Embodied Question Answering

We present a new AI task -- Embodied Question Answering (EmbodiedQA) -- where an agent is spawned at a random location in a 3D environment and asked a question ("What color is the car?"). In order to answer, the agent must first intelligently navigate to explore the environment, gather information through first-person (egocentric) vision, and then answer the question ("orange"). This challenging task requires a range of AI skills -- active perception, language understanding, goal-driven navigation, commonsense reasoning, and grounding of language into actions. In this work, we develop the environments, end-to-end-trained reinforcement learning agents, and evaluation protocols for EmbodiedQA.Comment: 20 pages, 13 figures, Webpage: https://embodiedqa.org

Evaluating Visual Conversational Agents via Cooperative Human-AI Games

As AI continues to advance, human-AI teams are inevitable. However, progress in AI is routinely measured in isolation, without a human in the loop. It is crucial to benchmark progress in AI, not just in isolation, but also in terms of how it translates to helping humans perform certain tasks, i.e., the performance of human-AI teams. In this work, we design a cooperative game - GuessWhich - to measure human-AI team performance in the specific context of the AI being a visual conversational agent. GuessWhich involves live interaction between the human and the AI. The AI, which we call ALICE, is provided an image which is unseen by the human. Following a brief description of the image, the human questions ALICE about this secret image to identify it from a fixed pool of images. We measure performance of the human-ALICE team by the number of guesses it takes the human to correctly identify the secret image after a fixed number of dialog rounds with ALICE. We compare performance of the human-ALICE teams for two versions of ALICE. Our human studies suggest a counterintuitive trend - that while AI literature shows that one version outperforms the other when paired with an AI questioner bot, we find that this improvement in AI-AI performance does not translate to improved human-AI performance. This suggests a mismatch between benchmarking of AI in isolation and in the context of human-AI teams.Comment: HCOMP 201

PAR1 Agonists Stimulate APC-Like Endothelial Cytoprotection and Confer Resistance to Thromboinflammatory Injury

Stimulation of protease-activated receptor 1 (PAR1) on endothelium by activated protein C (APC) is protective in several animal models of disease, and APC has been used clinically in severe sepsis and wound healing. Clinical use of APC, however, is limited by its immunogenicity and its anticoagulant activity. We show that a class of small molecules termed “parmodulins” that act at the cytosolic face of PAR1 stimulates APC-like cytoprotective signaling in endothelium. Parmodulins block thrombin generation in response to inflammatory mediators and inhibit platelet accumulation on endothelium cultured under flow. Evaluation of the antithrombotic mechanism showed that parmodulins induce cytoprotective signaling through Gβγ, activating a PI3K/Akt pathway and eliciting a genetic program that includes suppression of NF-κB–mediated transcriptional activation and up-regulation of select cytoprotective transcripts. STC1 is among the up-regulated transcripts, and knockdown of stanniocalin-1 blocks the protective effects of both parmodulins and APC. Induction of this signaling pathway in vivo protects against thromboinflammatory injury in blood vessels. Small-molecule activation of endothelial cytoprotection through PAR1 represents an approach for treatment of thromboinflammatory disease and provides proof-of-principle for the strategy of targeting the cytoplasmic surface of GPCRs to achieve pathway selective signaling