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AI – In Four Parts

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Part 1 – What Is AI and How Does It Work?

Artificial Intelligence (AI) is a term that often conjures up images of futuristic robots or complex algorithms. But at its core, AI is about machines performing tasks that typically require human intelligence: understanding language, recognising patterns, solving problems, and making decisions.

Defining AI

AI encompasses a range of technologies that enable computers to perform functions such as:

  1. • Learning from Experience: Adjusting actions based on previous outcomes.
  2. • Understanding Language: Comprehending and processing human languages.
  3. • Recognizing Patterns: Identifying trends or regularities in data.
  4. • Making Decisions: Choosing actions based on data analysis.

In essence, AI systems are designed to mimic human cognitive functions, allowing them to operate autonomously in complex environments.

AI vs. Machine Learning vs. Deep Learning.

These terms are interconnected but represent different concepts:
• Artificial Intelligence (AI): The overarching field focused on creating systems capable of intelligent behaviour.
• Machine Learning (ML): A subset of AI where algorithms learn from data to make predictions or decisions without explicit programming.
• Deep Learning (DL): A further subset of ML that uses neural networks with multiple layers to analyse various factors of data.

Visualising the Relationship:

+—————————+
| Artificial Intelligence |
| +———————+ |
| | Machine Learning | |
| | +—————+ | |
| | | Deep Learning | | |
| | +—————+ | |
| +———————+ |
+—————————+

How AI Learns

AI systems learn through data exposure:
1. Data Collection: Gathering relevant information.
2. Data Processing: Cleaning and organizing data for analysis.
3. Algorithm Application: Using mathematical models to identify patterns.
4. Training: Adjusting models based on data to improve accuracy.
5. Inference: Applying trained models to new data to make predictions or decisions.

• AI: Encompasses all intelligent machines.
• ML: Involves machines improving performance through data.
• DL: utilises complex neural networks for deeper data analysis.

For example, to teach an AI to recognise cats:

  1. • Collect Images: Amass a large dataset of cat images.
  2. • Process Images: Standardise image sizes and formats.
  3. • Apply Algorithms: Use models to detect features like fur patterns or ear shapes.
  4. • Train the Model: Adjust parameters to correctly identify cats.
  5. • Test and Refine: Validate the model with new images and refine as needed.

This iterative process enables AI systems to continuously improve their performance over time.

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