Image source: YouTube
Table of Contents
Introduction - AI, ML, DL, and GenAI
- Artificial Intelligence (AI): The broader field of creating intelligent agents, aiming to mimic human intelligence.
- Machine Learning (ML): A subset of AI focused on teaching computers to learn from data without explicit programming.
- Deep Learning (DL): A subset of ML using artificial neural networks to analyze complex patterns in data.
- Generative AI (Gen AI): A subfield focused on creating new content, such as text, images, or code, based on learned patterns.
InstructLab is a game-changer for enhancing large language models (LLMs). This open-source project, co-created by IBM and Red Hat, makes it easier to align LLMs with user intent, opening up possibilities for innovative AI applications.
Podman AI Lab is Red Hat’s answer to simplifying the AI development process. This extension provides a local environment with essential open-source tools and curated recipes to guide you through building AI solutions.
Red Hat OpenShift AI is a powerful platform for deploying and scaling AI applications across hybrid cloud environments. Built on open-source technologies, it offers a trusted foundation for teams to experiment, serve models, and deliver innovative AI-driven apps.
AI Knowledge vs Skill
Knowledge in AI:
- Knowledge represents the information and data that an AI system has access to, including facts, rules, and concepts about the world or a specific domain. It’s the foundational material that the AI uses to make decisions, predictions, and perform tasks.
- AI systems accumulate knowledge through data training (e.g., large datasets of text, images, or structured data) and can retrieve and apply this knowledge when required.
- Knowledge can be encoded as rules (in rule-based systems) or derived from patterns in data (in machine learning systems).
Examples:
- A language model like GPT is trained on vast amounts of text data, providing it with knowledge about grammar, facts, and even context.
- A medical diagnostic AI might have knowledge about symptoms, diseases, and treatments stored in its database or learned from medical literature.
Skill in AI:
- Skill refers to the ability of an AI to apply knowledge effectively to perform tasks or solve problems. It involves leveraging knowledge in a way that demonstrates proficiency in a specific task or set of tasks.
- AI develops skills through training, experience, and fine-tuning. Skill in AI can be thought of as the execution part, where the system demonstrates its capability to solve real-world problems using the knowledge it possesses.
Examples:
- A self-driving car’s ability to navigate roads by understanding traffic rules and reacting to real-time conditions.
- A machine learning model’s skill to recognize objects in images, using knowledge about how certain objects appear.
Relationship Between Knowledge and Skill in AI:
- Knowledge is the foundation, while skill is the application of that knowledge.
- An AI system may have vast knowledge but limited skill if it cannot effectively use the information to solve tasks.
- Conversely, a highly skilled AI must be backed by a strong knowledge base to perform its tasks accurately and consistently.
Glossary
- LAB - Large-scale Alignment for chatBots
- LLM - Large Language Models
- Gen AI - Generative Artificial Intelligence
Some facts
- Llama2–70B model — a large language model released by Meta.ai.
References
- InstructLab Project
- InstructLab Taxonomy Repo
- InstructLab Repo
- JJ and Paul continue introducing and getting started with InstructLab (2024) (Video)
- InstructLab – “Ever imagined the ease of tuning pre-trained LLMs? InstructLab makes it a reality. Let’s delve into how it sets itself apart from other model tuning methods.” (Blog)
- Generative AI Development with Podman AI Lab, InstructLab, & OpenShift AI (Video)