Artificial intelligence is a branch of computer science focused on creating systems capable of replicating human cognitive abilities such as reasoning, learning, and perception.
AI is built on rules, data, and the ability to adapt to new information:
| Characteristic | Academic Description |
|---|---|
| Ability to Seem Intelligent | Simulates intelligent behaviour without true consciousness. |
| Imitation of Human Behavior | Replicates perception, speech, reasoning, and translation. |
| Capacity to Learn | Learns from data through machine learning and deep learning. |
| Decision-Making Ability | Selects optimal actions using algorithms and criteria. |
| Adaptability | Adjusts outputs when exposed to dynamic data. |
| Goal-Oriented Operation | Operates based on defined objectives. |
| Problem-Solving Skills | Solves structured and unstructured problems. |
| Reasoning Ability | Uses inference and prediction. |
| Autonomy | Can operate with minimal human input. |
| Flexibility | Can be re-trained for different tasks. |
| Aspect | Strong AI | Weak AI |
|---|---|---|
| Cognitive Ability | Replicates real consciousness. | Performs tasks without understanding. |
| Independence | Autonomous reasoning. | Depends on rules. |
| Algorithmic Complexity | Advanced adaptive algorithms. | Simple algorithms. |
| Behavior | Learns beyond training. | Simulated intelligence. |
| Advantages | Disadvantages |
|---|---|
| Good at detail-oriented jobs. | Expensive implementation. |
| Reduced time for data-heavy tasks. | Requires deep technical expertise. |
| AI handles information better than humans. | Few efficient programmers are available to develop software to implement artificial intelligence. |
| AI-powered virtual agents are always available. | Only knows what it's been shown |
| Field | Examples |
|---|---|
| Industry | Robots, automation, precision manufacturing. |
| Education | Adaptive learning systems. |
| Medicine | AI diagnostics, assistants. |
| Gaming | Adaptive NPCs, procedural generation. |
| Society | Chatbots, assistants, face recognition. |
Machine learning enables systems to improve through experience.
Uses labelled data to predict outputs.
Uses unlabelled data to find hidden patterns.
Learns through rewards and penalties.
Uses multi-layer neural networks to detect complex patterns.
| Machine Learning | Deep Learning |
|---|---|
| Based on past data. | Based on neural networks. |
| Small datasets. | Requires huge datasets. |
| Manual feature extraction. | Automatic feature learning. |
| Modular system. | End-to-end model. |
| Longer testing. | Fast testing. |
| Explainable decisions. | Hard to interpret (“black box”). |
1.6 In which area of medicine is AI MOST commonly applied?
The use of Artificial Intelligence (AI) has been steadily increasing in various spheres of life. (a) Give one example of using AI in the following areas:
1) Medicine
AI is used in diagnostic systems that analyse medical images (such as MRI or CT scans) to detect diseases more accurately and efficiently than manual examination.
2) Gaming Industry
AI is applied to create adaptive non-player characters (NPCs) that respond intelligently to player actions, improving realism and gameplay experience.
3) Society
AI is used in automated decision-making systems, such as recommendation algorithms on social media and e-commerce platforms, which analyse user behaviour to personalise content.