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Generative AI and Large Language Models

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- Overview

Generative AI (GenAI) is a type of artificial intelligence (AI) that can use models or algorithms to create new content. These models learn patterns in training data and then generate new data with similar characteristics. New content can be text, images, videos, code, data, or 3D renderings.

Generative AI can produce a variety of novel content, such as: Images, video, music, voice, text, software code, product design. Some examples of generative AI tools include: GPT-4, ChatGPT, AlphaCode, GitHub Copilot, Bard.

For example, you can use generative AI in Google searches by typing a query and clicking the Generate button above the standard results.

The European Union has warned that generative AI tools could pose risks to free and fair debate in democratic societies.


- Generative AI and Large Language Models (LLMs)

Generative AI applications are built on top of large language models (LLMs) and foundation models. 

LLMs are a type of AI program that can recognize and generate text. They are trained on huge sets of data, such as trillions of words, across many natural-language tasks. LLMs are built on ML, specifically a type of neural network called a transformer model. 

LLMs are proficient in generating text, producing fluent, succinct, and precise linguistic expressions. They are also proficient in language comprehension tasks, such as sentiment analysis, text categorization, and processing factual input. 

Generative AI is a broader concept of AI systems capable of generating various types of content. It is a pinnacle achievement, particularly in the intricate domain of Natural Language Processing (NLP). 

LLMs provide context and memory capabilities, while generative AI enables the production of engaging responses. This results in more natural, humanlike, interactive conversations.


- Foundation Models (FMs)

Generative AI and large language models (LLMs) are both types of AI that use deep learning and neural networks. 

Generative AI is a broad category that refers to any AI that can create original content. Generative AI's main goal is to mimic and enhance human creativity while pushing the limits of what is achievable with AI-generated content. Generative AI is powered by very large ML models that are pre-trained on vast amounts of data, commonly referred to as foundation models (FMs).

LLMs are a subset of FMs that are trained on trillions of words across many natural-language tasks. LLMs are specialized AI models created to comprehend and produce text-based content. LLMs can decipher the nuances of language, while generative AI can create accurate translations and localized versions of the content.

LLMs can be utilized alongside generative AI models to improve content translation and localization.


- Generative AI

Generative AI (GenAI) is a type of artificial intelligence that can create a variety of data, such as images, video, audio, text, and 3D models. It does this by learning patterns from existing data and then using this knowledge to generate new and unique outputs. 

GenAI's ability to generate highly realistic and complex content that mimics human creativity makes it an invaluable tool in many industries, including gaming, entertainment, and product design. 

Recent breakthroughs in this field, such as GPT (Generative Pre-Training Transformer) and Midjourney, have significantly improved GenAI's capabilities. These advances open up new possibilities for using GenAI to solve complex problems, create art, and even assist in scientific research.

Within the AI discipline, GenAI is a catalyst that enables businesses to create, iterate and optimize solutions to complex problems. The growing interest in GenAI provides an excellent opportunity to explore its potential and gain insights into its capabilities for transformative business applications.


- Large Language Model (LLM) AI

Large language model (LLM) AI is a term that refers to AI models that can generate natural language text from large amounts of data. Large-scale language models use deep neural networks (such as Transformers) to learn from billions or trillions of words and generate text on any topic or domain. 

LLMs can also perform various natural language tasks such as classification, summarization, translation, generation, and dialogue. Some examples of large language models include GPT-3, BERT, XLNet, and EleutherAI.

The popular ChatGPT system is powered by the LLM AI model invented by OpenAI based on the GPT-3 model. You can think of ChatGPT as an application built on top of LLM AI, specially tuned for interactive chat.  


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- Generative AI vs. Predictive AI

Generative AI and predictive AI are two different approaches to artificial intelligence. Generative AI focuses on creating new content, while predictive AI focuses on making accurate predictions.

Generative AI uses deep learning and machine learning to generate content such as images, music, and text. It can also convert data into different formats.

Predictive AI analyzes existing data to make predictions, recommendations, and decisions. It uses various artificial intelligence and machine learning techniques. Predictive AI is commonly used in industries such as finance, healthcare, and marketing.

Predictive AI enables informed decision-making, cost reduction, and risk mitigation. It can also predict a company's future needs or events, such as foreseeing upcoming trends or predicting risks and their solutions.


- Generative Integration

Generative integration is an advanced approach to data and application integration that leverages generative artificial intelligence and large language models (LLMs). 

This innovative approach securely automates the creation of integration pipelines, simplifying the process of connecting disparate systems and data sources. 

By leveraging artificial intelligence and machine learning capabilities, generative integration can understand, interpret, and generate code, significantly reducing manual workload and increasing the efficiency and accuracy of data integration tasks.


- Trillion-Parameter Models

What is the interest in trillion-parameter models? We know many of the use cases today and interest is growing due to the promise of an increased capacity for:

  • Natural language processing tasks like translation, question answering, abstraction, and fluency.
  • Holding longer-term context and conversational ability.
  • Multimodal applications combining language, vision, and speech.
  • Creative applications like storytelling, poetry generation, and code generation.
  • Scientific applications, such as protein folding predictions and drug discovery.
  • Personalization, with the ability to develop a consistent personality and remember user context.

The benefits are huge, but training and deploying large models can be computationally and resource intensive. Computationally efficient, cost-effective, and energy-efficient systems designed to provide on-the-fly inference are critical for widespread deployment.


- Tokens and Parameters

In AI and ML, the terms "token" and "parameter" are often used interchangeably, but they have different meanings and roles in model training.

Tokens represent the smallest unit of data processed by the model, such as a word or character in natural language processing. 

Parameters, on the other hand, are internal variables that the model adjusts during training to improve its performance. Both tokens and parameters are key elements in model training, but they serve different purposes and significantly impact the model's accuracy and overall performance.


[More to come ...]

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