Explore our curated list of resources to enhance your knowledge and skills in AI and related technologies.
RECOMMENDED ARTICLES
Continuous Transformation: Transformation in Contact
by GEN James E, Rainey (Ret), U.S. Army
Continuous Transformation: Deliberate Transformation
by GEN James E, Rainey (Ret), U.S. Army
Continuous Transformation: Concept-Driven Transformation
by GEN James E, Rainey (Ret), U.S. Army
Open Source Al Can Help America Lead in AI and Strengthen Global Security
by Nick Clegg, President of Global Affairs (Meta)
MusicLM: Generating Music From Text
by Andrea Agostinelli, Timo I. Denk, Zalan Borsos, Jesse Engel, Mauro Verzetti, Antoine Caillon, Oingqing Huang, Aren Jansen, Adam Roberts,
Marco Tagliasaochi, Matt Sharifi, Neil Zeghidour, Christian Frank
AI Horizons and Beyond: CMU Welcomes Revolution in Human-First Al to Pittsburgh
by Michael Henninger
ARTICLES PUBLISHED BY AI2C SCHOLARS
MLTEing Models: Negotiating, Evaluating and Documenting Model and System Qualities
by Katherine R. Maffey, Kyle Dotterrer, Jennifer Niemann, Iain Cruickshank, Grace Lewis, and
Christian Kastner
A least squares approach for determining the coefficients of the CVBEM approximation function
By Bryce D. Wilkins and Theodore V Hromadka
The Center of Gravity in Artificial Intelligence Ethics Is the Dataset
By Timothy J. Naudet and Robert B Skinker
Black Swan Discovery in Live IoT Data Streams
By Eric Sturzinger, Jan Harkes, Gil Goldman, and Mahadev Satyanarayanan
Data-Driven Risk Management in USACE Construction Contracts
By Robert B Skinker and Timothy J. Naudet
Infantry Doctrine Proposal: UAS Sections that Mirror Mortar Sections
By Timothy J. Naudet, Ian Baird, Nathan Rosenberger, Thomas Canchola, and Avery Austin
Model and Program Repair via Group Actions and Structure Unwinding
By Paul C. Attie and William L. Cocke
Enumerating Word Maps in Finite goups
By Bogdan S. Chlebus, William L. Cocke, and Meng-Che Ho
The Amit-Ashurst Conjecture for Finite Metacyclic p-groups
By Rachel D. Camina, William L. Cocke, and Anitha Thillaisundaram
Current Research
Seed Project: Drone Dogfighting
- Description: The Russo-Ukrainian and Gaza Wars have highlighted the utility of low-cost drones for reconnaissance and targeted strike. Future wars will likely be fought with AI and swarm-on-swarm combat using thousands of drones. Traditional robotics methods do not scale to large numbers of robots so we propose a seed-research program to develop learning-based methods to control swarms and enable emergent behaviors for swarm-on-swarm engagements.
- For questions email: usarmy.pittsburgh-pa.afc-ai2c.mesg.research-engagements@army.mil
- POC: Wennie Tabib – The Robotics Institute – CMU
Seed Project: Cost-Efficient LLM Routing
- Description: State-of-the-art large language models (LLMs) have billions of parameters. Procession all inference queries via the largest and the most general model is infeasible due to cost and scalability constraints. Thus, there is a growing trend of deploying a diverse pool of task-specific models with different sizes and accuracy. The goal of this project is to design routing strategies that can seamlessly execute inference queries using a multitude of smaller and specialized models.
- For questions email: usarmy.pittsburgh-pa.afc-ai2c.mesg.research-engagements@army.mil
- POC: Gauri Joshi – College of Engineering at Carnegie Mellon University