Navigating the AI Landscape

1. Machine Learning (ML)

The Foundation (Pattern Recognition)

At the base level, ML is the subset of AI where computers learn from data without being explicitly programmed. In a business context, this is your "pattern recogniser". It powers your credit scoring, fraud detection, and recommendation engines.

2. Inference AI

The Execution (Real-World Application)

Inference AI refers to the process where a trained artificial intelligence model applies its learned knowledge to new, unseen data to make predictions, decisions, or generate outputs. It is the "execution phase" of AI where the model moves from learning to real-world application.

During inference, the model performs a "forward pass". It processes input data through its neural network layers using fixed parameters (weights) to produce an output. Examples include identifying objects in an image, generating text in response to a prompt (e.g. ChatGPT), detecting fraud in a financial transaction, or recognising speech in a voice assistant.

Unlike training, which is compute-intensive and involves adjusting model weights, inference is optimised for speed, low latency, and efficiency. It often runs on edge devices like smartphones or IoT hardware.

3. Natural Language Processing (NLP)

The Interface (Human Understanding)

Moving up the curve, NLP enables machines to understand, interpret, and generate human language. From sentiment analysis on social media to automated contract review in legal departments, NLP unlocks the data trapped in text and voice, bridging the gap between database logic and human communication.

4. Generative AI (GenAI)

The Creator (New Output)

Unlike traditional AI which analyses existing data, GenAI creates new content: text, code, images, or strategies. Tools like GPT-4 are transforming how proposals are written, how code is deployed, and how marketing copy is personalised at scale. It represents a leap in sophistication, moving from "reading" data to "writing" new value.

5. Agentic AI

The Autonomous Actor (Reasoning & Planning)

This is the cutting edge. Agentic AI refers to systems that can autonomously reason, plan, and act to achieve goals with minimal human intervention. Unlike traditional or generative AI, agentic AI does not just respond to prompts; it proactively perceives its environment, sets objectives, executes actions, and learns from outcomes in a continuous loop.

For example, while generative AI might draft an email, agentic AI can monitor customer issues, prioritise them, and resolve them end-to-end using connected systems.

6. Artificial General Intelligence (AGI)

The Future Horizon (Human Parity)

The final frontier. AGI represents a theoretical future where a machine possesses the ability to understand, learn, and apply knowledge across a wide variety of tasks, indistinguishable from a human.