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GVU Brown Bag Seminars
GVU Brown Bag Seminars
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ItemAugment, Diminish, Remap Reality: Freeing the Mind and its Resources(Georgia Institute of Technology, 2023-11-30) McLaughlin, AnneOur senses and minds construct our reality. Both are inherently limited and we naturally seek tools to improve our experiences. Anyone who covers their ears as a siren roars past, turns on closed captions, or dons sunglasses on a bright day has altered ‘reality.’ As technology advances, we can also control reality with cutting-edge extended reality (XR) technologies, which add to, subtract from, and remap sounds and visuals in our world. This presentation will cover the perceptual and mental processes underlying XR cognition aids, with methods of testing the effectiveness of these aids, current domains of inquiry, and results from several laboratory experiments on how altering visual and auditory reality can improve a person’s performance and experience.
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ItemInvestigating Transdisciplinary Approaches for Community-engagement(Georgia Institute of Technology, 2023-11-16) Hamidi, FoadParticipatory design (PD) offers powerful and inclusive principles and methods for enabling mutual learning among diverse stakeholders and interested parties. As our society’s aspirations for computational systems and processes that respond to multifaceted needs and desires continue to grow, so does the need to investigate approaches that transcend disciplinarity to achieve broad societal goals. One such goal is to develop the public's capacity to engage creatively and critically with emerging technologies of interest. In this talk, I draw on several recent projects where I use community-based PD to investigate and interrogate emerging technologies, such as DIY assistive technologies and living media interfaces (LMIs), together with stakeholders. I describe how we develop and use prototypes, design activities, and art installations in these projects to generate conversation about technological and social possibilities, limitations, and implications.
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ItemAlgorithmic Scenario Generation As Quality Diversity Optimization(Georgia Institute of Technology, 2023-11-09) Nikolaidis, StefanosThe advent of state-of-the-art machine learning models and complex human-robot interaction systems has been accompanied by an increasing need for the efficient generation of diverse and challenging scenarios to test these systems. In this talk, I will formalize the problem of algorithmic scenario generation and propose a general framework for searching, generating, and evaluating simulated scenarios. I will first discuss our fundamental advances in quality diversity optimization algorithms that search the continuous, multi-dimensional scenario space. I will then show how integrating quality diversity algorithms with generative models allows for the generation of realistic scenarios. Instead of performing expensive evaluations for every single generated scenario in a robotic simulator, I will discuss combining the scenario search with the self-supervised learning of surrogate models that predict human-robot interaction outcomes. Finally, I will introduce the notion of 'soft archives' for registering the generated scenarios, which significantly improves performance in hard-to-optimize domains. While the talk will focus on scenario generation, the proposed framework is general and can be applied to a wide range of applications where diverse datasets are desirable. I will conclude the talk by discussing applications in searching for diverse faces and robot locomotion policies, and warehouse layouts.
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ItemMeasure and Manage Trust in Human-AI Conversations(Georgia Institute of Technology, 2023-10-12) Li, MengyaoArtificial Intelligence (AI), with its increasing capability and connectivity, extends beyond limited and well-defined contexts and is integrated into broader societal domains. AI algorithms now steer autonomous vehicle fleets, shape political beliefs through news filtering, and oversee resource allocation and labor. Establishing trust between humans and their AI counterparts becomes important to facilitate effective cooperation. Trust profoundly influences how individuals use, communicate with, and collaborate alongside AI systems. Thus, trust measurement and management within human-AI cooperation are indispensable for ensuring safety, efficiency, and overall success. This talk focuses on trust in human-AI interactions, addressing three primary questions: (1) How can we measure people’s trust in human-AI conversations? (2) How does trust change over time within human-AI conversations? (3) How can we effectively manage instances of overtrust or undertrust through conversational cues to enhance human-AI cooperation? This talk highlights critical advancements in measurement of trust dynamics in human-AI cooperation, promising to influence the future of AI integration into broader societal domains.
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ItemEdge and IoT-supported Augmented Reality: Promise, Challenges, and Solutions(Georgia Institute of Technology, 2023-09-28) Gorlatova, MariaMobile augmented reality (AR), which integrates virtual objects with 3D real environments in real time, has been showing outstanding potential in many application areas including education, retail, and healthcare. AR is broadly expected to redefine how we interact with the world around us. Yet current AR falls short of many of the expectations. This talk presents our vision of multi-device, edge computing-supported and Internet-of-Things (IoT)-integrated architectures for next-generation intelligent context-adaptive AR. The talk describes shortcomings in modern AR’s semantic and spatial awareness capabilities and identifies key research gaps that need to be addressed to enable AR to become robust and resource-efficient; we discuss solutions to some of the key challenges, based on the advances in edge computing, machine learning, and resource-efficient simultaneous localization and mapping. The talk also highlights the opportunities associated with the close integration of AR platforms and their users, and describes how integrated multi-device architectures can improve user context awareness in AR. The talk showcases several applications of next-generation context-aware AR, including AR in surgery and mental health. This talk is based on research that appeared in ACM SenSys, IEEE ISMAR, ACM IMWUT, IEEE INFOCOM, and IEEE/ACM IPSN.
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ItemDemocratizing Robot Learning and Teaming(Georgia Institute of Technology, 2023-09-14) Gombolay, MatthewNew advances in robotics and autonomy offer a promise of revitalizing final assembly manufacturing, assisting in personalized at-home healthcare, and even scaling the power of earth-bound scientists for robotic space exploration. Yet, in real-world applications, autonomy is often run in the O-F-F mode because researchers fail to understand the human in human-in-the-loop systems. In this talk, I will share exciting research we are conducting at the nexus of human factors engineering and cognitive robotics to inform the design of human-robot interaction. In my talk, I will focus on our recent work on 1) enabling machines to learn skills from and model heterogeneous, suboptimal human decision-makers, 2) “white-box” that knowledge through explainable Artificial Intelligence (XAI) techniques, and 3) scale to coordinated control of stochastic human-robot teams. The goal of this research is to inform the design of autonomous teammates so that users want to turn – and benefit from turning – to the O-N mode.
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ItemShape Machine: From software to practice(Georgia Institute of Technology, 2023-09-07) Economou, AthanassiosWhat would it mean if we could select any part (shape) of a CAD model and use it to find (⌘F) all its geometrical instances in the model (or other CAD models for that matter) – same size, larger, smaller, rotated, reflected or transformed in some way? What would it mean if we could edit this part and use it to replace (⌘R) all its geometrical instances in the model? Why is that the Find and Replace (⌘F/⌘R) operations that are so essential in Word or Excel have yet to be implemented in CAD? And what would happen if we could seamlessly use these shape-based Find and Replace (⌘F/⌘R) operations in a logical processing framework using states, loops, jumps and conditionals to literally write programming code by drawing shapes? How would this affect our current view of computation and what would it mean for design? The talk discusses the current state of the Shape Machine, a shape-rewrite computational system that features shape-based Find and Replace (⌘F/⌘R) operations for lines and arcs in 2D vector graphics and a logical processing framework including familiar control flow constructs (looping and branching), to allow write programming code by drawing shapes. Shape Machine is developed at the Shape Computation Lab at the Georgia Institute of Technology and currently is integrated within Rhinoceros, a NURBS 2D/3D CAD software. Several applications drawn from architectural design, industrial design, game design, circuit design, mathematics and other fields showcase the potential impact of this new technology in various domains.
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ItemConsidering People and Technology(Georgia Institute of Technology, 2023-08-24) Best, Michael L.
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ItemComputational Models of Human-Like Skill and Concept Formation(Georgia Institute of Technology, 2023-04-13) MacLellan, Christopher J.The AI community has made significant strides in developing artificial systems with human-level proficiency across various tasks. However, the learning processes in most systems differ vastly from human learning, often being substantially less efficient and flexible. For instance, training large language models demands massive amounts of data and power, and updating them with new information remains challenging. In contrast, humans employ highly efficient incremental learning processes to continually update their knowledge, enabling them to acquire new knowledge with minimal examples and without overwriting prior learning. In this talk, I will discuss some of the key learning capabilities humans exhibit and present three research vignettes from my lab that explore the development of computational systems with these capabilities. The first two vignettes explore computational models of skill learning from worked examples, correctness feedback, and verbal instruction. The third vignette investigates computational models of concept formation from natural language corpora. In conclusion, I will discuss future research directions and a broader vision for how cognitive science and cognitive systems research can lead to new AI advancements.
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ItemDeep Thinking about Deepfake Videos: Understanding and Bolstering Humans’ Ability to Detect Deepfakes(Georgia Institute of Technology, 2023-03-16) Tidler, Zachary“Deepfakes” are videos in which the (usually human) subject of a video has been digitally altered to appear to do or say something that they never actually did or said. Sometimes these manipulations produce innocuous novelties (e.g., testing what it would look like if Will Smith had been cast as “Neo” in the film The Matrix), but far more dangerous use cases have been observed (e.g., producing fake footage of Ukrainian President, Volodymyr Zelenskyy, in which he instructs Ukrainian military forces to surrender on the battlefield). Generating the knowledge and tools necessary to defend against potential harms these videos could impose is likely to rely on contributions from a broad coalition of disciplines, many of which are represented in the GVU. In this week’s Brown Bag presentation, we will offer some real-time demonstrations of deepfake technology and present findings from our work that has largely focused on investigating the psychological factors influencing deepfake detection.