AI Glossary of Legal Terms

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Smokeball AI Glossary

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Algorithm

A precise set of rules or instructions that a computer follows to solve specific problems or perform tasks. An AI algorithm is fundamental to computational tasks ranging from simple calculations to complex processes.

Algorithmic Bias

Algorithmic bias occurs when an artificial intelligence system yields unjust or discriminatory results as a consequence of biased training data. Tackling this bias is essential to develop ethical and impartial AI models.

Artificial Intelligence (AI)

AI is a field of computer science focused on creating machines capable of performing tasks that typically require human intelligence. AI techniques include machine learning, natural language processing, and image creation.

Authentication

The process of verifying the identity of users or systems before granting access to AI resources or data, ensuring that only authorized entities can interact with AI systems.

Automation

The integration of automation and artificial intelligence is revolutionizing the business landscape and is expected to drive economic growth through enhancements in productivity. Automation streamlines processes, reduces errors, and improves efficiency.

Big Data

Large datasets, often referred to as big data, are used for the training of advanced models such as ChatGPT. These models synthesize extensive amounts of data gathered from various sources, including books, articles, websites, and other forms of digital content to learn and generate outcomes.

Chatbot

Chatbot AI refers to a software application that employs artificial intelligence to mimic dialogues with users. It’s frequently utilized for customer assistance, information gathering, and providing interactive experiences.

ChatGPT

ChatGPT is an advanced AI model created by OpenAI designed to comprehend and produce text that resembles human communication by utilizing extensive language models to deliver responses that are sensitive to the context of the input prompts.

Conversational AI

Conversational AI refers to a suite of technologies that drive the functionality of conversational assistants or chatbots. These technologies power effective and automated communication through both text and speech, interpreting user intent, analyzing language, and providing responses that mimic human interaction.

Cybersecurity

Cybersecurity in the context of artificial intelligence involves safeguarding AI systems and their data from unauthorized access, malicious attacks, and potential harm. Strong cybersecurity protocols are crucial for preserving the integrity of AI technologies.

Data Privacy

The protection of data privacy in conjunction with artificial intelligence involves safeguarding personal and sensitive information, ensuring it is managed responsibly, and adhering to privacy regulations.

Deep Learning

Deep learning artificial intelligence represents a specialized area within machine learning that utilizes intricate neural networks characterized by numerous layers, often referred to as deep architectures. Deep learning models demonstrate superior performance in various applications, including image recognition, natural language comprehension, and speech generation.

Deep Fakes

Deep fakes are AI-generated manipulated images and videos aimed at disseminating misinformation and disinformation.

Extractive AI

Extractive AI refers to a type of artificial intelligence that uses Natural Language Processing (NLP) to identify specific key phrases, sentences, or sections from extensive collections of documents. This technology allows users to input desired keywords and retrieves the precise text that corresponds to the keyword-driven search query.

Encryption

Encryption AI refers to the method of transforming data into a coded format to safeguard it from unauthorized access, ensuring the security of information during both transmission and storage.

Ethics

Ethics in artificial intelligence encompass the standards and frameworks that direct the creation and application of AI technologies. Practitioners committed to responsible AI take into account the ethical consequences, equity, clarity, and societal impact.

Fairness

Equity in artificial intelligence is essential to ensure that AI systems operate without bias related to race, gender, or socioeconomic status, fostering equal treatment and opportunities for all individuals.

Fine-Tuning

Fine-tuning in machine learning is the process of training a model further on a specific set of data to improve its performance for a particular application. This approach is crucial in deep learning, especially in the training of foundational models used in generative AI.

Foundation Models

Foundational models in generative AI are trained on vast amounts of data, making them adaptable for a wide range of general tasks, including generating outputs (generative AI).

Generative AI

This type of algorithm produces novel outputs derived from the data on which it has been trained. In contrast to extractive AI systems that focus on identifying patterns, retrieving existing data, and making forecasts, generative AI is capable of creating original content, including text, audio, and other formats.

Hallucinations (in AI)

Generative AI hallucinations occur when a system produces information or responses that are incorrect, inaccurate, fabricated, or unrelated to the input data.

Inference

AI inference is the process by which an AI system applies its trained model to make predictions or decisions based on new data. Inference is a key step in deploying AI models.

Large Language Model (LLM)

LLMs are advanced foundational models trained on vast amounts of text data to understand and generate human-like language for various tasks. LLMs are crucial for AI like ChatGPT.

Machine Learning (ML)

ML is a subset of AI where computers learn from data without being explicitly programmed. Machine learning algorithms improve their performance over time through experience.

Model

An AI model is a mathematical representation of a problem or system used by AI to make predictions or decisions. Models are trained on data to generalize patterns.

Natural Language Processing (NLP)

NLP is a branch of AI that enables computers to understand, interpret, and respond to human language, powering chatbots, language translation, and sentiment analysis.

Neural Network

A neural network is a computational model inspired by the human brain’s interconnected neurons, rendering it fundamental to deep learning and pattern recognition.

OpenAI

An organization dedicated to advancing AI research and development, OpenAI aims to ensure that AI benefits all of humanity while promoting transparency and safety.

Open-Source

Open-source refers to the underlying code used to run AI models being freely available for testing, scrutiny, and improvement.

Overfitting

When an AI model learns too much from its training data, including noise and irrelevant details, AI overfitting may occur, leading to poor performance on new data.

Predictive Analysis

Predictive analysis involves using AI to analyze historical data and make predictions about future events, informing decision-making in various domains.

Prompt Engineering

Prompt engineering in AI is the process of designing and refining prompts to improve AI model performance, yielding more accurate and relevant responses.

Prompt Writing

Prompt writing involves crafting specific questions or commands to elicit optimal responses from an AI system, enhancing user interactions.

Reinforcement Learning

Reinforcement learning is a type of machine learning where an AI system learns by interacting with its environment and receiving rewards or penalties, used in game playing and robotics.

Responsible AI

Responsible AI involves developing and using AI in ways that align with ethical standards, transparency, and societal well-being.

Robotics

Robotics, a field that merges engineering and computer science to create machines, is designed to execute specific programmed tasks. Although AI can be integrated into robotics, robots operate based on human programming, whereas AI involves a machine’s ability to learn and adapt to accomplish tasks independently.

Scalability

Scalability refers to an AI system’s capacity to handle increasing workloads or expand its capabilities without sacrificing performance, efficiently accommodating more data, users, or tasks as demand grows.

Semantic Search

Semantic search considers not only keywords but also the context and intent behind a query. Semantic search is critical for understanding legal texts.

Supervised Learning

Supervised learning is a type of machine learning where the AI system is trained using labeled data. The goal is for the AI model to learn patterns and relationships, enabling accurate predictions on new data.

Token

An AI token is a basic unit of text that a large language model (LLM) uses to understand and generate language. A token may be an entire word or parts of a word.

Training Data

Training data is the foundational dataset used to teach an AI system how to perform specific tasks. Training data helps AI models learn patterns, features, and correlations.

Transparency

Transparency involves making the decision-making processes of AI systems understandable and accessible to users and stakeholders. Transparent AI models foster trust and accountability.

Tuning

Tuning refers to adjusting an AI model’s parameters to optimize its performance on specific tasks or datasets. Tuning ensures that the model generalizes well and achieves the best possible results.