks such as email, to-do lists, and calendars - all with verbal commands. In recent years, prominent virtual assistants for direct consumer use have included Amazon's Alexa, Apple's Siri, Microsoft's Cortana, and Google Assistant. Also, companies in various industries often incorporate some kind of virtual assistant technology into their customer service or support. Recently, the emergence of recent artificial intelligence based chatbots, such as ChatGPT, has brought increased capability and interest to the field of virtual assistant products and services. Radio Rex was the first voice activated toy, patented in 1916 and released in 1922. It was a wooden toy in the shape of a dog that would come out of its house when its name is called. In 1952, Bell Labs presented "Audrey", the Automatic Digit Recognition machine. It occupied a six- foot-high relay rack, consumed substantial power, had streams of cables and exhibited the myriad maintenance problems associated with complex vacuum-tube circuitry. It could recognize the fundamental units of speech, phonemes. It was limited to accurate recognition of digits spoken by designated talkers. It could therefore be used for voice dialing, but in most cases push-button dialing was cheaper and faster, rather than speaking the consecutive digits.

Official source

https://en.wikipedia.org/wiki/Virtual_assistant

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Air Navigation Service Provider (ANSPs) replace paper flight strips through different digital solutions. The instructed commands from an air traffic controller (ATCOs) are then available in computer readable form. However, those systems require manual controller inputs, i.e. ATCOs’ workload increases. The Active Listening Assistant (AcListant®) project has shown that Assistant Based Speech Recognition (ABSR) is a potential solution to reduce this additional workload. However, the development of an ABSR application for a specific target-domain usually requires a large amount of manually transcribed audio data in order to achieve task- sufficient recognition accuracies. MALORCA project developed an initial basic ABSR system and semi-automatically tailored its recognition models for both Prague and Vienna approach by machine learning from automatically transcribed audio data. Command recognition error rates were reduced from 7.9% to under 0.6% for Prague and from 18.9% to 3.2% for Vienna.

2018

In surgical to industrial manipulation, the operator needs assistance for tasks requiring more than two hands. Teamwork may be the source of errors and inefficiency, especially if the assistant is a novice or unfamiliar with the main operator. The need for assistance may become problematic in case of lack of human resources e.g. in emergency surgical cases in the late hours of the night. Our objective is to improve the surgeon's autonomy and dexterity by a robotic arm under his own control. Although a number of robotic instrument holders have been developed, the best way to control such devices is still an open question. No behavioral study has been conducted on the best control strategy and human performance in three-handed tasks. We have selected the foot for commanding the third arm on the basis of a literature review. A series of experiments in virtual environments has been conducted to study the feasibility of this choice. The first experiment compares performance in the same task using two or three hands. Results show that three-handed manipulation is preferred to two-handed manipulation in demanding tasks. The second experiment investigated the type of tasks to be aimed in three-handed manipulation and the learning curve of users. Moving the hands and a foot simultaneously in opposite directions was perceived as difficult compared to a more active task with liberty in choosing the limbs coordination. Limbs were moved in parallel rather than serially. The performance improved within a few minutes of practice. Also, the sense of ownership improved constantly during the experiment. Two other experiments were aimed at handling the endoscope in laparoscopic surgery. Surgeons and medical students participated in these studies. Residents had a more positive approach towards foot usage and performed better compared to more experienced surgeons. This proves that the best training period for surgeons to use a foot controlled robotic arm is during their residency. A realistic virtual abdominal cavity has been developed for the last experiment. This had a positive influence on the participants' performance and emphasizes the importance of using a familiar context for training such a "three-handed surgery". Finally, two different foot interfaces were developed to investigate the most intuitive third arm commanding strategy. A robotic arm is hence controlled by the foot's translation or rotation in one interface (isotonic interface), and by force or torque in the other one (isometric interface). An experimental behavioral study was conducted to compare the two devices. Isometric rate control was preferred to isotonic position control due to the lower physical burden and higher movement accuracy of the robot. It was shown that the proposed device for isometric rate control could be used for intuitive control of four DoFs of a slave robotic arm. This thesis is the first step in a systematic investigation of a three-handed manipulation, two biological hands and a foot controlled robotic assistant. Findings suggest a high potential in using the foot to become more autonomous in surgery as well as other fields. Users can learn the control paradigm in a short period of time with little mental and physical burden. We expect the developed foot interfaces to be the basis of future development of more intuitive control interfaces. We believe that foot controlled robotic arms will be commonly used in various surgical as well as industrial applications.

EPFL2017

Computers have become affordable, small, omnipresent and are often connected to the Internet. However, despite the availability of such rich environments, user interfaces have not been adapted to fully leverage their potential. In our view, user interfaces will evolve to become more than simple tools that act using a click-and-act paradigm – people want software that can act as assistants to whom tasks can be delegated. This new type of software will provide us with more user-centric systems, able to interact naturally with human users and with the information environment. Although progress has been made in this direction over the last decade, current research in the field has shown that building intelligent assistants is a complex task that requires expertise in many fields, ranging from artificial intelligence to core software and hardware engineering. The difficulty of deeply integrating all the technologies and AI methodologies required to produce robust intelligent assistants has greatly limited the impact and widespread adoption of this form of software. In this thesis, we propose to design, implement and evaluate a new methodology and associated tool suite aimed to ease and accelerate the development of intelligent assistant software. Active, our solution, introduces the original concept of Active Ontologies, and combines it with a service oriented architecture to serve as the foundation for user-centric applications. The Active software suite features a programming editor, a runtime server and associated management tools. Using this unified platform, we developed techniques for rapidly creating intelligent assistant applications that weave together language processing, process modeling and dynamic service orchestration. To validate our approach, three prototypes have been implemented and evaluated. First, we created an assistant that helps mobile users retrieve data and access online services. The system allows users to fetch information through natural language dialog about restaurants, hotels, points of interest, flights status and weather forecasts. As a second prototype, we have implemented an assistant to help surgeons in the context of the operating room. The system uses a combination of voice and gesture recognition to help surgeons navigate through pre-operative information, visualize and control a live video stream coming from an endoscope mounted on a robotic arm. Lastly, we have created a system that helps organize meetings through emails and instant messages with the organizer and attendees. These three prototypes are deployed in very different application domains, and yet are built with the same tools and methods; this demonstrates the flexibility and versatility of our approach. We conducted a user study to validate our claim that, in specific domains, intelligent assistant software can perform better than conventional software approaches. First, we compared our mobile assistant against Google Mobile™, the leading commercially available search application for mobile users. We asked a population of users to accomplish ten travel-related tasks with both systems. Results show that, for the travel domain, our assistant-based system performs significantly better both in terms of effectiveness and time to completion than the more conventional, keyword-based search engine. In the medical domain, we asked a small population of surgeons to accomplish a set of tasks with our second prototype. After providing us with a positive feedback, surgeons used our prototype to discuss and experiment with innovative forms of user interaction in the context of the operating room. We also performed evaluations to support our claim that our unified approach accelerates the software development of intelligent assistants. First, we gave basic training on the Active platform to a population of software developers, asking them to go through a simple tutorial. Once trained, they were able to successfully program the core component of the mobile assistant prototype in under two hours. As a second validation, the meeting organizing assistant was designed, implemented and tested within three working days – comparable systems published in the literature were the product of significantly more time and resource investment. This demonstrates that a system encompassing numerous AI and software development aspects, including language processing, multiple modalities, contextual dialog, processing logic and backend services integration, can be rapidly implemented and tested using the Active framework. Our work shows that for many task-oriented domains, intelligent assistant applications can be significantly more effective than conventional systems. Through our Active Ontology approach and associated methodologies and tools, we have shown that complex intelligent assistant applications, perhaps for the first time, can be developed with low cost and high reliability. In addition to receiving a positive echo from the research world, our work is also causing ripples in the commercial world.

EPFL2008