Man holding light bulbs
0

50 Essential AI Terms Every Conference Attendee Should Know

TJ Mapes

Updated annually — your quick, friendly guide to understanding the language of modern AI before your next conference.

Quick Take Summary

  • 📚 Know the language: Understanding key AI terms makes conferences far more valuable.
  • 🧠 From AI to AGI: Learn the differences between artificial, machine, and deep learning.
  • 💡 Be conversation-ready: Decode common buzzwords like LLMs, RAG, and multimodal AI.
  • 🌍 Explore events: Attend top conferences like AI Engineering Conference, Zurich AI Festival, and AI World Congress 2025.

Why It Matters

Stepping into your first AI conference can feel like learning a new language. Speakers reference "transformers," "embeddings," and "RAG pipelines" as if they’re common knowledge. This glossary ensures you’re fluent enough to follow every keynote, workshop, and hallway chat.

Whether you’re attending CDAO Chicago, DeepFest, or Function 1, these 50 terms will help you connect the dots.

🧠 Core Concepts

1. Artificial Intelligence (AI): The science of making machines think and act like humans.
2. Machine Learning (ML): A subset of AI focused on learning from data.
3. Deep Learning (DL): A type of ML using neural networks for complex tasks.
4. Neural Network: A set of algorithms that mimic how the brain processes information.
5. Natural Language Processing (NLP): How computers understand and generate human language.
6. Computer Vision: Teaching machines to interpret images and videos.
7. Reinforcement Learning: Training models through rewards and penalties.
8. Supervised Learning: Learning from labeled data.
9. Unsupervised Learning: Finding patterns in unlabeled data.
10. Semi-Supervised Learning: Combining both labeled and unlabeled examples.

Learn more about how these concepts interact in our Machine Learning vs Deep Learning Guide.

⚙️ Models & Techniques

11. Large Language Model (LLM): An AI system trained on massive text datasets (e.g., GPT-4).
12. Transformer: The model architecture behind modern LLMs.
13. Tokenization: Breaking text into small pieces (tokens) for AI processing.
14. Embeddings: Numeric representations of words or data for AI understanding.
15. Fine-Tuning: Adapting a pretrained model for a specific task.
16. Zero-Shot Learning: Making predictions without seeing similar examples.
17. Few-Shot Learning: Learning from just a few examples.
18. Multimodal AI: Systems that understand multiple types of data (text, image, sound).
19. RAG (Retrieval-Augmented Generation): Enhancing AI with external data sources.
20. Agentic AI: AI systems that can plan, act, and interact autonomously.

🧩 Tools & Frameworks

21. TensorFlow: Open-source ML library by Google.
22. PyTorch: Popular deep learning library by Meta.
23. LangChain: Framework for building AI applications with LLMs.
24. Hugging Face: Platform for sharing and training open AI models.
25. OpenAI API: Tools for integrating GPT-based models into apps.
26. MLflow: Open-source tool for tracking and managing ML experiments.
27. Kubernetes: Manages large-scale AI workloads across servers.
28. Vector Databases: Store embeddings for similarity search (e.g., Pinecone, Weaviate).
29. MLOps: Machine learning operations — managing deployment pipelines.
30. AutoML: Automated machine learning that builds models with minimal coding.

🌐 Business & Strategy

31. Responsible AI: Ensuring fairness, transparency, and ethics in AI systems.
32. AI Governance: Frameworks for controlling how AI is used and monitored.
33. Edge AI: Running AI models directly on devices (not in the cloud).
34. Federated Learning: Training models without moving private data.
35. AI Policy: Rules and regulations shaping AI adoption globally.
36. Synthetic Data: Artificially generated data used for model training.
37. Data Governance: Managing how data is collected and used responsibly.
38. Explainable AI (XAI): Making AI model decisions understandable.
39. AI Risk Management: Identifying and mitigating risks in AI systems.
40. AI ROI (Return on Investment): Measuring the business impact of AI projects.

🚀 Emerging Technologies

41. Foundation Models: Large-scale pretrained models like GPT or Claude.
42. Generative AI: AI that creates new content — text, images, code, or music.
43. Diffusion Models: Used in image generation (like Stable Diffusion).
44. Prompt Engineering: Crafting inputs to get better AI outputs.
45. Synthetic Agents: AI systems simulating human decision-making.
46. Cognitive AI: AI designed to mimic human thought processes.
47. AI in Production: The practice of deploying AI models at scale.
48. Human-in-the-Loop: Combining AI automation with human oversight.
49. Continual Learning: AI systems that learn and adapt over time.
50. AGI (Artificial General Intelligence): The theoretical “human-level” AI — not yet achieved.

🎓 Learn These Terms in Context

Reading about these concepts is one thing — hearing them explained live by experts is another.
At conferences like:

You’ll hear these terms used by the people defining the future of AI.

Related Reading

Final Thoughts

AI vocabulary evolves fast — but mastering the fundamentals gives you the confidence to engage meaningfully. Bookmark this page and revisit it before your next event. Each year, we update it with new terms that emerge across the AI landscape.

🎟️ Explore all upcoming AI events → View the full directory