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From California to Alaska: Teaching AI to Adapt - Mansi Maheshwari

University of Massachusetts Amherst
2025
artificial intelligence
machine learning
Continual Learning
Autonomous Vehicles
Self-Driving Cars
AI Safety
neural networks
Knowledge Compression
Lifelong Learning
deep learning
Adaptive AI
reinforcement learning
Catastrophic Forgetting
Intelligent Systems
robotics
AI reliability
autonomous systems
Human Learning
simulation
Computer Science
AI Research
Cognitive Computing
Transportation Technology
Safety Engineering
Neural Compression
Learning Algorithms
adaptive systems
Future Technology
Smart Vehicles
Computational Intelligence

This research explores how artificial intelligence systems can continue learning without forgetting previously acquired knowledge. Instead of erasing old information, the proposed method compresses knowledge into more efficient representations, allowing AI systems such as self-driving cars to adapt safely to new environments while avoiding dangerous performance failures during learning.

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