Artificial Intelligence (AI) Fundamentals

Study Notes

Definition: AI refers to the development of computer systems that can perform tasks that typically require human intelligence, such as visual perception, speech recognition, decision-making, and language translation.
Types of AI:
- Narrow or Weak AI: designed to perform a specific task, e.g. facial recognition, language translation
- General or Strong AI: capable of performing any intellectual task, e.g. human-like intelligence
- Superintelligence: significantly more intelligent than the best human minds

Definition: ML is a subset of AI that involves training algorithms to learn from data and improve their performance over time, without being explicitly programmed.
Types of ML:
- Supervised Learning: algorithms learn from labeled data to make predictions
- Unsupervised Learning: algorithms identify patterns in unlabeled data
- Reinforcement Learning: algorithms learn from feedback to make decisions
ML Applications:
- Image and speech recognition
- Natural Language Processing (NLP)
- Predictive analytics and modeling
- Robotics and autonomous systems

Model: a set of algorithms and data structures that enable a machine to learn from data
Training: the process of feeding data to a model to enable it to learn
Testing: the process of evaluating a model's performance on new, unseen data
Overfitting: when a model becomes too specialized to the training data and fails to generalize well to new data
Bias-Variance Tradeoff: the balance between the error introduced by simplifying a model (bias) and the error introduced by overfitting to the training data (variance)

Neural Networks: modeled after the human brain, these networks consist of interconnected nodes (neurons) that process inputs and produce outputs
Decision Trees: a tree-like model that splits data into subsets based on features and predicts outcomes
Clustering: grouping similar data points together to identify patterns and relationships
Gradient Descent: an optimization algorithm used to minimize the loss function and find the optimal model parameters

AI involves the creation of systems that emulate human intelligence capabilities like perception, speech, decision-making, and translation.
Types of AI:
- Narrow or Weak AI: Tailored for specific tasks, such as image recognition and translation.
- General or Strong AI: Possesses the ability to perform any intellectual task comparable to human cognition.
- Superintelligence: Represents an intelligence level far exceeding that of the best human capabilities.

ML is a branch of AI that focuses on developing algorithms that learn from data and enhance performance autonomously.
Types of ML:
- Supervised Learning: Utilizes labeled datasets to train models for making predictions.
- Unsupervised Learning: Operates on unlabeled data to discern patterns or groupings.
- Reinforcement Learning: Involves learning through trial and error, gaining feedback to enhance decision-making.
Applications of ML:
- Employed in image and speech recognition systems.
- Facilitates Natural Language Processing (NLP) to interpret and generate human language.
- Powers predictive analytics for forecasting trends and behaviors.
- Vital for robotics and autonomous systems, enabling them to operate efficiently in real-world settings.

Model: A systematic configuration of algorithms and data structures aiding machines in learning from input data.
Training: Involves providing data to a model, allowing it to learn and recognize patterns.
Testing: Assesses a model's effectiveness through evaluation on previously unseen data.
Overfitting: Occurs when a model is overly tailored to training data, impairing its ability to perform on new data.
Bias-Variance Tradeoff: Represents the compromise between underfitting (bias) and overfitting (variance) in model performance.

Neural Networks: Inspired by the human brain, these networks consist of layers of interconnected nodes (neurons) that process input data and generate responses.
Decision Trees: A branching model that categorizes data inputs based on specific attributes to forecast outcomes.
Clustering: The method of organizing similar data points into groups to reveal intrinsic patterns and relationships.
Gradient Descent: An optimization strategy aimed at reducing errors by adjusting model parameters for improved accuracy.