Artificial Intelligence Types

Study Notes

Artificial intelligence (AI) refers to the development of computer systems that can perform tasks that typically require human intelligence, such as learning, problem-solving, and decision-making.
There are two main types of AI:
- Narrow or Weak AI: designed to perform a specific task, such as playing chess or recognizing faces.
- General or Strong AI: designed to perform any intellectual task, similar to human intelligence.

Machine learning is a subfield of AI that involves training algorithms to learn from data and make predictions or decisions.
Types of machine learning:
- Supervised learning: the algorithm is trained on labeled data to learn a mapping between input and output.
- Unsupervised learning: the algorithm is trained on unlabeled data to discover patterns or relationships.
- Reinforcement learning: the algorithm is trained to make decisions based on rewards or penalties.

Natural Language Processing (NLP): enables computers to understand, generate, and process human language.
Computer Vision: enables computers to interpret and understand visual data from images and videos.
Robotics: uses AI to control and navigate robots.
Expert Systems: uses AI to mimic human decision-making in specific domains, such as medicine or finance.

Neural Networks: modeled after the human brain, these networks consist of layers of interconnected nodes that process and transmit information.
Deep Learning: a type of neural network that uses multiple layers to learn complex patterns in data.
Rule-Based Systems: uses pre-defined rules to reason and make decisions.

Bias in AI: AI systems can perpetuate biases present in the data used to train them.
Explainability: it can be difficult to understand how AI systems arrive at their decisions.
Job displacement: AI may automate certain jobs, leading to job displacement.

Increased use of AI in industries such as healthcare, finance, and education.
Development of more advanced AI systems that can learn and adapt in complex environments.
Potential for AI to augment human capabilities and improve decision-making.

Artificial intelligence (AI) involves developing computer systems that mimic human intelligence in tasks like learning, problem-solving, and decision-making.
There are two main types of AI: Narrow or Weak AI, designed for specific tasks, and General or Strong AI, designed to perform any intellectual task.

Machine learning is a subfield of AI that involves training algorithms to learn from data and make predictions or decisions.
There are three types of machine learning: Supervised learning (trained on labeled data), Unsupervised learning (trained on unlabeled data), and Reinforcement learning (trained on rewards or penalties).

Natural Language Processing (NLP) enables computers to understand, generate, and process human language.
Computer Vision enables computers to interpret and understand visual data from images and videos.
Robotics uses AI to control and navigate robots.
Expert Systems use AI to mimic human decision-making in specific domains, such as medicine or finance.

Neural Networks are modeled after the human brain, consisting of layers of interconnected nodes that process and transmit information.
Deep Learning is a type of neural network that uses multiple layers to learn complex patterns in data.
Rule-Based Systems use pre-defined rules to reason and make decisions.

Bias in AI occurs when AI systems perpetuate biases present in the training data.
Explainability is a challenge, as it's often difficult to understand how AI systems arrive at their decisions.
Job displacement is a concern, as AI may automate certain jobs.

AI will increasingly be used in industries such as healthcare, finance, and education.
There will be a focus on developing more advanced AI systems that can learn and adapt in complex environments.
AI has the potential to augment human capabilities and improve decision-making.