You’ve probably noticed all the buzz surrounding artificial intelligence (AI) these days. It’s an exciting technology, but with so many terms and phrases flying around, it can be a bit overwhelming to keep up.
But don’t worry. We’ve got your back! Our artificial intelligence glossary is here to help you navigate the fast-paced world of AI and have conversations like a pro. Whether it’s machine learning, neural networks, or natural language processing, we’ll break down the jargon and acronyms for you.
So, let’s dive in and navigate those AI terms together!
Anthropomorphism refers to the human tendency to attribute human qualities to AI chatbots. Despite lacking emotions or sentience, people often perceive chatbots as kind or cruel based on their responses. This can be due to the chatbot’s skill in mimicking human language, leading to the mistaken belief that it possesses human-like consciousness.
An algorithm is a set of rules that guide machines in problem-solving and learning processes.
Artificial Intelligence (AI)
Artificial intelligence (AI) involves machines performing tasks that imitate or replicate human intelligence, with capabilities ranging from human-like communication to decision-making processes.
BERT (aka Bidirectional Encoder Representation from Transformers)
BERT is an ML framework introduced by Google in 2018. It enhances the comprehension of unlabeled text in various tasks within natural language processing.
Bias in machine learning refers to the assumptions made by a model to simplify the learning process for its assigned task.
A chatbot, also known as a conversational agent or virtual assistant, is a system that engages in dialogue with users based on pre-written responses.
ChatGPT is an advanced conversational AI model developed by OpenAI. It uses a vast amount of back data analysis and pre-existing knowledge and language patterns to generate human-like responses and provide helpful information across various topics.
Cognitive computing is a term often used interchangeably with artificial intelligence (AI) and serves as a way for marketing teams to present AI as more approachable, avoiding the potential negative connotations associated with science fiction.
Computer vision is an interdisciplinary scientific field that focuses on enabling computers to comprehend and interpret digital images or videos at a higher level. It aims to automate tasks that humans can perform with their visual system.
Conversational AI is a specialized area of AI that revolves around creating systems capable of comprehending and producing human-like language to engage in interactive conversations.
Data mining involves analyzing datasets to identify new data patterns that can enhance the model.
Data science is an interdisciplinary field that applies statistical analysis, computer science, and information science to solve data-related problems. Data scientists analyze large datasets to uncover trends and insights, enabling informed decision-making.
Deep learning is a subfield of machine learning that emulates the human brain’s ability to learn from data structures rather than relying on specific programmed instructions. It involves using a neural network with multiple layers to analyze and extract patterns from complex data.
Entity annotation is the practice of assigning labels or tags to unstructured sentences to make them readable by machines. This involves identifying and labeling specific entities such as people, organizations, and locations within a document or text.
Entity extraction is a natural language processing (NLP) technique that involves identifying and extracting specific entities from unstructured text or data, such as names, dates, locations, or organizations.
The F-score is a measure that combines a system’s precision and recall values using their harmonic mean. It is calculated using the formula 2 x [(Precision x Recall) / (Precision + Recall)].
Generative AI is a technology that generates original content, such as text, images, videos, or computer code, by identifying patterns in large training data sets. For instance, ChatGPT generates text-based responses, while DALL-E and Midjourney generate images.
Hallucination is a phenomenon that occurs in large language models where generated text presents made-up information that appears plausible but is actually incorrect. This can include fabrications of data, references, or sources.
LangOps (Language Operations)
LangOps refers to the processes and methods used to develop, train, test, deploy, and manage language models and natural language solutions.
Large Language Model (LLM)
An LLM is a deep learning model trained on extensive text data from the internet to perform tasks such as language understanding and generation. Notable LLMs include BERT, PaLM, GPT-2, GPT-3, GPT-3.5, and the groundbreaking GPT-4.
Machine Learning (ML)
Machine learning is a field within AI that enables systems to process and analyze data automatically without explicit programming. It involves the study of algorithms that can automatically improve and make predictions based on experience and training data.
MLOps (Machine Learning Operations)
ML Ops is the practice of deploying and integrating experimental machine learning models into production web systems. It involves collaboration between data scientists, DevOps, and machine learning engineers to ensure a smooth transition from the development and testing phase to the operational environment.
Natural Language Generation (NLG)
Natural language generation (NLG) involves transforming structured data into understandable text or speech by machines. It is a part of natural language processing (NLP) that focuses on generating human-readable content.
Natural Language Processing (NLP)
Natural Language Processing (NLP) is a field that focuses on enabling machines to understand and interact with human language. It involves analyzing and interpreting spoken or written input to extract meaning and respond intelligibly. NLP plays a crucial role in applications like information retrieval, machine translation, image recognition, and sentiment analysis, allowing machines to communicate effectively and engage in human-like conversations.
A neural network is a machine learning model inspired by the structure and function of the human brain, composed of interconnected artificial nodes called neurons. It is deep neural network that can perform tasks such as speech recognition and image processing by leveraging the collective power of these interconnected neurons.
OpenAI is a research organization that created ChatGPT and focuses on the development of friendly and responsible artificial intelligence. Their GPT-3 model stands as a prominent example of a powerful language model used for various natural language processing tasks.
Overfitting is a common issue in machine learning where an algorithm becomes too focused on the specific examples it was trained on, resulting in poor performance on new and unseen data.
Parameters are variables within a model that enable it to make predictions, and their values are typically estimated using data. In large language models like GPT-4, parameters are numerical values that shape the model’s structure and guide its predictions, with models of this scale having hundreds of billions of parameters.
Predictive analytics is an analytical approach combining data mining and using machine learning algorithms to predict future events or outcomes based on historical data and patterns. It is widely used in various industries and fields as a tool for informed decision-making, enabling organizations to anticipate future performance and trends.
Reinforcement learning is a machine learning technique where an AI model learns to make decisions by trial and error in order to maximize cumulative rewards. It involves the model interacting with its environment, receiving feedback in the form of rewards or punishments, and adjusting its actions accordingly. This approach can be enhanced by human feedback, helping the model improve its performance through ratings, corrections, and suggestions.
Responsible AI refers to the ethical and responsible practices adopted by organizations when implementing and using AI technologies. It involves ensuring transparency, explainability, fairness, and sustainability in the development and deployment of AI models.
Robotic Process Automation (RPA)
RPA is a software technology that enables creating and managing software robots that mimic human actions when interacting with digital systems and software. These robots automate tasks such as filling out web forms with predefined information, simplifying processes, and increasing efficiency.
Sequence modeling refers to a subfield of NLP which deals with the modeling of sequential data such as text, speech, or time series data.
Steerability in AI refers to the capacity to guide and control the behavior and output of an AI system based on human intentions or specific goals. It involves designing AI models with mechanisms that align with user preferences and avoid unintended or undesirable results. Achieving steerability involves ongoing research and refinement, utilizing techniques such as fine-tuning and rule-based systems, and incorporating continuous human feedback during AI development.
Supervised learning is a type of machine learning where a model learns to map inputs to outputs based on labeled examples. It is commonly used for prediction and classification tasks. The learning process involves human intervention, where humans provide the labeled data for the machine to learn from and propose improvements to hierarchical learning that are validated by humans before implementation.
Structured data refers to information that is organized in a consistent and predefined format, often stored in databases. It follows a specific data model, making it easily accessible and usable for both humans and computer programs.
Text to Speech (TTS)
Text-to-speech (TTS) is a technology that converts written text into spoken words using natural-sounding voices. It enables machines to read text aloud, providing a means of communication and accessibility for various applications.
The Turing Test, proposed by Alan Turing, is an assessment of a machine’s ability to mimic human speech and behavior to the point where it becomes indistinguishable from a real person. It remains a widely accepted benchmark for evaluating the level of artificial intelligence achieved by a machine, particularly in terms of language and behavior.
The transformer model is a neural network architecture that revolutionized language understanding by allowing the analysis of entire sentences instead of individual words. It uses self-attention, a technique that enables the model to focus on important words to understand the sentence’s meaning. This architecture, utilized in models like ChatGPT, has significantly advanced natural language processing tasks.
Training data is the dataset used to teach a machine learning algorithm and enable it to make accurate predictions or classifications. It is separate from testing data and plays a crucial role in training the model to recognize patterns and relationships within the training data set.
Unsupervised learning is a machine learning approach that uncovers patterns and insights in data without the need for pre-existing labels or extensive human supervision. It applies logical rules that allow machines to autonomously identify hidden correlations and provide recommendations based on the discovered knowledge. Unlike supervised learning, unsupervised learning operates without human validation, relying on mathematical thresholds to guide its exploration and understanding of the data.
Unstructured data refers to information that does not adhere to a predefined data model or structure and can come from various sources such as text documents, images, videos, and more. Unlike structured data, it lacks a rigid format, making it a representation of real-world information and a significant factor in the growth of artificial intelligence technology.
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