In reinforcement learning, AI systems learn by interacting with their environment and receiving feedback in the form of rewards or penalties. The AI system aims to maximise the cumulative reward over time by choosing the optimal sequence of actions.
As the Azerbaijan Formula One race approaches, it's clear that AI has become an integral part of the sport. By harnessing the power of data, AI is transforming racing, helping teams optimise performance, develop winning strategies, and ensure driver safety.
Machine learning (ML) is a specific method within the broader field of artificial intelligence (AI). Deciding whether to use AI or ML depends on the problem, data availability, and adaptability required. Recognising the differences helps unlock their full potential to drive innovation.
Natural Language Processing (NLP) is a subfield of AI that focuses on understanding and generating human language. It encompasses tasks like text analysis, machine translation, and sentiment analysis, transforming how we interact with technology and opening up new possibilities.
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.
In the world of artificial intelligence (AI), algorithms are the backbone that drives the learning and decision-making capabilities of AI models. Broadly, these algorithms fall into two categories: Traditional Machine Learning Algorithms and Deep Learning Algorithms.
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.
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
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.