An Introduction to AI LLMs & SharePoint For Champions and Super Users Part 1

This is part 1 of an 8-part introductory course for SharePoint Champions and Superusers focusing on integrating Large Language Models (LLMs) into corporate environments. Section 1 introduces LLMs, covering their definition, history, and capabilities. It explores how LLMs work, their impact across industries, and current limitations. The section also discusses popular LLM examples and future directions in the field, setting the foundation for understanding their potential in SharePoint contexts.

The course then takes a look at using online LLMs, local LLM deployment for corporate use, and the intricate process of installing and configuring these models. It provides detailed guidance on integrating LLMs with SharePoint, exploring various applications such as enhanced search, automated content tagging, and intelligent document processing. The later sections cover best practices and governance for LLM-enhanced SharePoint environments, addressing crucial aspects like data privacy, ethical considerations, and user adoption strategies.

The course concludes by examining future trends and considerations, preparing participants for the evolving landscape of AI-enhanced knowledge management. Throughout, it emphasizes practical applications, challenges, and solutions, equipping SharePoint Champions and Superusers with the knowledge to leverage LLMs effectively within their organizations.

Yes, most of it was written by an LLM.

1. AI LLMs & SharePoint Using Large Language Models (LLMs) with SharePoint within the corporate firewall Part 1: A brief introduction to Large Language Models

2. Introduction to Large Language Models Definition and basic concepts Brief history and evolution Capabilities and limitations

3. Definition and Basic Concepts What are Large Language Models (LLMs)? Key characteristics of LLMs How LLMs differ from traditional NLP models

4. What are Large Language Models (LLMs)? Large Language Models (LLMs) are advanced artificial intelligence systems designed to understand, generate, and manipulate human language. These models are trained on vast amounts of text data, allowing them to capture intricate patterns and nuances in language.

5. Key characteristics of LLMs Massive scale: Typically containing billions of parameters Generative capabilities: Able to produce human-like text Contextual understanding: Can interpret and respond to complex prompts

6. How LLMs differ from traditional NLP models NLP – Natural Language Processing LLMs differ from traditional NLP models in their scale, versatility, and ability to perform a wide range of language tasks without task-specific training.

7. Brief History and Evolution Early language models and their limitations Breakthrough developments (e.g., transformer architecture) Major milestones in LLM development (e.g., BERT, GPT series)

8. Early language models and their limitations Lack of context understanding Limited vocabulary No common sense No understanding of figurative language Lack of emotional intelligence

9. Breakthrough developments (e.g., transformer architecture) Transformer Architecture Self-Attention Mechanism Pre-training on Large Datasets Adversarial Training Multitask Learning

10. Major milestones in LLM development Transformer Architecture BERT and its Variants Pre-training and Fine-tuning Multitask Learning and Transfer Learning Attention-Based Mechanisms

11. How LLMs Work Overview of neural network architecture Training process: unsupervised learning on vast text corpora Concept of "understanding" in LLMs

12. Overview of neural network architecture “At their core, most modern LLMs use transformer architectures, which allow for parallel processing of input data and capture long-range dependencies in text.” What does THAT mean?

13. What is a Transformer Architecture? A transformer architecture is a type of artificial intelligence (AI) model that allows computers to process and analyze large amounts of data quickly and efficiently. It's like a super-powerful, ultra-fast librarian that can find connections between different pieces of information.

14. How does it work? Imagine you're reading a long book. As you read, you might notice that certain words or phrases keep appearing throughout the text, even if they're on different pages. A transformer architecture is designed to help computers do the same thing – it looks for patterns and connections between different parts of a large piece of data (like text).

15. Parallel Processing One of the key features of transformers is their ability to process multiple pieces of information at the same time, or "in parallel." This means that instead of reading the book page by page, the computer can look at multiple pages simultaneously and find connections between them.

16. Long-range Dependencies Transformers are also great at capturing "long-range dependencies" in data. What does this mean? Well, imagine you're trying to understand a joke. The punchline might not make sense until you've heard the setup and the context of the entire joke – it's not just about individual words or phrases, but how they all fit together. Transformers can capture these long-range dependencies by looking at large chunks of data and finding patterns that connect different parts.

17. Summary - What is a Transformer Architecture? in short, modern LLMs (Large Language Models) use transformer architectures to process and analyze text quickly and efficiently. This allows them to find connections between different pieces of information, even if they're far apart – which is super helpful for tasks like language translation, text summarization, and more!

18. Training process How LLMs Learn Large Language Models (LLMs) learn by reading lots of text from the internet, books, and articles. This helps them understand how language works. The Training Process The model tries to predict what word comes next in a sentence or paragraph. As it makes more predictions, it gets better at understanding patterns in language. Think of it like learning a new language by reading lots of texts, newspapers, and books. You start to recognize common phrases, sentence structures, and even idioms! The LLM is doing something similar, but with computers and algorithms.

19. Concept of "understanding" in LLMs The concept of "understanding" in LLMs is a subject of debate. While they can produce remarkably human-like responses, their "understanding" is based on statistical patterns rather than true comprehension.

20. Capabilities of LLMs Natural language understanding and generation Translation and multilingual capabilities Text summarization and paraphrasing Question answering and information retrieval Code generation and analysis

21. Natural language understanding and generation Question Answering and Reading Comprehension Text Generation and Summarization Conversational AI and Dialogue Systems

22. Translation and multilingual capabilities Neural Machine Translation (NMT) Multilingual Language Models Transliteration and Transcription Post-Editing Machine Translation (PMT)

23. Text summarization and paraphrasing Automatic Summarization Paraphrasing and Sentiment Analysis Summary Generation Multimodal Summarization

24. Question answering and information retrieval Question Answering (QA) Systems Information Retrieval (IR) Models Passage Retrieval and Summarization Conversational QA and Dialogue Systems

25. Code generation and analysis Code Completion and Suggestions Code Generation from Natural Language Code Analysis and Inspection Code Synthesis and Generation from Abstract Specifications

26. Limitations and Challenges Biases in training data and outputs Hallucinations and factual inaccuracies Lack of true understanding or reasoning Ethical concerns and potential misuse

27. Biases in training data and outputs Unintended Biases in Training Data Implicit Biases in Model Outputs Cascading Biases Lack of Representation

28. Hallucinations and factual inaccuracies AI-generated Content that Doesn't Exist Factual Inaccuracies in AI-generated Text AI-generated Images with Incorrect Context Factual Biases in AI-generated Content

29. Lack of true understanding or reasoning AI Systems that Don't Truly Understand Lack of Common Sense Reasoning Insufficient Contextual Understanding Over-Reliance on Memorization

30. Ethical concerns and potential misuse Biased Decision-Making Privacy Violations Surveillance and Monitoring Moral Responsibility and Accountability

31. Popular LLM Examples OpenAI's GPT models Google's BERT and LaMDA Meta's LLaMA Anthropic's Claude

32. Impact on Various Industries How LLMs are transforming business processes Potential applications in different sectors (e.g., healthcare, finance, education)

33. Transforming business Automating Routine Tasks Improving Customer Service Enhancing Product Development Streamlining Compliance and Risk Management Optimizing Operations and Supply Chain Management Enabling Strategic Decision-Making:

34. Potential applications Healthcare: Medical Documentation and Research Finance: Risk Analysis and Compliance Education: Personalized Learning and Research Support Oil and Gas: Predictive Maintenance and Risk Analysis Telecommunications: Network Optimization and Customer Support Manufacturing: Quality Control and Supply Chain Optimization

35. Future Directions Ongoing research and development in LLMs Potential advancements and their implications

36. Ongoing research Multitask Learning Adversarial Training Explainable AI (XAI) Transfer Learning Low-Resource Languages Human-Like Language Generation

37. Potential advancements and their implications Improved Language Understanding Increased Automation Enhanced Creative Capabilities Advanced Customer Service Faster Discovery and Innovation New Forms of Human-AI Interaction:

38. AI LLMs & SharePoint Using Large Language Models (LLMs) with SharePoint within the corporate firewall Part 1: A brief introduction to Large Language Models

39. AI LLMs & SharePoint