Some pre-reading material:
Large language modelLast edited: Tue, Apr 18, 2023
A large language model (LLM) is a language model consisting of a neural network with many parameters (typically billions of weights or more), trained on large quantities of unlabelled text using self-supervised learning. LLMs emerged around 2018 and perform well at a wide variety of tasks. This has shifted the focus of natural language processing research away from the previous paradigm of training specialized supervised models for specific tasks.
See more
Generative AI is what people initially thought the Internet was in the early 1990s.
The Internet was forecast to be the Global Brain. In fact, it was the Global Filing Cabinet.
Generative AI and large language models will be the Global Brain.
Those getting ahead of the curve now by learning to build language models for their own company and business domain will become the key players and movers in this realm.
The “Algorithm Economy”, not even 10 years old, is already being superceded by the Cognition Economy.
Course Objective
This course aims to provide business professionals with a comprehensive understanding of generative AI language models and large language models, enabling them to make informed decisions and manage projects effectively.
Course Structure
Introduction to Artificial Intelligence and Machine Learning Overview of Natural Language Processing (NLP) Generative AI Language Models: Basics and Framework Large Language Models: Key Features and Concepts Applications of Generative AI Language Models in Business Challenges and Limitations of Generative AI Language Models Best Practices for Implementing AI Language Models in Business 1. Introduction to Artificial Intelligence and Machine Learning
What is Artificial Intelligence (AI)? Understanding Machine Learning (ML) and Deep Learning (DL) Types of Machine Learning: Supervised, Unsupervised, and Reinforcement Learning Key concepts: Data sets, Algorithms, and Model Training 2. Overview of Natural Language Processing (NLP)
What is Natural Language Processing (NLP)? NLP Applications: Sentiment Analysis, Chatbots, Text Summarization, and more NLP Techniques: Tokenization, Stemming, Lemmatization, and POS Tagging Introduction to Word Embeddings and Vector Space Models 3. Generative AI Language Models: Basics and Framework
What are Generative AI Language Models? Types of Language Models: Rule-based, Statistical, and Neural Networks Understanding Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) Introduction to the Transformer Architecture 4. Large Language Models: Key Features and Concepts
What are Large Language Models? Understanding GPT (Generative Pre-trained Transformer) and its Variants BERT (Bidirectional Encoder Representations from Transformers) Fine-tuning Large Language Models for Specific Tasks Scaling up Language Models: Model Size, Training Data, and Computational Power 5. Applications of Generative AI Language Models in Business
Content Generation: Blog Posts, Ad Copy, Social Media Updates Automated Customer Support: Chatbots and Email Responses Data Analysis: Sentiment Analysis, Market Trend Prediction, Text Mining Personal Assistants: Scheduling, Task Management, Information Retrieval 6. Challenges and Limitations of Generative AI Language Models
Ethical Considerations: Bias, Misinformation, and Manipulation Model Explainability and Interpretability Data Privacy and Security Resource Requirements: Computational Power, Time, and Costs 7. Best Practices for Implementing AI Language Models in Business
Identifying the Right Use Cases for AI Language Models Evaluating and Selecting Language Model Providers Collaboration with Data Scientists and ML Engineers Monitoring and Maintaining Models: Quality Assurance and Updates Ensuring Ethical and Responsible AI Deployment Course Duration
The course will be conducted over 8 weeks, with 1 session per week, each session lasting 3 hours.
Assessment
Participants will be assessed based on their understanding of the concepts and ability to apply their knowledge to real-world business scenarios through group discussions, case studies, and a final project presentation.