IN PICs | 10 Artificial Intelligence Terminologies And Buzzwords You Should Know

Artificial Intelligence (AI), even with all its flaws and regulatory worries has become a hot topic. After ChatGPT and Brad came along its presence is undeniable. Here's a handy glossary of AI terms.

IN PICs | 10 Artificial Intelligence Terminologies And Buzzwords You Should Know

Deep Learning: Deep learning is a subset of machine learning that trains neural networks to recognize patterns and make decisions. It revolutionizes AI by improving computer vision, natural language processing, and speech recognition. It enables machines to understand complex information and enhances accuracy in various applications. (Image: Getty)

Automation: AI automation refers to using AI technologies to automate tasks traditionally performed by humans. It utilises algorithms, machine learning, robotics, and cognitive computing to streamline operations, improve efficiency, and reduce human error. Examples include chatbots, robotic process automation, and autonomous vehicles. (Image: Getty)

Generative AI: Generative AI is a subset of AI that creates new content through algorithmic models. It uses deep learning techniques like GANs and VAEs to learn patterns from training data and generate unique outputs. It has applications in art, entertainment, and design, enabling the synthesis of images, text, music, and simulations. (Image: Getty)

Reinforcement learning: Reinforcement learning is a branch of machine learning that trains agents to make decisions through trial-and-error interactions with an environment. Agents learn by receiving rewards or penalties for their actions and aim to maximize cumulative rewards. It is used in game playing, robotics, and autonomous control systems. (Image: Getty)

Machine learning: Machine learning is a branch of AI that enables computer systems to learn from data and make predictions or decisions without explicit programming. It involves training models with data to recognise patterns and extract insights. It uses labeled data, unlabeled data, and reinforcement learning learns through trial and error. (Image: Getty)

Interpretability: Interpretability in AI refers to the ability to understand and explain how an AI model or system makes decisions. It is crucial for building trust and comprehension, especially in critical domains. Techniques like feature importance analysis and model visualization enhance interpretability. (Image: Getty)

Long Short-Term Memory: LSTM is a type of recurrent neural network (RNN) architecture that addresses the limitations of traditional RNNs in capturing long-term dependencies in sequential data. LSTMs excel at capturing long-term dependencies and are used in tasks like language translation, speech recognition, and time series prediction. (Image: Getty)

Optimisation in AI involves finding the best solution or configuration for a given problem. It aims to minimise or maximise objective functions by adjusting parameters or variables. Common optimisation algorithms include gradient descent, genetic algorithms, and simulated annealing. (Image: Getty)

Quantum computing: Quantum computing explores the use of quantum mechanical phenomena to perform computations. It uses qubits that can exist in multiple states simultaneously. Quantum computers have the potential to solve complex problems faster than classical computers. (Image: Getty)

Convolutional Neural Networks: CNNs are deep learning architectures designed for processing grid-like data, such as images or videos. They capture spatial patterns using convolutional layers and extract relevant features through filters or kernels. Their hierarchical structure and weight sharing enable efficient processing of large-scale visual data. (Image: Getty)