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AI2 is back at CVPR in June 2019, this time to talk Common Sense AI—we will be  presenting two papers that successfully demonstrate important elements of machine common sense in a real-world simulation for the first time. AI2’s PRIOR team will discuss AI agents that can communicate and cooperate together visually, and an AI agent that can teach itself how to navigate around in a new environment.

Both of these advances were created with the powerful AI2-THOR framework, a unique way to study and develop common sense for AI that goes beyond the limitations of text or robotics. AI2-THOR is a platform for building near-photorealistic simulated environments with real-world physics, textures, and object interactions.

CVPR: Long Beach California June 16-20, 2019

AI2 will make a strong showing at CVPR with a total of eight accepted papers, four of which will have oral presentations. Learn more about some of our submissions in this special edition of the AI2 Newsletter.

Two images comparing navigation with communication and without communication
Two Body Problem: Collaborative Visual Task Completion
Unnat Jain*, Luca Weihs*, Eric Kolve, Mohammad Rastegari, Svetlana Lazebnik, A. Farhadi, Alexander G. Schwing, Aniruddha Kembhavi
[*equal contribution]

Collaboration is a necessary skill to perform tasks that are beyond a single agent’s capabilities. This paper argues that there are inherently visual aspects to collaboration that should be studied in visually rich environments. We investigate the problem of learning to collaborate directly from pixels using the AI2-THOR framework, and demonstrate the benefits of explicit and implicit modes of communication in performing visual tasks.

A two-part image. The image on the top shows a student learning but getting confused in the real world. The image on the bottom shows a student learning and succeeding in the real world
Learning to Learn How to Learn: Self-Adaptive Visual Navigation Using Meta-Learning
Mitchell Wortsman, Kiana Ehsani, Mohammad Rastegari, Ali Farhadi, Roozbeh Mottaghi

Learning is continuous; after humans learn a new task, we keep learning about it while we perform the task. Learning how to learn is a critical skill that enables us to generalize our knowledge to new problems. In this paper, we challenge an AI agent to learn self-supervised, effective navigation around a room. Our method demonstrates major improvements for visual navigation in novel scenes.

A very tiny diagram of a table with some check marks on it ELASTIC: Improving CNNs with Instance Specific Scaling Policies
Huiyu Wang, Aniruddha Kembhavi, Ali Farhadi, Alan Loddon Yuille, Mohammad Rastegari

ELASTIC is a simple, efficient, and very effective approach to learn the scale variations (relative sizes) of objects in a given image purely from data. We demonstrate consistent improvement over the state-of-the-art-performance on multiple well-known image classification and segmentation challenges, without extra (sometimes with even lower) computational effort required.

A picture of three people eating with facial recognition boxes around the people From Recognition to Cognition: Visual Commonsense Reasoning
Rowan Zellers, Yonatan Bisk, Ali Farhadi, Yejin Choi

Visual Commonsense Reasoning (VCR) is a compelling new challenge for cognition-level visual understanding in AI. Good performance on the VCR dataset will require a model to answer challenging visual questions using natural language, as well as to provide a rationale explaining why its answer is true.
See all of AI2's recent publications on our website →

Featured AI2er Joanna Power talks about her career path, what's exciting at AI2, advice for aspiring engineers, and the underrated places in Seattle.

Three ways to build a strong AI-training pipeline : How common is it for AI researchers to leave academia for industry? What should universities and policymakers do? AI2 CEO Oren Etzioni talks to Nature about keeping the academic AI programs in the US healthy.
SAGE Ocean catches up with the team from AI2 who recently won the NYU Coleridge Initiative's Rich Context Competition.
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