Heads Up eXperience (HUX): Always-On AI Companion for Human Computer Environment Interaction
- URL: http://arxiv.org/abs/2407.19492v1
- Date: Sun, 28 Jul 2024 13:15:51 GMT
- Title: Heads Up eXperience (HUX): Always-On AI Companion for Human Computer Environment Interaction
- Authors: Sukanth K, Sudhiksha Kandavel Rajan, Rajashekhar V S, Gowdham Prabhakar,
- Abstract summary: Heads Up eXperience (HUX) is an AI system designed to bridge the gap between digital and human environments.
By tracking the user's eye gaze, analyzing the surrounding environment, and interpreting verbal contexts, the system captures and enhances multi-modal data.
Intended for deployment in smart glasses and extended reality headsets, HUX AI aims to become a personal and useful AI companion for daily life.
- Score: 0.5825410941577593
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: While current personal smart devices excel in digital domains, they fall short in assisting users during human environment interaction. This paper proposes Heads Up eXperience (HUX), an AI system designed to bridge this gap, serving as a constant companion across the extended reality (XR) environments. By tracking the user's eye gaze, analyzing the surrounding environment, and interpreting verbal contexts, the system captures and enhances multi-modal data, providing holistic context interpretation and memory storage in real-time task specific situations. This comprehensive approach enables more natural, empathetic and intelligent interactions between the user and HUX AI, paving the path for human computer environment interaction. Intended for deployment in smart glasses and extended reality headsets, HUX AI aims to become a personal and useful AI companion for daily life. By integrating digital assistance with enhanced physical world interactions, this technology has the potential to revolutionize human-AI collaboration in both personal and professional spheres paving the way for the future of personal smart devices.
Related papers
- Explainable Human-AI Interaction: A Planning Perspective [32.477369282996385]
AI systems need to be explainable to the humans in the loop.
We will discuss how the AI agent can use mental models to either conform to human expectations, or change those expectations through explanatory communication.
While the main focus of the book is on cooperative scenarios, we will point out how the same mental models can be used for obfuscation and deception.
arXiv Detail & Related papers (2024-05-19T22:22:21Z) - AIris: An AI-powered Wearable Assistive Device for the Visually Impaired [0.0]
We introduce AIris, an AI-powered wearable device that provides environmental awareness and interaction capabilities to visually impaired users.
We have created a functional prototype system that operates effectively in real-world conditions.
arXiv Detail & Related papers (2024-05-13T10:09:37Z) - On the Emergence of Symmetrical Reality [51.21203247240322]
We introduce the symmetrical reality framework, which offers a unified representation encompassing various forms of physical-virtual amalgamations.
We propose an instance of an AI-driven active assistance service that illustrates the potential applications of symmetrical reality.
arXiv Detail & Related papers (2024-01-26T16:09:39Z) - Agent AI: Surveying the Horizons of Multimodal Interaction [83.18367129924997]
"Agent AI" is a class of interactive systems that can perceive visual stimuli, language inputs, and other environmentally-grounded data.
We envision a future where people can easily create any virtual reality or simulated scene and interact with agents embodied within the virtual environment.
arXiv Detail & Related papers (2024-01-07T19:11:18Z) - Spatial Computing and Intuitive Interaction: Bringing Mixed Reality and
Robotics Together [68.44697646919515]
This paper presents several human-robot systems that utilize spatial computing to enable novel robot use cases.
The combination of spatial computing and egocentric sensing on mixed reality devices enables them to capture and understand human actions and translate these to actions with spatial meaning.
arXiv Detail & Related papers (2022-02-03T10:04:26Z) - Human in the Loop for Machine Creativity [0.0]
We conceptualize existing and future human-in-the-loop (HITL) approaches for creative applications.
We examine and speculate on long term implications for models, interfaces, and machine creativity.
We envision multimodal HITL processes, where texts, visuals, sounds, and other information are coupled together, with automated analysis of humans and environments.
arXiv Detail & Related papers (2021-10-07T15:42:18Z) - A User-Centred Framework for Explainable Artificial Intelligence in
Human-Robot Interaction [70.11080854486953]
We propose a user-centred framework for XAI that focuses on its social-interactive aspect.
The framework aims to provide a structure for interactive XAI solutions thought for non-expert users.
arXiv Detail & Related papers (2021-09-27T09:56:23Z) - Embodied AI-Driven Operation of Smart Cities: A Concise Review [3.441021278275805]
Embodied AI focuses on learning through interaction with the surrounding environment.
We will go through its definitions, its characteristics, and its current achievements along with different algorithms, approaches, and solutions.
We will then explore all the available simulators and 3D interactable databases that will make the research in this area feasible.
arXiv Detail & Related papers (2021-08-22T19:14:59Z) - Cognitive architecture aided by working-memory for self-supervised
multi-modal humans recognition [54.749127627191655]
The ability to recognize human partners is an important social skill to build personalized and long-term human-robot interactions.
Deep learning networks have achieved state-of-the-art results and demonstrated to be suitable tools to address such a task.
One solution is to make robots learn from their first-hand sensory data with self-supervision.
arXiv Detail & Related papers (2021-03-16T13:50:24Z) - AEGIS: A real-time multimodal augmented reality computer vision based
system to assist facial expression recognition for individuals with autism
spectrum disorder [93.0013343535411]
This paper presents the development of a multimodal augmented reality (AR) system which combines the use of computer vision and deep convolutional neural networks (CNN)
The proposed system, which we call AEGIS, is an assistive technology deployable on a variety of user devices including tablets, smartphones, video conference systems, or smartglasses.
We leverage both spatial and temporal information in order to provide an accurate expression prediction, which is then converted into its corresponding visualization and drawn on top of the original video frame.
arXiv Detail & Related papers (2020-10-22T17:20:38Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.