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Decoding AI: Key Terms for Navigating the World of Artificial Intelligence

Machine Learning Neural Network Deep Learning Natural Language Processing Reinforcement Learning Generative AI DALL·E Artificial General Intelligence Chatbot


In the realm of Artificial Intelligence (AI), certain terms are fundamental. AI itself is the simulation of human intelligence by machines. Machine Learning, a key subset, allows computers to learn and act on data. Systems like Generative AI can create content, while Artificial General Intelligence (AGI) mimics human intellectual abilities. OpenAI's Dall-E crafts images from textual cues, and Neural Networks, modeled after the human brain, recognize patterns. Chatbots simulate human conversations, and Large Language Models (LLM) process vast text amounts. With methodologies from Supervised to Reinforcement Learning, AI's lexicon is extensive, reflecting its dynamic nature.

Here's a list of 50 essential words and phrases related to Artificial Intelligence (AI):

  • Artificial Intelligence (AI): The capability of a machine to imitate human intelligence.
  • Machine Learning: A subset of AI where algorithms allow computers to learn from and act on data.
  • Generative AI: AI systems that can create content, such as images, music, or text.
  • Artificial General Intelligence (AGI): A type of AI that possesses the ability to understand, learn, and perform any intellectual task that a human can.
  • Dall-E: An AI system by OpenAI that can generate original images from textual descriptions.
  • Neural Networks: Computational models inspired by the human brain, used for pattern recognition and learning.
  • Chatbot: A software application designed to simulate human conversation.
  • Copilot: AI-powered code suggestions tool.
  • Hallucination: In AI, when a model generates something that isn’t accurate or doesn’t exist in the real world.
  • Large Language Model (LLM): A model trained on vast amounts of text data to understand and generate human-like text.
  • OpenAI: An organization focused on developing friendly AI for the benefit of humanity.
  • Prompt engineering: Techniques used to better instruct AI models to generate desired outputs.
  • Turing Test: A measure of a machine's ability to exhibit intelligent behavior indistinguishable from that of a human.
  • Unsupervised Learning: A machine learning approach where the model learns from data without explicit labels.
  • Supervised Learning: Learning where the model is trained on labeled data.
  • Deep Learning: A subset of machine learning using neural networks with many layers.
  • Reinforcement Learning: Learning by interacting with an environment and receiving feedback in the form of rewards or penalties.
  • Algorithms: Step-by-step procedures or formulas for solving problems.
  • Training Data: The data on which an AI model is trained.
  • Testing Data: Data used to evaluate the accuracy and effectiveness of an AI model.
  • Bias in AI: When AI models show unfair prejudice towards certain groups or outcomes.
  • Natural Language Processing (NLP): The branch of AI that focuses on the interaction between computers and humans through language.
  • Convolutional Neural Networks (CNN): A type of deep learning model primarily used in image processing.
  • Recurrent Neural Networks (RNN): Neural networks that work with sequential data, often used for time series or language.
  • Transfer Learning: Using pre-trained models on a new, but related task.
  • Backpropagation: An optimization algorithm used for minimizing the error in neural networks.
  • Activation Function: A function in a neural network that introduces non-linearity.
  • Epoch: One complete forward and backward pass of all the training examples in a dataset.
  • Overfitting: When a model learns the training data too well, including its noise and outliers, making it perform poorly on new data.
  • Underfitting: When a model is too simple to capture underlying patterns in the data.
  • Gradient Descent: An optimization algorithm used to minimize the error in learning algorithms.
  • Data Augmentation: Techniques used to increase the amount of training data by altering the existing data.
  • Feature Extraction: Identifying and using specific attributes from raw data for machine learning.
  • Hyperparameters: Parameters in a model that are set before training, rather than learned during training.
  • Inference: Using a trained model to make predictions on new data.
  • Loss Function: A mathematical function that measures the difference between the predicted and true values.
  • Regularization: Techniques used to prevent overfitting in a machine learning model.
  • Weights: Values in a neural network that are learned from training data.
  • Attention Mechanism: A component in deep learning models, especially in NLP, that allows the model to focus on specific parts of the input.
  • GAN (Generative Adversarial Network): A set of neural networks, one generating data and another evaluating it, often used in generative tasks.
  • Fine-tuning: Adjusting a pre-trained model slightly to make it suitable for a new, similar task.
  • Model Architecture: The layout or structure of a machine learning model.
  • Embeddings: The representation of data, like words or items, as vectors in a high-dimensional space.
  • Batch: A set of training examples used in one iteration of model training.
  • Classifier: An algorithm that determines the category of input data.
  • Dataset: A collection of data used in machine learning.
  • Linear Regression: A method used to model and analyze relationships between variables.
  • Optimization: The process of adjusting a model to improve its performance.
  • Perceptron: The simplest form of a neural network, typically used for binary classification.
  • Semantic Analysis: The process of understanding the meaning behind words and sentences in language processing.