To better understand artificial intelligence and the new generation of AI-powered chatbots like ChatGPT, Bing, and Bard, it’s helpful to become familiar with specific technical terms and concepts. We have compiled a glossary of such words for your convenience, but please note that this is just a basic overview, and more in-depth information is available elsewhere.
Chatbots are helpful for clarification and learning about AI, but they may occasionally provide incorrect information. Verifying any information received from chatbots before accepting it as accurate is essential.
Here are some terms to get you started:
- Artificial Intelligence (AI): The field of computer science that focuses on creating machines that can perform tasks that would typically require human intelligence, such as visual perception, speech recognition, decision-making, and natural language understanding.
- Large Language Models (LLM): Large language models are computer models designed to process and understand natural language text at a large scale. They are typically built using deep learning techniques, such as neural networks, and are trained on vast amounts of text data.
- Machine Learning: A subset of AI that involves training algorithms to learn from data and make predictions or decisions without being explicitly programmed. Machine learning is used in various applications, such as recommendation systems, fraud detection, and autonomous vehicles.
- Natural Language Processing (NLP): A subfield of AI that focuses on enabling machines to understand and generate human language. NLP has been used for language translation, sentiment analysis, and chatbot development tasks.
- Deep Learning: A subfield of machine learning that involves training artificial neural networks with large amounts of data. Deep learning has been used to achieve state-of-the-art results in various AI tasks, such as image recognition, natural language processing, and speech recognition.
- Neural Network: A type of artificial neural network inspired by the structure and function of biological neurons. Neural networks consist of interconnected nodes, or “neurons,” that process information by receiving input signals, performing calculations, and generating output signals.
- Reinforcement Learning: A type of machine learning that involves training an agent to interact with an environment and learn from its actions through feedback in the form of rewards or penalties. Reinforcement learning has trained AI agents to play games, control robots, and make decisions in complex environments.
- Supervised Learning: A type of machine learning that involves training a model to make predictions or decisions based on labeled data, where each data point is associated with a target output. Supervised learning has been used for various AI applications, such as image classification, speech recognition, and natural language processing.
- Unsupervised Learning: A type of machine learning that involves training a model to identify patterns or structures in unlabeled data without explicit guidance or supervision. Unsupervised learning has been used for clustering, dimensionality reduction, and anomaly detection tasks.
- Overfitting: A common problem in machine learning where a model is trained too well on the training data and becomes too specific, resulting in poor performance on new, unseen data. Overfitting can be addressed by techniques such as regularization, early stopping, and cross-validation.
- Underfitting: A common problem in machine learning is where a model needs to be more complex and capture the underlying patterns in the data. Underfitting can be addressed by using more complex models or by increasing the amount of training data.
- Data Augmentation: A technique used in machine learning to increase the amount of training data by generating additional data from existing data. Data augmentation can improve the performance and robustness of machine learning models, particularly in cases where the amount of available data is limited.
- Convolutional Neural Network (CNN): A type of neural network commonly used for image recognition and processing tasks. CNNs use a process called convolution to extract features from images, which are then used to make predictions or classifications.
- Recurrent Neural Network (RNN): A type of neural network commonly used for sequential data processing tasks, such as natural language processing and speech recognition. RNNs are designed to process input data with temporal dependencies and can remember information from previous time steps.
- Generative Adversarial Networks (GANs): A type of neural network consisting of two parts – a generator and a discriminator – that work together to generate realistic data samples, such as images, audio, or text.
- Transfer Learning: A technique in machine learning that involves using pre-trained models to improve the performance of a new model. Transfer learning can save time and resources by leveraging the knowledge and expertise gained from previous tasks.
- Computer Vision: A field of AI that focuses on enabling machines to interpret and understand visual information from the world around them. Computer vision is used in various applications, such as autonomous vehicles, surveillance systems, and medical image analysis.
- Natural Language Generation (NLG): A subfield of NLP that focuses on enabling machines to generate human-like language, such as written or spoken text. NLG is used in various applications, such as chatbots, content creation, and language translation.
- Speech Recognition: A subfield of NLP that focuses on enabling machines to interpret and understand human speech. Speech recognition is used in various applications, such as virtual assistants, transcription services, and automated phone systems.
- Object Detection: A computer vision technique that involves identifying and localizing objects within an image or video stream. Object detection is used in various applications, such as autonomous vehicles, surveillance systems, and robotics.
- Edge Computing: A computing paradigm that involves processing and analyzing data at the edge of the network, closer to the source of data. Edge computing is used in various applications, such as autonomous vehicles, IoT devices, and real-time analytics.
- Federated Learning: A machine learning technique that enables multiple devices to collaboratively train a shared model without sharing their data. Federated learning is used in various applications, such as mobile devices, healthcare, and finance.
- Explainable AI (XAI): A field of AI that focuses on developing models and systems that can explain their reasoning and decision-making processes in a human-understandable way. XAI is used in various applications, such as healthcare, finance, and autonomous vehicles.
- Time Series Analysis: A branch of statistics that involves analyzing and modeling time-dependent data. Time series analysis is used in various applications, such as finance, economics, and weather forecasting.
- Ensemble Learning: A machine learning technique that involves combining multiple models to improve the accuracy and robustness of predictions. Ensemble learning is used in various applications, such as finance, healthcare, and image recognition.
- Clustering: A machine learning technique that involves grouping similar data points together based on their features or characteristics. Clustering is used in various applications, such as market segmentation, customer profiling, and anomaly detection.
- Dimensionality Reduction: A machine learning technique that involves reducing the number of features or variables in a dataset while preserving its important information. Dimensionality reduction is used in various applications, such as data visualization, pattern recognition, and feature selection.
- Adversarial Attacks: A technique used to deceive machine learning models by introducing malicious input data that can cause incorrect predictions or classifications. Adversarial attacks are used in various applications, such as cybersecurity, autonomous vehicles, and facial recognition.
- Hyperparameters: Parameters in a machine learning model that are set before the training process begins and determine the model’s architecture and behavior. Hyperparameters are used to optimize the performance and accuracy of the model.
- Bias and Fairness: A critical issue in AI and machine learning that involves ensuring the models are fair and unbiased towards all individuals and groups. Bias and fairness are used in various applications, such as hiring, lending, and criminal justice.
- Deep Reinforcement Learning: A type of machine learning that combines deep learning and reinforcement learning to train agents to make decisions based on high-dimensional sensory input. Deep reinforcement learning is used in various applications, such as robotics, gaming, and finance.
As the field of AI and machine learning continues to evolve, new terms and concepts will emerge. It’s essential to stay up-to-date with the latest developments and technologies to fully understand and leverage the power of AI.
While this glossary provides a basic introduction to AI and machine learning terminology, there is much more to explore and learn about in this exciting and rapidly evolving field.
If you need help understanding these explanations or want to learn more, contact me to explore additional resources and consulting.