The world of AI is a complicated one. With terms flying around the place it is hard to keep up with the lingo. Here at Brocaly we have devised this glossary to help you get your head around the key terminology you might bump into.
Check it out below and of course if you have any questions drop us a line on LinkedIn!
AI Ethics:
Considerations and principles addressing ethical societal impacts of using AI technologies.
AIaaS (AI as a Service):
Cloud-based platforms offering AI capabilities and services, enabling businesses to leverage AI without large investment or infrastructure.
AI Hallucination:
AI Hallucination happens when an artificial intelligence system gives out false or wrong information, presenting it as true.
Algorithm:
Step-by-step procedure or formula for solving a problem or accomplishing a task in AI and ML.
Artificial Intelligence (AI):
Artificial Intelligence (AI) is a part of computer science where computers and machines are taught to do tasks that usually need human intelligence.
Autonomous Agents:
Autonomous agents are computer systems or robots capable of independent decision-making and task execution without human intervention. They use artificial intelligence to sense, understand, and act upon their environment to achieve predefined goals.
AutoQA:
Automated Quality Assurance is the use of technology to test software functionality, ensuring its quality without manual intervention.
Chatbots:
Chatbots are computer programs that talk to people using everyday language. They act like humans in conversations and are able to answer questions.
Conversation Intelligence:
The practice of analysing and deriving insights from conversations, encompassing verbal, written, or digital interactions, to understand communication dynamics, identify patterns, and inform decision-making across various domains.
Data Labelling:
Data labelling involves annotating or tagging data with attributes to make it usable for machine learning algorithms. This process provides context to the raw data, enabling algorithms to learn and make predictions. Accurate labelling is essential for effective model training.
Data Mining:
Process of discovering patterns and insights from large datasets using AI techniques.
Data Pre-processing:
Cleaning, transforming and organisation raw data to make it suitable for AI model training.
Deep Learning:
Deep Learning is a subset of Machine Learning (ML) that uses Artificial Intelligence (AI) to make computer systems learn like human brains.
Generative AI:
Generative AI is a type of Artificial Intelligence that can produce new content, like images or text, inspired by human-created data. It learns from large datasets to generate original outputs, making it useful for various tasks such as art creation and content generation.
Horizontal AI:
Horizontal AI, also known as generic AI, refers to artificial intelligence systems designed to perform a wide range of tasks across multiple domains, rather than specializing in a single area like vertical AI.
Large Language Models (LLM):
A Large Language Model (LLM) is an advanced artificial intelligence system designed to understand and generate human-like text by analyzing extensive datasets. Functioning as a sophisticated tool, it utilizes pattern recognition to predict subsequent words in a sentence, facilitating coherent and context-relevant textual outputs.
Machine Learning (ML):
Machine Learning (ML) lets computer programs learn to do tasks without needing to be specifically told how.
Models:
Models are computer systems trained to understand patterns in the data that they have been trained on. This is then used to apply the learned patterns on unseen data and predict outcomes.
Natural Language Processing (NLP):
Natural Language Processing (NLP) refers to teaching computers to understand and work with human language, enabling them to do tasks like translate languages, analyse sentiments in text and recognise speech.
Outcome Testing Teams (OTT):
Outcome Testing Teams (OTT) check how well Artificial Intelligence (AI) works in the real world, especially in financial services.
PII Data Redaction:
PII Data Redaction: The process of identifying and removing personally identifiable information from data sets to protect individual privacy before analysis or sharing.
Sentiment Analysis:
Sentiment Analysis is a way to figure out the emotional tone of words spoken or written using Natural Language Processing (NLP).
Speech Analytics:
Analysing recorded conversations to extract insights for improving customer experiences and informing decision-making.
Transfer Learning:
Technique where knowledge gained from one task is applied to a related task, speeding up model training.
Vertical AI:
Vertical AI is specialized artificial intelligence designed for specific industries or applications, offering tailored solutions to meet unique requirements.
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