True or False: Machine Learning vs Rule-Based Systems

A type of machine learning where an AI agent learns through trial and error, receiving rewards for desired actions and penalties for undesired actions. This is often used in scenarios where the goal is to learn an optimal strategy or policy.

A technique where multiple devices train a machine learning model collaboratively, but without sharing their raw data. This helps protect user privacy while allowing models to learn from diverse datasets.

A way to represent data in a structured and machine-readable format, using a subject-predicate-object structure to describe relationships between entities. This representation is well-suited for reasoning and knowledge extraction.

Ensuring that the AI system is designed and operated in a way that respects user privacy, minimizing the collection and storage of personal information, and providing users with clear control over their data.

A set of rules and guidelines that aim to ensure that AI systems are developed and used in a responsible, ethical, and safe manner. This includes addressing issues such as bias, transparency, and explainability.

Machine learning (ML) focuses on extracting patterns from data to make predictions or decisions. It learns from examples without explicit programming, allowing automated improvements over time.

Rule-based systems use predefined rules to process data. These rules represent human knowledge and expertise.

The primary goal of ML is to predict outcomes for new data based on patterns identified during training. This involves identifying relationships and trends within the data.

Classification in ML involves grouping data into predefined categories based on its characteristics. This can be used for tasks such as image recognition or spam detection.

Reinforcement learning (RL) focuses on training agents to interact with an environment and maximize rewards over time. It learns through trial and error by adjusting its actions based on feedback.

Unsupervised learning in ML involves discovering hidden patterns or structures in data without predefined labels. It allows for data exploration, clustering similar data points, and anomaly detection.

Supervised learning in ML uses labelled data to train models to make predictions on new data. This requires pre-preparing the data with both the features and desired outcomes.

Anomaly detection in ML involves identifying unusual or suspicious patterns in data that deviate from the norm.

Human effort is required to develop and refine rules for rule-based systems, as it involves translating real-world knowledge into formal rules.

Human effort is generally minimal in developing rules for ML algorithms. The process of learning patterns and making predictions is largely automated, relying on the data itself.

The primary goal of reinforcement learning (RL) is to maximize cumulative rewards achieved by an agent through interactions with an environment. This involves learning optimal strategies through trial and error.

RL does not require explicit labeling of data. Instead, it learns from feedback and rewards received during interactions with the environment.

Unsupervised learning minimizes human effort by enabling data exploration and pattern extraction without the need for pre-labeling.

Supervised learning typically requires significant human effort to label and prepare data for training models.

MR requires significant computation resources, especially when working with large models and complex datasets.

The ability to explain the reasoning behind generated results is crucial for ensuring trust and reliability in AI systems.

RL algorithms can generate their own policies or rules through interactions with the environment, enabling adaptive and autonomous behavior.

Supervised learning excels at generating highly accurate predictions when trained on large amounts of labeled data.

RL-based systems require rigorous testing and validation to ensure safety and stability, particularly in real-world applications.

Supervised learning requires data to be structured in a defined format, making it less suitable for unstructured data analysis.

Unsupervised learning requires significant effort in understanding and interpreting the results, as it often involves identifying patterns without pre-defined labels.

Classification is a powerful technique in AI for categorizing and classifying data points into distinct groups based on their features. This can be applied to tasks such as image recognition, spam detection, and customer segmentation.

Classification is commonly used in AI for categorizing and classifying data points into distinct groups based on their features.