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    SOURCE: Red Hat Developer

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    AI Fundamentals: Key Takeaways

    This document compiles the key takeaways from each lesson of the AI Fundamentals course.

    Lesson 1: Introduction

    • Part of what makes the incredible growth in AI possible is Moore's law, which states that the number of transistors in an integrated circuit doubles approximately every two years

    • AI is a branch of computer science that enables machines to perform tasks that typically require human intelligence.

    • Machine learning (ML) is a subcategory of AI that uses algorithms to identify patterns and make predictions within a set of data.

    Lesson 2: Predictive AI

    • Predictive AI is a common type of artificial intelligence system used in business applications that predicts or forecasts outcomes based on historical data.

    • Predictive AI is an integral part of many everyday activities such as conducting web searches, texting, shopping online, and engaging with video and music streaming services.

    • Data science is an interdisciplinary field that leverages mathematical, statistical, and computational techniques to extract knowledge and insights from structured and unstructured data.

    • Some of the tasks data scientists perform include data collection, data cleansing, model selection and training, and evaluation and validation of those models.

    • Examples of enterprises using predictive AI include logistic companies employing it to optimize delivery routes and prevent package theft, and banks or other financial institutions using it to identify fraud, money laundering, and other financial crimes.

    Lesson 3: Generative AI

    • The Turing test is a well-known experiment designed to assess the ability of a machine to exhibit intelligent behavior that is indistinguishable from that of a human. In the Turing test, a human evaluator reviews text from a conversation between two participants: a human and a machine. A machine is said to pass the test if the evaluator cannot reliably tell the human participant from the machine.

    • Generative AI is AI technology that relies on deep learning models trained on large data sets to create new content.

    • Deep learning is a specialized form of machine learning that teaches computers to process data by using an algorithm inspired by the human brain and neural networks. Deep learning teaches computers to learn through observation, imitating the way humans gain knowledge.

    • Some examples of generative AI include AI-generated summaries of customer reviews on Amazon, chatbots such as ChatGPT generating content based on a text prompt, AI-generated highlight reels from sporting events, and Red Hat's KCS Solution Summaries.

    • A large language model (LLM) is a type of AI program designed to understand and generate human language.

    • Creating an LLM from scratch requires a tremendous amount of money, expertise, and resources. For all but a handful of organizations, creating such a model is out of reach; most organizations are likely to start with a foundation model.

    • There are a variety of approaches an organization might use when working with a foundation model. One of the most popular approaches is retrieval-augmented generation (RAG), a method that involves getting better answers from a generative AI application by linking an LLM to an external resource.

    • There are several issues associated with LLMs, including massive energy and resource costs, a lack of transparency with regard to how models are trained, biases in model training, hallucinations, data recency, and issues related to copyright and privacy.

    Standalone AI

    Standalone AI refers to AI frameworks and platforms that can be used independently to build and deploy AI models. These tools often provide a comprehensive set of features for developing AI applications from scratch.

    Type: Open-source machine learning framework.\ Popularity: Widely used for deep learning and machine learning tasks.\ Open-source: Yes.

    • PyTorch

    Type: Open-source deep learning framework.\ Popularity: Popular among researchers and developers for its flexibility and ease of use.\ Open-source: Yes.

    • IBM Watson

    Type: Enterprise AI platform.\ Popularity: Used for various AI applications including natural language processing and data analytics.\ Open-source: No.

    Integrated AI

    Integrated AI refers to AI services that are integrated into larger platforms or ecosystems, often provided by cloud service providers. These services are designed to be easily integrated into existing applications and workflows.

    • Google AI Services

    Type: Integrated AI services including vision, speech, and language processing.\ Popularity: Widely used for integrating AI capabilities into applications.\ Open-source: No.

    • Amazon Web Services (AWS) AI

    Type: Integrated AI services including machine learning, natural language processing, and computer vision.\ Popularity: Popular for cloud-based AI solutions.\ Open-source: No.

    • Microsoft Azure AI

    Type: Integrated AI services including cognitive services, machine learning, and bot services.\ Popularity: Used for a variety of AI applications in the cloud.\ Open-source: No.

    Custom AI

    Custom AI refers to AI libraries and platforms that allow for the creation of custom AI models tailored to specific needs. These tools often provide flexibility and control over the AI development process.

    • Hugging Face Transformers

    Type: Open-source library for state-of-the-art Natural Language Processing (NLP).\ Popularity: Widely used for building custom NLP models.\ Open-source: Yes.

    • Apache MXNet

    Type: Open-source deep learning framework.\ Popularity: Used for building custom deep learning models.\ Open-source: Yes.

    • Seldon

    Type: Open-source platform for deploying machine learning models.\ Popularity: Used for deploying and managing custom AI models at scale.\ Open-source: Yes.

    Todo List

    • [[RAG]] - Retrieval-augmented generation "RAG"
      • retrieval: retrieve relevant information from a knowledge base with text embeddings stored in a vector store
      • generation: insert the relevant information to the prompt for the LLM to generate information
    • [[Function calling]] - enables models to connect to external tools
    • [[Text generation]] - enables streaming and provides the ability to display partial model results in real-time
    • [[Prompt engineering]] - enables developers to create customized and specilized models
    • [[Embeddings]] - useful for RAG where it represents the meaning of text as a list of numbers
    • [[JSON mode]] - enables developers to set the response format to json_object
    • [[Guardrailing]] - enables developers to enforce policies at the system level of models
    • [[AI Model Deployment]]

    Note Created: 2025-01-06

    Projects
    - [[AI Journey]]
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