AI Terminology You Need To Know

A

• Agents

  Definition: Agents in AI are autonomous systems or programs that perceive their environment and take actions to achieve specific goals.

  Example: Virtual assistants like Siri or Alexa are agents that help users by interpreting voice commands and executing tasks like setting reminders or playing music.

• Algorithms

  Definition: An algorithm is a step-by-step procedure or formula for solving a problem. In AI, algorithms are used to process data and make decisions.

  Example: In an e-commerce setting, algorithms are used to recommend products based on a customer’s browsing history.

• Artificial Intelligence (AI)

  Definition: AI refers to machines or software that mimic human intelligence, enabling them to perform tasks like learning, problem-solving, or decision-making.

  Example: Chatbots that respond to customer inquiries on websites are powered by AI.

B

• Big Data

  Definition: Big Data refers to extremely large datasets that can be analyzed to reveal trends, patterns, and associations.

  Example: A retailer can analyze big data to understand customer buying habits and improve their marketing strategy.

• Benchmarks

  Definition: Benchmarks are standards or points of reference used to evaluate the performance of AI models.

  Example: AI benchmarks like GLUE or SuperGLUE are used to measure the language understanding ability of models like GPT.

C

• Chain of Thought Prompting

  Definition: A technique where an AI model is guided step-by-step through a reasoning process to ensure accurate, logical responses.

  Example: When asked to solve a math problem, an AI can be prompted to explain each step it takes to ensure the final answer is correct.

• Chatbot

  Definition: A chatbot is an AI-powered program designed to simulate human conversation through text or voice.

  Example: Many businesses use chatbots on their websites to assist customers 24/7 without needing a human agent.

• Claude

  Definition: Claude is an AI chatbot created by Anthropic, designed to have safe, steerable conversations and to understand and generate human language.

  Example: Claude is similar to ChatGPT and is used in customer support systems or as a conversational assistant for various business applications.

D

• Deep Learning

  Definition: Deep learning is a type of AI that mimics the way the human brain works to process data and create patterns for decision-making.

  Example: Voice assistants like Siri use deep learning to understand and respond to commands.

E

• Edge Computing

  Definition: Edge computing refers to processing data near the location where it is generated, rather than sending it to a central server or cloud.

  Example: A security camera using edge computing can analyze footage in real-time, making faster decisions, like identifying a suspicious person.

F

• Facial Recognition

  Definition: Facial recognition is an AI technology that can identify or verify a person by analyzing facial features.

  Example: Smartphones that unlock by scanning your face use facial recognition.

• Fine-Tuning

  Definition: Fine-tuning is the process of taking a pre-trained AI model and training it further on a specific task or dataset.

  Example: A business can fine-tune an AI model like GPT to understand its industry-specific jargon and improve customer interactions.

• Frontier Models

  Definition: Frontier models refer to cutting-edge, large-scale AI systems that push the boundaries of what AI can achieve in reasoning, creativity, and complexity.

  Example: GPT-4 and Google’s Gemini are frontier models designed for advanced problem-solving and creativity tasks.

G

• Gemini

  Definition: Gemini is Google’s next-generation AI, designed for advanced multimodal tasks, integrating text, images, and other inputs to create more sophisticated outputs.

  Example: Businesses can use Gemini for everything from customer service automation to content generation, similar to GPT models but with enhanced multimodal capabilities.

• GPT (Generative Pre-trained Transformer)

  Definition: GPT (Generative Pre-trained Transformer) is a type of AI model that uses deep learning to generate human-like text. It can answer questions, create various forms of written content, translate languages, and provide informative responses. GPT models are trained on vast datasets, enabling them to understand and generate text with impressive accuracy and relevance.

  Example: GPT models, like ChatGPT, are used for tasks like writing, customer support, coding, and more.

• Genearative AI

  Definition: Generative AI refers to AI systems that create new content, such as images, music, or text, based on the data they’ve been trained on.

  Example: Tools like Gemini, ChatGPT or DALL-E are examples of generative AI that help create content from scratch.

H

• Hallucination

  Definition: In AI, hallucination refers to instances where a model generates information or facts that are incorrect or not based on its training data.

  Example: An AI assistant might “hallucinate” by creating an answer to a question that sounds convincing but is factually inaccurate.

• Hyperautomation

  Definition: Hyperautomation is the use of AI and other advanced technologies to automate as many business processes as possible.

  Example: Invoicing, email marketing, and customer support can all be handled through hyperautomation tools, reducing the need for manual tasks.

I

• Internet of Things (IoT)

  Definition: IoT is the network of physical objects (like devices, vehicles, or home appliances) embedded with sensors and connected to the internet.

  Example: Smart thermostats that adjust temperature automatically based on your preferences are part of IoT.

J

• Just-in-Time Learning

  Definition: This refers to delivering training or learning opportunities at the exact moment they are needed, often supported by AI systems that can assess when a user might need help.

  Example: A customer service tool that provides agents with suggested solutions based on the current problem is an example of just-in-time learning.

K

• Knowledge Graph

  Definition: A knowledge graph is a database that stores information in a way that allows AI to understand and reason about relationships between things.

  Example: A knowledge graph is a database that stores information in a way that allows AI to understand and reason about relationships between things.

L

• Large Language Models (LLMs)

  Definition: LLMs are AI systems trained on vast amounts of text data, enabling them to understand, generate, and translate human language.

  Example: GPT-4 is a large language model capable of writing essays, summarizing content, and carrying out conversations in multiple languages.

• LLaMA (Large Language Model Meta AI)

  Definition: LLaMA is Meta’s (Facebook's) large language model designed to help AI researchers and developers create more responsible and high-performing AI systems.

  Example: LLaMA is being used to develop more efficient AI tools that balance performance with ethical concerns

M

• Machine Learning (ML)

  Definition: ML is a subset of AI that allows systems to automatically improve their performance by learning from data without being explicitly programmed.

  Example: A business can use machine learning to analyze customer reviews and automatically detect common complaints or praises.

• Model Collapse

  Definition: Model collapse refers to a scenario where an AI model's performance degrades over time, often due to poor training data or constant reliance on generated data rather than original inputs.

  Example: If a business continually trains its customer service chatbot on low-quality data, it could experience model collapse, where responses become less accurate over time.

• Model Training

  Definition: Model training is the process of teaching an AI system to make decisions or predictions by providing it with data to learn from.

  Example: Training an AI model with historical sales data can help predict future sales trends.

• Multi-modal

  Definition: Multi-modal AI refers to systems that can process and generate multiple types of data, such as text, images, audio, and video.

  Example: Tools like Gemini and GPT-4 are multi-modal, allowing users to input both text and images to generate responses.

N

• Natural Language Processing (NLP)

  Definition: NLP is a branch of AI that enables machines to interpret, analyze, and generate human language the way a human would.

  Example: AI tools that generate automatic email replies or analyze customer feedback use NLP.

• Neural Networks

  Definition: Neural networks are the underlying architecture of AI models, mimicking the human brain’s network of neurons to process and learn from data.

  Example: Neural networks are used in tasks like image recognition, allowing AI to identify objects in photos.

O

• Open Source

  Definition: Open source refers to software or models that are made available for anyone to use, modify, or distribute freely.

  Example: OpenAI’s GPT-3 is not open source, but many AI models, like LLaMA, are open source, allowing developers to build on them for free.

• Optical Character Recognition (OCR)

  Definition: OCR is the technology that enables AI to read text from images or scanned documents.

  Example: OCR can be used to scan and digitize paper receipts for bookkeeping.

P

• Parameters

  Definition: Parameters are the variables that AI models use to make predictions or decisions. The more parameters a model has, the more complex its decision-making process can be.

  Example: GPT-4 has billions of parameters, allowing it to generate highly accurate and nuanced text outputs.

• Prompt

  Definition: A prompt is the input or question given to an AI model to generate a response.

  Example: When using Gemini or ChatGPT, a user might enter the prompt “Transcribe the text in the attached image,” and the model will generate a response.

• Prompt Engineering

  Definition: Prompt engineering is the process of crafting effective prompts to get the best possible responses from an AI model.

  Example: A business might fine-tune prompts to ensure its AI customer service tool generates helpful, accurate responses.

• Predictive Analytics

  Definition: Predictive analytics uses AI to analyze data and make predictions about future outcomes.

  Example: E-commerce businesses use predictive analytics to suggest products to customers based on their past shopping behavior.

Q

• Quantum Computing

  Definition: Quantum computing is a type of computing that uses the principles of quantum mechanics to process data at significantly faster speeds than traditional computers.

  Example: While still in its early stages, quantum computing could revolutionize industries like pharmaceuticals by speeding up drug discovery.

R

• Reasoning

  Definition: Reasoning in AI refers to the model's ability to make decisions based on data, rules, or logic.

  Example: GPT-4 can reason through complex queries, such as explaining the pros and cons of different marketing strategies.

• Robotic Process Automation (RPA)

  Definition: RPA uses software robots or "bots" to automate repetitive business tasks, like data entry or processing transactions.

  Example: RPA can be used to automatically generate invoices or update customer records, reducing the workload for employees.

S

• Synthetic Data

  Definition: Synthetic data is artificially generated data used to train AI models, often to avoid privacy concerns or to create datasets where real-world data is scarce.

  Example: A healthcare company might use synthetic patient data to train an AI model to predict medical conditions without violating patient privacy.

• Supervised Learning

  Definition: A type of machine learning where the AI is trained on labeled data, meaning each training example is paired with the correct output.

  Example: A system trained to recognize spam emails uses supervised learning to differentiate between spam and non-spam messages.

T

• Training Data

  Definition: Training data is the dataset used to teach an AI model how to make decisions or predictions.

  Example: A company might use thousands of customer reviews as training data to teach an AI model to identify sentiment in new reviews.

• Turing Test

  Definition: A test created by Alan Turing to determine whether a machine can exhibit human-like intelligence.

  Example: If an AI can hold a conversation with a human and the human can't tell it's a machine, the AI has passed the Turing Test.

U

• Unsupervised Learning

  Definition: A type of machine learning where the AI is given data without explicit labels and must find patterns or relationships on its own.

  Example: Clustering customers based on their purchasing habits without prior knowledge of their categories uses unsupervised learning.

V

• Virtual Assistant

  Definition: A virtual assistant is an AI-powered software that performs tasks or services based on user commands or questions.

  Example: Siri, Alexa, and Google Assistant are common virtual assistants used to set reminders, control smart home devices, or search the web.

W

• Workflow Automation

  Definition: Workflow automation uses AI and other tools to streamline business processes by automating repetitive tasks.

  Example: Automating email campaigns, social media posts, and employee onboarding tasks to save time.

X

• Explainable AI (XAI)

  Definition: Explainable AI refers to systems that provide understandable and transparent reasons for their decisions or actions.

  Example: XAI is important in industries like healthcare, where a doctor might need to understand why an AI system recommended a particular treatment.

Y

• Yield Optimization

  Definition: Yield optimization is the use of AI to maximize the output or profitability of a system, such as improving advertising placements or optimizing product pricing.

  Example: A retailer might use AI-driven yield optimization to adjust prices in real-time to increase profit margins during peak sales periods.

Z

• Zero-Day Attack

  Definition: A zero-day attack is a cyberattack that exploits a previously unknown vulnerability in a system before developers have a chance to fix it.

  Example: AI-driven cybersecurity tools can help detect potential zero-day attacks by analyzing patterns and identifying unusual behavior in real-time.