Speaker
Description
1 Introduction
1.1 Artificial Intelligence (AI)
1.2 Data Mining and Big Data
1.3 History of Artificial Intelligence
1.3.1 Turing’s Test (1950)
1.3.2 The Formal Beginning
1.3.3 Early Success
1.3.4 1st AI Winter
1.3.5 Revival
1.3.6 2nd AI Winter
1.3.7 The Rise of Machine Learning
1.3.8 AI in the 21st Century
1.3.9 Current AI Technology
1.4 Supercomputers & AI
2 Introduction to Machine Learning
2.1 Traditional Programming
2.2 Machine Learning Algorithms
2.3 Input - Output
2.4 Fields of ML/AI
2.5 Tabular datasets
2.6 Computer Vision
3 Mathematical Models
3.1 A fundamental problem
3.2 ML Models
3.3 Artificial Neural Networks
3.4 Training a Model
3.5 Newton’s Method for Root Finding
3.6 Newton’s Method for Optimization
3.7 In Multiple Dimensions
3.8 Fundamental inefficiencies
4 Introduction to Large Language Models
4.1 Gödel’s incompleteness theorem & Turing’s Test
4.2 2024 LLMs
4.3 Language issues
4.4 Semiotics
4.5 Word representation
4.6 One-hot encoding
4.7 Representation of sentences
4.8 Vocabulary with N words
4.9 Semantic Limitations of One-hot Encoding
4.10 Word Embeddings and Distributional Semantics
4.11 Training Transformers with Embedded Word Representations
4.12 Training and Fine-tuning
4.13 Sequences of words
4.14 Train Dataset
4.15 The Transformer model architecture
4.16 Big Data
4.17 Model and Token Size
4.18 EuroHPC JU Access Call for AI and Data-Intensive Applications
5 Applications
5.1 Generative AI
5.2 Large Language Models
5.3 Computer Vision
5.4 Recommender Systems
5.4.1 Timeseries Analysis and Predictions
5.5 Black-Box Optimization
5.6 Analysis of Scientific Literature
5.7 More
6 Introduction to Prompt Engineering
6.1 Definition and significance
6.2 Overview of language models
6.3 Fundamentals of Crafting Effective Prompts
6.4 Basic principles of prompt design
6.5 The role of prompts in steering the model’s responses
6.6 Common pitfalls
6.7 Ethical Considerations and Best Practices
6.8 Technical Aspects of Prompt Engineering
