Page 81 - Ai V2.0 Flipbook C8
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Systems Thinking
Systems Thinking is an approach to understanding the world by focusing on how different
parts of a system are connected and interact with one another. When applied to AI, it means
recognising how elements like data, algorithms, and results work together and affect the larger
environment. This broad perspective helps develop AI solutions that are better suited to tackle
complex issues in society.
AI Project Cycle
The AI Project Cycle is like a step-by-step guide that helps us build an AI solution—from identifying
a problem to making sure the final system keeps getting better. Think of it as a journey where
each stage has a clear goal.
The stages of the AI Project Cycle include:
Stage 1: Problem Scoping
Define precisely what the AI should accomplish and why it is important. Establish success criteria
and identify any constraints such as time, budget, or ethical considerations like fairness.
Stage 2: Data Collection
Collect a large amount of relevant, high-quality data for the system to learn from—this can
include images, sounds, text, numbers, sensor data, and more.
Stage 3: Data Exploration
Examine, clean, and organise the data. Remove errors, add useful labels, and look for patterns or
missing information. Decide which features (key pieces of information) will be most valuable for
the model.
Stage 4: Model Training
Use the prepared data to train a learning algorithm, enabling the system to recognise patterns
and create a model to solve the problem.
Stage 5: Evaluation
Assess the trained model using new, unseen data to evaluate its performance. Check metrics
such as accuracy, speed, fairness, and watch out for unintended errors or bias.
Stage 6: Deployment and Improvement
Deploy the model in a real-world setting — this could be within an app, robot, website, or project.
Continuously monitor its performance, gather feedback, and update or retrain the model when
errors are found or circumstances change.
AI at a Glance 79

