AI Terminology

Anomaly Detection: The ability to automatically detect errors or unusual activity in a system.
Artificial General Intelligence (AGI): An AI that theoretically has the capacity to learn and perform any intellectual task that humans or animals can do.
Artificial Intelligence (AI): A system that demonstrates behavior that could be interpreted as human intelligence.
Computer Vision: The ability of software to visually interpret the world through cameras, video, and images.
Deep Learning (DL): A subset of machine learning where algorithms are designed to function similarly to the human brain. These algorithms, called artificial neural networks, are designed to imitate the way humans think and learn.
Discriminative AI: Discriminative AI models are trained to distinguish between different classes of data. For example, a discriminative AI model could be trained to distinguish between cats and dogs.
Enterprise AI: Enterprise AI is the application of AI technologies to solve business problems. For example, enterprise AI could be used to automate customer service, optimize supply chains, or detect fraud.
Generative AI: Used to create new text, images, video, audio, code or synthetic data.
Knowledge Mining: The ability to extract information from large volumes of often unstructured data to create a searchable knowledge store.
LLMs: LLMs or Large Language Models are a type of generative AI model that are trained on massive datasets of text and code. LLMs can be used to generate text, translate languages, write different kinds of creative content, and answer your questions in an informative way.
Machine Learning: Often the foundation for an AI system, it is the method we use to "teach" a computer model to make predictions and draw conclusions from data.
Natural Language Processing: The ability of a computer to interpret written or spoken language, and respond in kind.
Neural Network: A series of algorithms that endeavor to recognize underlying relationships in a set of data through a process that mimics the way the human brain operates.
Prompt Engineering: Prompt engineering is the process of designing prompts to get exactly the answer you need. It’s carefully crafting or choosing the input (prompt) that you give to a machine learning model to get the best possible output.

What it means for customers: When your generative AI tool gets a strong prompt, it’s able to deliver a strong output. The stronger and more relevant the prompt, the better the end user experience.

What it means for teams: Can be used to ask a large language model to generate a personalized email to a customer, or to analyze customer feedback and extract key insights.
Reinforcement Learning: A type of Machine Learning where an agent learns to behave in an environment by performing certain actions and observing the results or rewards of those actions.
Sentiment Analysis: Sentiment analysis involves determining the emotional tone behind words to gain an understanding of the attitudes, opinions, and emotions of a speaker or writer. It is commonly used in CRM to understand customer feedback or social media conversation about a brand or product.

What it means for customers: Customers can offer feedback through new channels, leading to more informed decisions from the companies they interact with.

What it means for teams: Sentiment analysis can be used to understand how customers feel about a product or brand, based on their feedback or social media posts, which can inform many aspects of brand or product reputation and management.
Strong AI: Also known as Artificial General Intelligence (AGI), it refers to a type of artificial intelligence that is theoretically as capable as a human in any intellectual task. It represents an AI system with the ability to understand, learn, adapt, and implement knowledge in a wide array of tasks, similar to a human mind.
Weak AI: Also known as Narrow AI, it refers to AI systems that are designed and trained for a specific task. These systems do not possess general intelligence, but they can outperform humans in their specific tasks. Examples include recommendation systems like those used by Netflix or Amazon, and voice recognition systems like Siri or Alexa.