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Training Syllabus: Understanding Generative AI Language Models and Large Language Models for Business Professionals

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Large language model
Last 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.
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en.wikipedia.org
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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The “Algorithm Economy”, not even 10 years old, is already being superceded by the Cognition Economy.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
Translation Services

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.
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