Practice Free 1z0-1122-23 Oracle Cloud Infrastructure 2023 AI Foundations Associate Exam Questions Answers With Explanation

Generative AI is a branch of artificial intelligence that focuses on creating new content or data based on the patterns and structure of existing data. Unlike other AI approaches that aim to recognize, classify, or predict data, generative AI aims to generate data that is realistic, diverse, and novel. Generative AI can produce various types of content, such as images, text, audio, video, software code, product designs, and more. Generative AI uses different techniques and models to learn from data and generate new examples, such as generative adversarial networks (GANs), variational autoencoders (VAEs), diffusion models, and foundation models. Generative AI has many applications across different domains and industries, such as art, entertainment, education, healthcare, engineering, marketing, and more. References: : Oracle Cloud Infrastructure AI - Generative AI, Generative artificial intelligence - Wikipedia

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

A graphics processing unit (GPU) is a specialized hardware component that can perform parallel computations on large amounts of data. GPUs are widely used in deep learning to accelerate the training of deep neural networks, as they can execute many matrix operations and tensor operations simultaneously. GPUs can significantly reduce the training time and improve the performance of deep learning models compared to using CPUs alone678. References: Hardware Recommendations for Machine Learning / AI, New hardware offers faster computation for artificial intelligence …, The Best Hardware for Machine Learning - ReHack, Hardware for Deep Learning Inference: How to Choose the Best One for …

 In-context learning is a technique that leverages the ability of large language models to learn from a few input-output examples provided in the input prompt. By conditioning on these examples, the model can infer the task and the format of the desired output, and generate a suitable response. In-context learning does not require any additional training or fine-tuning of the model, and can be used for various tasks such as text summarization, question answering, text generation, and more45. In-context learning is also known as few-shot learning or prompt-based learning. References: [2307.12375] In-Context Learning in Large Language Models Learns Label …](https://arxiv.org/abs/2307.12375), [2307.07164] Learning to Retrieve In-Context Examples for Large Language Models] (https://arxiv.org/abs/2307.07164)