1. Core AI Concepts
- Artificial Intelligence (AI) AI refers to systems or machines that mimic cognitive functions like learning, reasoning, and problem-solving to perform tasks that typically require human intelligence.
- Machine Learning (ML) A subset of AI where algorithms improve their performance on a task by learning patterns from data rather than following explicitly programmed rules.
- Neural Network A computational system inspired by the human brain, consisting of layers of interconnected nodes (neurons) that process data to recognize patterns and make predictions.
- Deep Learning A branch of machine learning that uses multiple layers of neural networks to automatically learn and extract complex patterns from large datasets.
- Algorithm A step-by-step set of rules or instructions that a computer follows to perform a specific task or solve a problem.
- Training Data The dataset used to teach a machine learning model by providing examples of input-output relationships.
- Supervised Learning A machine learning approach where the model learns from labeled data, meaning each input has a corresponding correct output.
- Unsupervised Learning A machine learning technique where the model finds patterns and structures in unlabeled data without predefined outputs.
- Reinforcement Learning (RL) A learning method where an AI agent interacts with an environment and improves its actions based on rewards and punishments.
- Natural Language Processing (NLP) The field of AI that enables machines to understand, interpret, generate, and manipulate human language.
2. Key Techniques & Tools
- Bias Systematic errors or unfair outcomes in AI models caused by imbalanced training data, flawed algorithms, or human prejudices.
- Overfitting When a model learns too much detail from the training data, making it perform well on that data but poorly on unseen data.
- Underfitting When a model is too simple to capture patterns in the data, leading to poor performance on both training and test data.
- Feature Engineering The process of selecting, transforming, or creating relevant input variables (features) to improve a model’s accuracy and performance.
- Hyperparameter Tuning The process of optimizing the adjustable settings of a machine learning model to improve its performance.
- Generative AI AI systems, such as GPT and Stable Diffusion, that generate new content, including text, images, music, and videos, based on patterns learned from data.
- Chatbot A software program that interacts with users using predefined rules or AI-powered NLP techniques, commonly used in customer support and virtual assistants.
- Computer Vision The field of AI focused on enabling machines to interpret, analyze, and understand visual data from images or videos.
- Convolutional Neural Network (CNN) A type of deep learning model designed for image processing tasks, using specialized layers to detect visual features.
- Recurrent Neural Network (RNN) A type of neural network designed for sequential data, such as speech or text, with connections that allow it to retain memory of past inputs.
3. Data & Model Fundamentals
- Dataset A structured collection of data used for training, validating, and testing AI models.
- Label The correct output or target value assigned to data in supervised learning.
- Feature An individual measurable property or characteristic of a dataset that serves as an input to a machine learning model.
- Validation Set A subset of data used to tune a model’s parameters and prevent overfitting during training.
- Test Set A separate dataset used to evaluate a trained model’s final performance.
- Loss Function A mathematical function that quantifies the difference between a model’s predictions and the actual values.
- Gradient Descent An optimization algorithm that updates model parameters iteratively to minimize the loss function.
- Backpropagation The process of calculating and distributing gradients in a neural network to adjust weights and improve learning.
- Activation Function A mathematical function applied to neurons in a neural network to introduce non-linearity, allowing the model to learn complex patterns.
- Dropout A regularization technique that randomly disables neurons during training to prevent overfitting.
4. Applications & Use Cases
- Autonomous Vehicles Self-driving cars and other AI-powered systems that use sensors, computer vision, and deep learning to navigate environments.
- Predictive Analytics Using AI to analyze historical data and make forecasts about future trends or behaviors.
- Recommendation System An AI-driven system that suggests products, movies, or content based on user preferences and past interactions.
- Sentiment Analysis The use of NLP to determine emotions or opinions expressed in text.
- Object Detection A computer vision technique that identifies and locates objects within an image or video.
- Speech Recognition The ability of AI to convert spoken language into text.
- Transfer Learning A technique where a pre-trained model is adapted for a new, related task, improving efficiency and accuracy.
- Edge AI Running AI models directly on edge devices (e.g., smartphones, IoT devices) instead of relying on cloud-based processing.
- Explainable AI (XAI) AI systems designed to provide transparent explanations for their decisions and predictions.
- AI Ethics The study of the moral and societal implications of AI development, including bias, privacy, and accountability.
5. Emerging Trends & Advanced Topics
- Generative Adversarial Networks (GANs) A deep learning framework where two neural networks (a generator and a discriminator) compete to create highly realistic synthetic data.
- Transformer Models A deep learning architecture that processes sequential data efficiently using self-attention mechanisms, powering models like GPT and BERT.
- Large Language Models (LLMs) AI models trained on vast amounts of text data to generate coherent and context-aware responses, such as GPT-4 and Claude.
- Reinforcement Learning with Human Feedback (RLHF) A method of improving AI models by training them with direct human feedback to align better with user expectations.
- Federated Learning A decentralized learning approach where models are trained across multiple devices while keeping data private.
- Quantum Machine Learning The integration of quantum computing principles with machine learning to accelerate problem-solving in complex domains.
- Synthetic Data Artificially generated data used for AI training when real-world data is scarce, expensive, or sensitive.
- Multimodal AI AI systems that can process and combine multiple types of data, such as text, images, and audio, for richer interactions.
- AI-Powered Automation The use of AI to automate repetitive or complex tasks, improving efficiency and reducing human workload.
- Singularity A hypothetical future point where AI surpasses human intelligence, potentially leading to rapid, uncontrollable technological growth.
Why Sharing This Post Helps Others
If you’ve found this glossary helpful, chances are others will too. By sharing this post, you’re helping fellow learners overcome the intimidation of AI jargon and take confident steps toward mastering the field.
What’s Next?
Ready to go beyond the basics? Check out my affordable AI course and you can earn a 50% affiliate commission by referring the course to others.
Start your journey today—and don’t forget to save this glossary for future reference!
P.S. Which AI term surprised you the most? Let me know in the comments—I’d love to hear your thoughts!
Feel free to share this post with anyone who’s curious about AI. Together, let’s demystify the language of the future!
AI Course | Bundle Offer (including AI/RAG ebook) | AI coaching
No comments:
Post a Comment