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)

The attention mechanism in the Transformer architecture is a technique that allows the model to focus on the most relevant parts of the input and output sequences. It computes a weighted sum of the input or output embeddings, where the weights indicate how much each word contributes to the representation of the current word. The attention mechanism helps the model capture the long-range dependencies and the semantic relationships between words in a sequence12. References: The Transformer Attention Mechanism - MachineLearningMastery.com, Attention Mechanism in the Transformers Model - Baeldung

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

Text to speech (TTS) is an AI task that involves audio generation from text. TTS is a technology that converts text into spoken audio using natural sounding voices. TTS can read aloud any text data, such as PDFs, websites, books, emails, etc., and provide an auditory format for accessing written content. TTS can be helpful for anyone who needs to listen to text data for various reasons, such as accessibility, convenience, multitasking, learning, entertainment, etc. TTS uses different techniques and models to generate speech from text data, such as:

• Concatenative synthesis: Combining pre-recorded segments of human speech based on the phonetic units of the text.
• Parametric synthesis: Generating speech signals from acoustic parameters derived from the text using statistical models.
• Neural synthesis: Using deep neural networks to learn the mapping between text and speech features and produce high-quality speech signals.
• Expressive synthesis: Adding emotions or styles to the speech output to make it more natural and engaging. References: : Text-to-Speech AI: Lifelike Speech Synthesis | Google Cloud, Text-to-speech synthesis - Wikipedia

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 text References: : Language Overview - Oracle, AI Text Analysis at Scale | Oracle

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