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Goel,
Ashok K.
Goel,
Ashok K.
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ItemNational AI Institute for Adult Learning and Online Education: Vision, Goals and Plans( 2022-03-10) Goel, Ashok K.NSF has recently established a National AI Institute for Adult Learning and Online Education (AI-ALOE) headquartered at Georgia Tech. The AI-ALOE Institute seeks to address the societal challenges of lifelong learning, workforce development, and reskilling and upskilling of millions of American workers. Online education offers an affordable medium for taking education to workers where they live. AI-ALOE will develop new AI technologies that enhance cognitive engagement, teacher presence, social interaction, and self-directed learning in online education, and thereby improve its quality for adult learners in STEM disciplines. In addition to these use-inspired AI techniques, AI-ALOE will conduct foundational AI research on personalization of learning at scale, interactive machine teaching, mutual theory of mind, and participatory design of sociotechnical systems for responsible AI. I will describe AI-ALOE’s vision, goals and plans, using examples from my research laboratory for illustration.
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ItemThinking in Pictures as a Cognitive Account of Autism(Georgia Institute of Technology, 2010) Kunda, Maithilee ; Goel, Ashok K.We analyze the hypothesis that some individuals on the autism spectrum may use visual mental representations and processes to perform certain tasks that typically developing individuals perform verbally. We present a framework for interpreting empirical evidence related to this “Thinking in Pictures” hypothesis and then provide comprehensive reviews of data from several different cognitive tasks, including the /n/-back task, serial recall, dual task studies, Raven’s Progressive Matrices, semantic processing, false belief tasks, visual search and attention, spatial recall, and visual recall. We also discuss the relationships between the Thinking in Pictures hypothesis and other cognitive theories of autism including Mindblindness, Executive Dysfunction, Weak Central Coherence, and Enhanced Perceptual Functioning.
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ItemInteractive Story Authoring for Knowledge‐Based Support for Investigative Analysis: A Second STAB at Making Sense of VAST Data(Georgia Institute of Technology, 2010) Goel, Ashok K. ; Sinharoy, Avik ; Adams, Summer ; Dokania, AdityThe sensemaking task in investigative analysis generates stories that connect entities and events in an input stream of data. The Stab system represents crime stories as hierarchical scripts with goals and states. It generates multiple stories as explanatory hypotheses for an input data stream containing interleaved sequences of events, recognizes intent in a specific event sequence, and calculates confidence values for the generated hypotheses. In this report, we describe Stab2, a new interactive version of the knowledge‐based Stab system. Stab2 contains a story editor that enables users to enter and edit crime stories. We illustrate Stab2 with examples from the IEEE VAST contest datasets.
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ItemPlaying Detective: Using AI for Sensemaking in Investigative Analysis(Georgia Institute of Technology, 2009) Goel, Ashok K. ; Adams, Summer ; Cutshaw, Neil ; Sugandh, NehaThe sensemaking task in investigative analysis generates models that connect entities and events in an input stream of data. We describe two knowledge systems for aiding sensemaking in investigative analysis. The Spade system uses crime schemas to generate an explanatory hypothesis and past cases to validate the hypothesis. The STAB system represents crime schemas as hierarchical scripts with goals and states. It generates multiple explanatory hypotheses for an input data stream containing interleaved sequences of events, recognizes intent in a specific event sequence, and calculates confidence values for the generated hypotheses. We view STAB and Spade as automated cognitive assistants to human analysts: they may support sensemaking in investigative analysis by generating and managing multiple competing hypotheses.
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ItemBiologically-Inspired Innovation in Engineering Design: a Cognitive Study(Georgia Institute of Technology, 2007) Vattam, Swaroop ; Helms, Michael E. ; Goel, Ashok K.Biologically-inspired design uses analogous biological phenomena to develop solutions for engineering problems. Understanding, learning and practicing this approach to design is challenging because biologists and engineers speak different languages, have different perspectives on design, with different constraints on design problems and different resources for realizing an abstract design. In Fall 2006, we attended ME/ISyE/MSE/PTFe/BIOL 4803: Biologically-Inspired Design, an interdisciplinary introductory course for juniors and seniors offered at Georgia Tech. We collected course materials, took class notes, observed teacher-student and student-student interactions in the classroom. We also observed some sessions of a few interdisciplinary teams of students engaged in their design projects outside the classroom. We then analyzed the observations in terms of existing cognitive theories of design, modeling, and analogy. The goals of this cognitive study were to (1) understand the cognitive basis of biologically-inspired innovation in engineering design, (2) identify opportunities for enabling more effective learning of biologically-inspired design, and (3) examine the implications for developing computational tools for facilitating effective biologically-inspired design. This report summarizes our main observations about learning biologically-inspired design, and presents our preliminary analysis of biologically-inspired design in a classroom setting.