AI Terms Glossary (2024 Edition)
Explore the cutting-edge world of AI with our comprehensive 2024 glossary, covering key terms, concepts, and predictions in the rapidly evolving field of artificial intelligence.
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Fundamental AI Concepts
AGI (Artificial General Intelligence)
AI that can understand, learn, and apply intelligence across any domain, similar to human cognition. Still theoretical in 2024 but a major focus in AI research.
Machine Learning (ML)
A subset of AI that enables systems to learn from data without explicit programming. Powers recommendation systems, fraud detection, and predictive analytics.
Deep Learning (DL)
A subset of machine learning that uses neural networks with many layers to model complex patterns in data. Key to advances in image recognition, speech processing, and autonomous vehicles.
Natural Language Processing (NLP)
The field of AI focused on enabling computers to understand, interpret, and generate human language. Powers applications like virtual assistants and speech recognition.
Advanced AI Technologies

1

LLM (Large Language Model)
AI models trained on extensive datasets to predict and generate text that mimics human language. Used in chatbots, translation services, and text summarization.

2

Generative AI
AI that can create new content, such as text, images, or music. Examples include OpenAI's DALL·E and GPT-4.

3

Foundation Model
A broad AI model trained on diverse data, serving as a base for many tasks. Examples include GPT-4 and BERT, which can be fine-tuned for specific applications.

4

Transformer Model
The architecture behind many recent breakthroughs in AI, including LLMs. Uses attention mechanisms to understand relationships between words or data elements.
AI Ethics and Governance
AI Alignment
Ensuring AI systems' goals align with human values and objectives, especially important as AI systems become more autonomous and powerful.
AI Bias
Prejudices or unfair outcomes arising when AI models are trained on biased data. Can perpetuate stereotypes and unequal treatment if not properly addressed.
AI Governance
Policies and frameworks used to oversee AI development and ensure safe and responsible deployment, including legal, ethical, and technical standards.
Ethical AI Frameworks
Guidelines designed to ensure AI systems are developed and deployed responsibly, considering fairness, accountability, transparency, and inclusivity.
Ethics in AI
The field concerned with the ethical implications of AI technologies, such as privacy, bias, job displacement, and the moral status of autonomous systems.
Explainability (XAI)
AI's ability to make its decision-making process understandable to humans. Critical for transparency, especially in high-stakes industries like healthcare and finance.
AI Applications and Innovations
AI-Driven Automation
The use of AI to automate repetitive or mundane tasks in industries ranging from finance to healthcare, leading to efficiency gains but raising job displacement concerns.
AI-Augmented Creativity
AI systems designed to enhance human creativity in areas like design, music, and storytelling by generating novel ideas or assisting in the creative process.
AI for Social Good
The use of AI to solve societal challenges such as climate change, healthcare access, and poverty. Focused on ensuring AI benefits everyone.
Robotic Process Automation (RPA)
Technology that uses AI and machine learning to automate structured, repetitive tasks across business processes. Popular in sectors like finance and customer service.
Emerging AI Concepts
Digital Twins
Digital replicas of physical systems that AI and machine learning can analyze and simulate. Used in manufacturing, urban planning, and healthcare to optimize operations and predict outcomes.
Edge AI
AI processing that happens locally on devices (the "edge") rather than in centralized cloud servers. Allows for faster response times and greater privacy.
Quantum AI
The fusion of quantum computing with AI to solve problems intractable for classical computers. Still in its infancy but promises to revolutionize fields requiring vast computational power.
AI's Impact and Future Predictions
1
2024: Current Impact
AI's Impact on Roles: Experienced positions, especially in knowledge work, are increasingly affected by AI, with both positive (enhanced productivity) and negative (job displacement) implications. Productivity Gains from AI: AI can boost productivity by 20–80%, depending on integration depth within workflows.
2
2025-2026: Near Future
2025: Inflection point for AI and quantum computing. 2026: Affordable robots for cooking and cleaning (~£80–100k).
3
2028-2029: Looking Ahead
Deployment of robots across various sectors; advancements in cybernetics and fusion energy.
Challenges and Considerations