Allen School researchers are at the forefront of exciting developments in AI spanning machine learning, computer vision, natural language processing, robotics and more.
We cultivate a deeper understanding of the science and potential impact of rapidly evolving technologies, such as large language models and generative AI, while developing practical tools for their ethical and responsible application in a variety of domains — from biomedical research and disaster response, to autonomous vehicles and urban planning.
Groups & Labs
Makeability Lab
The Makeability Lab specializes in Human-Computer Interaction and applied machine learning for high-impact problems in accessibility, computational urban science, and augmented reality.
Mobile Intelligence Lab
The interdisciplinary Mobile Intelligence Lab builds intelligent systems and tools for tackling hard technical and societal problems, including battery-free computing, medical diagnostics, augmented human perception and more.
Faculty Members
Centers & Initiatives
The NSF AI Institute for Agent-based Cyber Threat Intelligence and Operation (ACTION) seeks to change the way mission-critical systems are protected against sophisticated, ever-changing security threats. In cooperation with (and learning from) security operations experts, intelligent agents will use complex knowledge representation, logic reasoning, and learning to identify flaws, detect attacks, perform attribution, and respond to breaches in a timely and scalable fashion.
Computing for the Environment (CS4Env) at the University of Washington supports novel collaborations across the broad fields of environmental sciences and computer science & engineering. The initiative engages environmental scientists and engineers, computer scientists and engineers, and data scientists in using advanced technologies, methodologies and computing resources to accelerate research that addresses pressing societal challenges related to climate change, pollution, biodiversity and more.
Highlights
Allen School News
A team of Allen School and Ai2 researchers were recognized for developing an efficient, scalable system for indexing petabyte-level text corpora with minimal storage overhead to better understand the data on which large language models are trained.
Allen School News
Allen School researchers led the development of a benchmark dataset of 26,000 real-world, open-ended queries to evaluate the creative generation of large language models. They discovered major LLMs all generate similar outputs as if they’re part of an Artificial Hivemind.
Business Insider
Farhadi, who co-leads the Allen School’s Reasoning, AI, and VisioN (RAIVN) Lab and is also CEO of the Allen Institute for Artificial Intelligence (Ai2), was recognized for his leadership in open AI research and his influence on how institutions scale AI for the benefit of humanity.