Artificial Intelligence A-Z Glossary

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Artificial Intelligence A-Z Glossary

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ByDawn Christine Simmons

As AI-driven technologies like generative AI, Agent Assist, and automated workflows reshape industries, a shared language is more critical than ever. 72% of enterprises now consider an AI glossary essential for aligning teams and accelerating innovation.

Ask the Experts: Secure & Compliant AI

Wondering how to establish a robust AI foundation while maintaining full compliance? By implementing the right security controls and governance frameworks from day one, you can confidently adopt Agentic AI and embrace Workflow Data Fabric solutions. Furthermore, prioritizing risk assessment, data privacy, and transparent processes paves the way for faster innovation and seamless enterprise-scale adoption.

Why the urgency? With AI analytics, automation, and evolving acronyms, a trusted reference ensures everyone—from data architects to business leaders—stays on the same page. More importantly, it empowers fast, informed decisions in an era of rapid digital transformation. Explore the key terms, tools, and processes shaping AI’s future.

Stanford University’s Pedram Mokrian introduced AI for Business Leaders:

Deep Dive: Artificial Intelligence A-Z Glossary

Navigate Agentic AI and Workflow Data-Fabric with this streamlined glossary of key terms.

A-B

  • AI Assist – AI-driven prompts, suggestions, and automated workflows that boost productivity.
  • Agent Assist – Real-time AI support for human agents using NLP and ML.
  • Agentic AI – Autonomous AI making decisions with live data and minimal human input.
  • Algorithm – A set of instructions for solving problems or computations.
  • Anthropomorphism – Assigning human traits to AI, even when it lacks emotions.
  • Automated Development – AI tools handling code generation and deployment in CI/CD.
  • Automated Test – Scripts automating software testing to speed up QA cycles.
  • Bias – Errors in AI predictions due to skewed training data.
  • Big Data – Large, fast-moving datasets requiring advanced analytics.

C-D

  • CD (Continuous Delivery/Deployment) – Automates code releases for efficiency.
  • Chatbot – AI-powered conversational agents via text or voice.
  • CI (Continuous Integration) – Merges code changes frequently with automated testing.
  • Data Fabric – Unified architecture for managing structured and unstructured data.

E-M

  • Generative AI (GenAI) – AI creating new content—text, images, or code—by learning from data.
  • Hallucination – AI generating incorrect or nonsensical outputs.
  • Machine Learning (ML) – Algorithms learning from data to make predictions.
  • Modern AI Data Fabric – AI-enhanced data streaming for real-time decisions.

N-Z

  • Now Platform-Automate and improve work across the enterprise with AI, powered by the intelligent platform for end-to-end digital transformation. NOW Platform Glossary
  • Unstructured Data – Free-form data without a predefined structure.: Data without a fixed format, such as text documents, images, or sensor data.
  • Predictive Intelligence – ML-driven forecasting based on historical data.
  • RPA (Robotic Process Automation) – Automates repetitive tasks using AI “bots.” Robotic Process Automation (RPA) Hub glossary
  • Structured Data – Organized data stored in databases with fixed schema.
AI Revolutionizes Service Management - Association of Artificial Intelligence (AI) and Robotic Process Automation (RPA) https://www.linkedin.com/groups/13699504/
AI Revolutionizes Service Management – Association of Artificial Intelligence (AI) and Robotic Process Automation (RPA) https://www.linkedin.com/groups/13699504/

MIT presents AI and What is Intelligence on Machines

AI A-Z Glossary of Top Job Roles

Remarkably, AI is one of the fastest-growing career fields, projected to create 97 million new jobs this year. Moreover, roles like Machine Learning Engineer and AI Specialist are surging at a 74% annual rate. Consequently, we’ve compiled this concise A–Z list of pivotal AI careers—each definition kept short and impactful—to help you navigate the explosive world of AI opportunities. 🚀

A–C

  • AI Product Manager (Role)
    Strategically shapes AI product roadmaps, aligning business needs with technical feasibility.
    🔗 Find AI Product Manager Jobs
  • Big Data Engineer (Role)
    Efficiently builds and optimizes data pipelines for massive, fast-evolving datasets.
    🔗 Find Big Data Engineer Jobs
  • Computer Vision Engineer (Role)
    Rapidly creates AI-driven image and video processing applications for real-world use.
    🔗 Find Computer Vision Engineer Jobs
  • Chatbot Developer (Role)
    Swiftly designs and deploys AI-powered virtual assistants for automated user interactions.
    🔗 Find Chatbot Developer Jobs

D–G

H–P

R–Z

Jobs n Career Success: https://www.linkedin.com/groups/2079/
Jobs n Career Success: https://www.linkedin.com/groups/2079/

Acronyms: Artificial Intelligence A-Z Glossary:

AI is transforming every industry, yet the surge of acronyms can be daunting. This succinct guide clarifies the core terms behind AI, RPA, and Generative AI—empowering you to navigate the future of innovation with confidence.

  • AI (Artificial Intelligence): Refers to the simulation of human intelligence by computer systems, enabling machines to learn from data, reason, and perform tasks.
  • ASR (Automatic Speech Recognition): Converts spoken language into text through machine learning models, commonly used in virtual assistants and transcription services.
  • GAN (Generative Adversarial Network): A class of machine learning frameworks where two models (generator and discriminator) compete, leading to the creation of realistic synthetic data (images, audio, etc.).
  • GPT (Generative Pre-trained Transformer): A family of large language models known for generating human-like text. Trained on vast text corpora, GPT can perform tasks like translation and summarization.
  • LLM (Large Language Model): Extremely large neural networks trained on diverse text data, enabling them to understand and generate coherent, context-aware language outputs.
  • ML Ops (Machine Learning Operations): Practices that merge machine learning, DevOps, and data engineering to streamline the development, deployment, and monitoring of ML models in production.
  • NLG (Natural Language Generation): AI-driven technology that produces human-readable text from data inputs, often used for automated reporting and content creation.
  • NLP (Natural Language Processing): The branch of AI focusing on enabling computers to understand, interpret, and respond to human language through text and speech.
  • NLU (Natural Language Understanding): A subset of NLP that delves deeper into comprehension of context and intent within spoken or written language.
  • RPA (Robotic Process Automation): Software bots that automate repetitive, rules-based tasks, streamlining operations and reducing human error in data entry, billing, or customer support.
  • TTS (Text to Speech): Transforms written text into spoken voice output, enhancing accessibility and interactive experiences in applications and devices.
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Other Artificial Intelligence A-Z Glossary Resources

Use Cases in the News:

Accelerating AI in Biomedical Research at the Broad Institute and NVIDIA

Accelerating AI in Biomedical Research

As AI and biomedical research rapidly evolve, collaboration becomes essential. Leading this revolution, the Broad Institute, partnering with NVIDIA, is pushing boundaries in computational biology, precision medicine, and drug discovery. By seamlessly integrating AI with cutting-edge hardware and scalable data infrastructure, they are dramatically improving speed and accuracy in biomedical breakthroughs.

As data complexity surges, traditional methods struggle to keep pace. To bridge this gap, AI-driven solutions not only streamline genomic analysis but also enhance disease modeling, biomarker discovery, and therapeutic innovation. In short, years of research now take mere months.

Even more significantly, the Broad Institute’s genomic expertise, NVIDIA’s AI acceleration, and 8INS’s AI-driven workflows combine to reshape healthcare. As a result, AI is no longer optional—it’s essential for tackling the world’s toughest health challenges.

Through strategic collaboration, continuous innovation, and transformative AI applications, these organizations are setting a new benchmark for biomedical research. Moving forward, their work promises even faster life-saving discoveries.

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