Learn Before
Evidence Before Action — AI Tutor Demo @ Honor
Learners will gain the practical skills needed to rigorously audit AI-generated research claims, identifying critical flaws such as sampling bias, unsupported inferences, and omitted datasets. You will master techniques for issuing bounded delegation instructions that guide AI agents to produce structured, evidence-grounded revisions within safe organizational constraints. By the end of this course, you will be equipped to design defensible, reversible pilot recommendations that mitigate risk and align with key decision-makers.
0
1
Contributors are:
Tags
Prep Sessions
Related
Transformer Architecture and Large Language Model Capabilities @ University of Michigan - Ann Arbor
Foundational Deep Learning Architectures: Transformers and Residual Networks @ University of Michigan - Ann Arbor
Edge-Native Mixture-of-Experts Serving with FreeToken @ University of Michigan - Ann Arbor
The Architecture of Connected Systems: Tracing End-to-End Data Flow @ University of Michigan - Ann Arbor
Overcoming Neural Network Degradation Through Residual Learning @ University of Michigan - Ann Arbor
Frontier Model Dynamics: Scaling Laws, Calibration, and Post-Training Alignment @ University of Michigan - Ann Arbor
Frontier Foundation Models, Capability Evaluation, and Just-In-Time Agent Harnesses @ University of Michigan - Ann Arbor
Engineering State-Bound Execution Runtimes for Autonomous Agents @ University of Michigan - Ann Arbor
The Aqueous Continuum: Biomolecular Solvation and Planetary Dynamics @ Honor
Network Architecture and Packet Flow: Inside Modern Communication Systems @ University of Michigan - Ann Arbor
Long-Horizon Agent Reliability: Stateful Scaffolding and Runtime Verification @ University of Michigan - Ann Arbor
Evidence Before Action — AI Tutor Demo @ Honor