Which statement best describes the relationship between Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL)?
Artificial Intelligence (AI) is the broadest field encompassing all technologies that enable machines to perform tasks that typically require human intelligence. Within AI, Machine Learning (ML) is a subset focused on the development of algorithms that allow systems to learn from and make predictions or decisions based on data. Deep Learning (DL) is a further subset of ML, characterized by the use of artificial neural networks with many layers (hence 'deep').
In this hierarchy:
AI includes all methods to make machines intelligent.
ML refers to the methods within AI that focus on learning from data.
DL is a specialized field within ML that deals with deep neural networks.
What is the benefit of using embedding models in OCI Generative AI service?
Embedding models in the OCI Generative AI service are designed to represent text, phrases, or other data types in a dense vector space, where semantically similar items are located closer to each other. This representation enables more effective semantic searches, where the goal is to retrieve information based on the meaning and context of the query, rather than just exact keyword matches.
The benefit of using embedding models is that they allow for more nuanced and contextually relevant searches. For example, if a user searches for 'financial reports,' an embedding model can understand that 'quarterly earnings' is semantically related, even if the exact phrase does not appear in the document. This capability greatly enhances the accuracy and relevance of search results, making it a powerful tool for handling large and diverse datasets .
Which capability is supported by the Oracle Cloud Infrastructure Vision service?
The Oracle Cloud Infrastructure (OCI) Vision service is designed for image analysis tasks, which includes the capability to detect and recognize objects, such as vehicle number plates. This functionality is particularly useful for applications such as automated enforcement of traffic laws, where the system can identify vehicles exceeding speed limits and issue citations based on the detected number plates. This capability leverages advanced computer vision techniques to process and analyze visual data, making it suitable for applications in public safety, transportation, and law enforcement.
You are working on a project for a healthcare organization that wants to develop a system to predict the severity of patients' illnesses upon admission to a hospital. The goal is to classify patients into three categories -- Low Risk, Moderate Risk, and High Risk -- based on their medical history and vital signs. Which type of supervised learning algorithm is required in this scenario?
In this healthcare scenario, where the goal is to classify patients into three categories---Low Risk, Moderate Risk, and High Risk---based on their medical history and vital signs, a Multi-Class Classification algorithm is required. Multi-class classification is a type of supervised learning algorithm used when there are three or more classes or categories to predict. This method is well-suited for situations where each instance needs to be classified into one of several categories, which aligns with the requirement to categorize patients into different risk levels.
What are Convolutional Neural Networks (CNNs) primarily used for?
Convolutional Neural Networks (CNNs) are primarily used for image classification and other tasks involving spatial data. CNNs are particularly effective at recognizing patterns in images due to their ability to detect features such as edges, textures, and shapes across multiple layers of convolutional filters. This makes them the model of choice for tasks such as object recognition, image segmentation, and facial recognition.
CNNs are also used in other domains like video analysis and medical image processing, but their primary application remains in image classification.
