Computer Vision is an AI domain that is associated with tasks such as recognizing faces in images and classifying objects. Computer vision is a field of artificial intelligence that enables computers and systems to derive meaningful information from digital images, videos, and other visual inputs, and to take actions or make recommendations based on that information. Computer vision works by applying machine learning and deep learning models to visual data, such as pixels, colors, shapes, textures, etc., and extracting features and patterns that can be used for various purposes. Some of the common techniques and applications of computer vision are:

Face recognition: Identifying or verifying the identity of a person based on their facial features.

Object detection: Locating and labeling objects of interest in an image or a video.

Object recognition: Classifying objects into predefined categories, such as animals, vehicles, fruits, etc.

Scene understanding: Analyzing the context and semantics of a visual scene, such as the location, time, weather, activity, etc.

Image segmentation: Partitioning an image into multiple regions that share similar characteristics, such as color, texture, shape, etc.

Image enhancement: Improving the quality or appearance of an image by applying filters, transformations, or corrections.

Image generation: Creating realistic or stylized images from scratch or based on some input data, such as sketches, captions, or attributes.Reference::What is Computer Vision? | IBM,Computer vision - Wikipedia

Prompt engineering is the art of designing natural language instructions or queries that can elicit the desired response from a large language model. Prompt engineering does not modify the model parameters or architecture, but rather relies on the model's existing knowledge and capabilities.Prompt engineering can be used to perform various tasks such as text generation, sentiment analysis, and code completion, by providing the model with the appropriate context, format, and constraints67. Prompt engineering is also known as zero-shot learning or query-based learning.Reference:[2211.01910] Large Language Models Are Human-Level Prompt Engineers](https://arxiv.org/abs/2211.01910),A developer's guide to prompt engineering and LLMs - The GitHub Blog

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 artificial intelligence 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.Reference::What is Natural Language Processing? | IBM,Natural language processing - Wikipedia

Reinforcement learning is a type of machine learning that is based on learning from outcomes to make decisions. Reinforcement learning algorithms learn from their own actions and experiences in an environment, rather than from labeled data or explicit feedback. The goal of reinforcement learning is to find an optimal policy that maximizes a cumulative reward over time. A policy is a rule that determines what action to take in each state of the environment. A reward is a feedback signal that indicates how good or bad an action was for achieving a desired objective. Reinforcement learning involves a trial-and-error process of exploring different actions and observing their consequences, and then updating the policy accordingly. Some of the challenges and components of reinforcement learning are:

Exploration vs exploitation: Balancing between trying new actions that might lead to higher rewards in the future (exploration) and choosing known actions that yield immediate rewards (exploitation).

Markov decision process (MDP): A mathematical framework for modeling sequential decision making problems under uncertainty, where the outcomes depend only on the current state and action, not on the previous ones.

Value function: A function that estimates the expected long-term return of each state or state-action pair, based on the current policy.

Q-learning: A popular reinforcement learning algorithm that learns a value function called Q-function, which represents the quality of taking a certain action in a certain state.

Deep reinforcement learning: A branch of reinforcement learning that combines deep neural networks with reinforcement learning algorithms to handle complex and high-dimensional problems, such as playing video games or controlling robots.Reference::Reinforcement learning - Wikipedia,What is Reinforcement Learning? -- Overview of How it Works - Synopsys

Oracle Cloud Infrastructure Language service is a cloud-based AI service for performing sophisticated text analysis at scale. It provides various capabilities to process unstructured text and extract structured information like sentiment or entities using natural language processing techniques. Some of the capabilities supported by Oracle Cloud Infrastructure Language service are:

Language Detection: Detects languages based on the provided text, and includes a confidence score.

Text Classification: Identifies the document category and subcategory that the text belongs to.

Named Entity Recognition: Identifies common entities, people, places, locations, email, and so on.

Key Phrase Extraction: Extracts an important set of phrases from a block of text.

Sentiment Analysis: Identifies aspects from the provided text and classifies each into positive, negative, or neutral polarity.

Text Translation: Translates text into the language of your choice.

Personal Identifiable Information: Identifies, classifies, and de-identifies private information in unstructured textReference::Language Overview - Oracle,AI Text Analysis at Scale | Oracle