People are becoming increasingly comfortable using Digital Assistants (DAs) to interact with services or connected objects. However, for non-programming users, the available possibilities for customizing their DA are limited and do not include the possibility of teaching the assistant new tasks. To make the most of the potential of DAs, users should be able to customize assistants by instructing them through Natural Language (NL). To provide such functionalities, NL interpretation in traditional assistants should be improved: (1) The intent identification system should be able to recognize new forms of known intents, and to acquire new intents as they are expressed by the user. (2) In order to be adaptive to novel intents, the Natural Language Understanding module should be sample efficient, and should not rely on a pretrained model. Rather, the system should continuously collect the training data as it learns new intents from the user. In this work, we propose AidMe (Adaptive Intent Detection in Multi-Domain Environments), a user-in-the-loop adaptive intent detection framework that allows the assistant to adapt to its user by learning his intents as their interaction progresses. AidMe builds its repertoire of intents and collects data to train a model of semantic similarity evaluation that can discriminate between the learned intents and autonomously discover new forms of known intents. AidMe addresses two major issues - intent learning and user adaptation - for instructable digital assistants. We demonstrate the capabilities of AidMe as a standalone system by comparing it with a one-shot learning system and a pretrained NLU module through simulations of interactions with a user. We also show how AidMe can smoothly integrate to an existing instructable digital assistant.
Accurate prediction of the user intent to interact with a voice assistant (VA) on a device (e.g. on the phone) is critical for achieving naturalistic, engaging, and privacy-centric interactions with the VA. To this end, we present a novel approach to predict the user's intent (the user speaking to the device or not) directly from acoustic and textual information encoded at subword tokens which are obtained via an end-to-end ASR model. Modeling directly the subword tokens, compared to modeling of the phonemes and/or full words, has at least two advantages: (i) it provides a unique vocabulary re…
Generative AI Assistants in Software Development Education: A vision for integrating Generative AI into educational practice, not instinctively defending against it
Newcastle University（纽卡斯尔大学）
针对生成式 AI 编程助手带来的行业变革，本文通过访谈从业者了解实践与挑战，提出将 GAI 融入软件开发教育的愿景及教学建议。
University of California, Davis（加州大学戴维斯分校）；Columbia University（哥伦比亚大学）
Humans are increasingly interacting with machines through language, sometimes in contexts where the user may not know they are talking to a machine (like over the phone or a text chatbot). We aim to understand how system designers and researchers might allow their systems to confirm its non-human identity. We collect over 2,500 phrasings related to the intent of ``Are you a robot?". This is paired with over 2,500 adversarially selected utterances where only confirming the system is non-human would be insufficient or disfluent. We compare classifiers to recognize the intent and discuss the prec…
This paper reports the findings of a study where users (N=220) interacted with Malexa, Alexa's malicious twin. Malexa is an intelligent voice assistant with a simple and seemingly harmless third-party skill that delivers news briefings to users. The twist, however, is that Malexa covertly rewords these briefings to intentionally introduce misperception about the reported events. This covert rewording is referred to as a Malware-Induced Misperception (MIM) attack. It differs from squatting or invocation hijacking attacks in that it is focused on manipulating the "content" delivered through a th…
AREAs-Lab: An Interactive Environment for AI-driven Requirement Elicitation for AI Systems
AI Foundations, Capital One（Capital One AI Foundations）；Emory University（埃默里大学）；University of Wisconsin–Madison（威斯康星大学麦迪逊分校）；University of Central Florida（中佛罗里达大学）
