Artificial Intelligence (AI) Fundamentals

Questions and Answers

To exclusively process large datasets

To automate repetitive tasks without any analysis

To perform tasks requiring human intelligence (correct)

To enhance human cognitive abilities

AI focused on performing a specific task (correct)

AI that can learn independently without data

AI designed for general problem solving across tasks

AI with human-like intelligence and capabilities

Learning through trial and error with feedback

Learning without any form of data input

Learning from labeled data to make predictions (correct)

Learning from unlabeled data to find hidden patterns

The model becoming too specialized to training data Signup and view all the answers

Language translation and predictive modeling Signup and view all the answers

Interconnected nodes designed to process inputs and outputs Signup and view all the answers

Gradient Descent Signup and view all the answers

To evaluate a model's performance on unseen data Signup and view all the answers

The compromise between two errors in model performance Signup and view all the answers

Identifying patterns in unlabeled data Signup and view all the answers

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.