Learning Strategies or Paradigms refer to the higher-level strategies that an AI model can use to enhance its learning process, often regardless of the learning type being used. This category includes: Transfer Learning, Multi-task Learning, Active Learning and Semi-Supervised Learning.
Hyperparameter tuning optimises AI model performance by finding the best parameter settings. It improves accuracy, saves resources, and is crucial for AI development. It also prevents overfitting, where a model learns to perform exceptionally well on the training data but fails with new unseen data
Replay memory is a machine learning technique that stores and reuses past experiences to enhance an agent's decision-making. Common in reinforcement learning, it helps agents learn from diverse situations, improving performance in applications like game AI, robotics, and autonomous vehicles.
Multi-agent systems (MAS) embody an innovative approach in artificial intelligence, where multiple autonomous agents collaborate and interact to address complex problems, showcasing their adaptability and efficiency across a wide range of applications, such as robotics, smart grids, and e-commerce.
As we continue to develop and integrate AI into various aspects of our lives, it's crucial to recognize the distinctions between the two primary forms of intelligence: Artificial General Intelligence (AGI) and Narrow AI.
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