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Ethical Considerations for AI Researchers & A Multifunction Printer Conversational User Interface for the Blind
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NAVER LABS Europe is hosting a guest from PARC.
The seminar run from 11am to 12pm.
Date: 21st May 2019
Speaker: Kyle Dent, research area manager at PARC, USA.
Title: Ethical Considerations for AI Researchers
Abstract: Use of artificial intelligence is growing and expanding into applications that impact people’s lives. People trust their technology without really understanding it or its limitations. There is the potential for harm and we are already seeing examples of that in the world. AI researchers have an obligation to consider the impact of intelligent applications they work on. While the ethics of AI is not clear-cut, there are guidelines we can consider to minimize the harm we might introduce.
Title: A Multifunction Printer Conversational User Interface for the Blind
Abstract: Advances in interface design using touch surfaces creates greater obstacles for blind and visually impaired users of technology. Conversational user interfaces offer a reasonable alternative for interactions and enable greater access and most importantly greater independence for the blind. This talk presents a case study of our work to develop a conversational user interface for accessibility for multifunction printers (MFP). It describes our approach to conversational interfaces in general and the specifics of the solution we created for MFPs. It also presents a user study we performed to assess the solution and guide our future efforts.
To make robots autonomous in real-world everyday spaces, they should be able to learn from their interactions within these spaces, how to best execute tasks specified by non-expert users in a safe and reliable way. To do so requires sequential decision-making skills that combine machine learning, adaptive planning and control in uncertain environments as well as solving hard combinatorial optimisation problems. Our research combines expertise in reinforcement learning, computer vision, robotic control, sim2real transfer, large multimodal foundation models and neural combinatorial optimisation to build AI-based architectures and algorithms to improve robot autonomy and robustness when completing everyday complex tasks in constantly changing environments.
VISION
The research we conduct on expressive visual representations is applicable to visual search, object detection, image classification and the automatic extraction of 3D human poses and shapes that can be used for human behavior understanding and prediction, human-robot interaction or even avatar animation. We also extract 3D information from images that can be used for intelligent robot navigation, augmented reality and the 3D reconstruction of objects, buildings or even entire cities.
Our work covers the spectrum from unsupervised to supervised approaches, and from very deep architectures to very compact ones. We’re excited about the promise of big data to bring big performance gains to our algorithms but also passionate about the challenge of working in data-scarce and low-power scenarios.
Furthermore, we believe that a modern computer vision system needs to be able to continuously adapt itself to its environment and to improve itself via lifelong learning. Our driving goal is to use our research to deliver embodied intelligence to our users in robotics, autonomous driving, via phone cameras and any other visual means to reach people wherever they may be.
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