Evolutionary Algorithm
An optimization technique inspired by biological evolution that evolves a population of candidate solutions through selection, mutation, and crossover to find optimal or near-optimal solutions.
Evolutionary algorithms include genetic algorithms, genetic programming, and evolution strategies. They are used for neural architecture search (discovering optimal network designs), hyperparameter optimization, and robotics. Google used evolutionary methods to discover the AmoebaNet architecture, which matched human-designed networks. OpenAI's evolution strategies have been applied to reinforcement learning. These methods are especially valuable when the search space is too complex for gradient-based optimization.
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Related Terms
Artificial General Intelligence (AGI)
A hypothetical form of AI that can understand, learn, and apply knowledge across any intellectual task at or above human level, rather than being specialized for specific tasks.
AI Alignment
The research field focused on ensuring AI systems behave in accordance with human values and intentions, particularly as systems become more capable.
Fine-Tuning
The process of further training a pre-trained AI model on a specific, smaller dataset to specialize it for a particular task or domain, requiring far less compute than training from scratch.
Foundation Model
A large AI model trained on broad data that can be adapted to a wide range of downstream tasks — examples include GPT-4, Claude, Gemini, and Llama.
Machine Learning
A subset of AI where systems learn patterns from data rather than being explicitly programmed, improving their performance on tasks through experience without human-written rules.
Model Training
The computationally intensive process of teaching an AI model by feeding it data and adjusting its parameters to minimize errors, often requiring thousands of GPUs running for weeks or months.
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