1Z0-1122-23 Oracle Cloud Infrastructure 2023 AI Foundations Associate Questions and Answers

Oracle Cloud Infrastructure Speech is an AI service that applies automatic speech recognition (ASR) technology to transform audio-based content into text. Developers can easily make API calls to integrate Speech’s pretrained models into their applications. Speech can be used for accurate, text-normalized, time-stamped transcription via the console and REST APIs as well as command-line interfaces or SDKs. You can also use Speech in an OCI Data Science notebook session. With Speech, you can filter profanities, get confidence scores for both single words and complete transcriptions, and more1. References: Speech AI Service that Uses ASR | OCI Speech - Oracle

Supervised learning is a type of machine learning that uses labeled data sets to train algorithms that can classify data or predict outcomes. Labeled data sets are data sets that have both input features and output labels for each instance. For example, a labeled data set for image classification would have images as input features and the corresponding categories (such as dog, cat, bird, etc.) as output labels. Supervised learning algorithms learn the relationship between the input features and the output labels from the training data set and then use that relationship to make predictions on new or unseen data. Supervised learning can be divided into two subtypes: classification and regression. Classification is the task of assigning discrete categories to data instances, such as spam or not spam for emails. Regression is the task of predicting continuous values for data instances, such as house prices or stock prices. References: : Oracle Cloud Infrastructure AI - Machine Learning Concepts, What is Supervised Learning? | IBM

Large language models (LLMs) are a class of deep learning models that can recognize and generate natural language, among other tasks. LLMs are trained on huge sets of text data, learning grammar, semantics, and context. LLMs use the Transformer architecture, which relies on self-attention to process and understand the input and output sequences. LLMs can perform various natural language processing and understanding tasks based on the input provided, such as text summarization, question answering, text generation, and more34. Traditional machine learning models, on the other hand, are usually trained with specific statistical algorithms that deliver pre-defined outcomes. They often require labeled data and feature engineering, and they are not as flexible and adaptable as LLMs5. References: What are LLMs, and how are they used in generative AI?, An Introduction to LLMOps: Operationalizing and Managing Large Language Models using Azure ML, An Introduction to Large Language Models (LLMs): How It Got … - Labellerr

Natural Language Processing (NLP) is an AI domain that is associated with tasks such as identifying the sentiment of text and translating text between languages. NLP is an interdisciplinary field that combines computer science, linguistics, and artificialintelligence to enable computers to process and understand natural language data, such as text or speech. NLP involves various techniques and applications, such as:

• Text analysis: Extracting meaningful information from text data, such as keywords, entities, topics, sentiments, emotions, etc.
• Text generation: Producing natural language text from structured or unstructured data, such as summaries, captions, headlines, stories, etc.
• Machine translation: Translating text or speech from one language to another automatically and accurately.
• Question answering: Retrieving relevant answers to natural language questions from a knowledge base or a document collection.
• Speech recognition: Converting speech signals into text or commands.
• Speech synthesis: Converting text into speech signals with natural sounding voices.
• Natural language understanding: Interpreting the meaning and intent of natural language inputs and generating appropriate responses.
• Natural language generation: Creating natural language outputs that are coherent, fluent, and relevant to the context. References: : What is Natural Language Processing? | IBM, Natural language processing - Wikipedia

Oracle Cloud Infrastructure Vision is a serverless, multi-tenant service, accessible using the Console, or over REST APIs. You can upload images to detect and classify objects in them. If you have lots of images, you can process them in batch using asynchronous API endpoints. Vision’s features are thematically split between Document AI for document-centric images, and Image Analysis for object and scene-based images. Image Analysis supports both pretrained and custom models for object detection and image classification3. References: Vision - Oracle

Model training is the process of finding the optimal values for the model parameters that minimize the error between the model predictions and the actual output. This is done by using a learning algorithm that iteratively updates the parameters based on the input features and the output1. References: Oracle Cloud Infrastructure Documentation