AI (Artificial Intelligence) introduction training

Cui îi este dedicat cursul?

The training is addressed to the participants who are new to the AI field and want to understand the basics of AI:

• Business professionals 
• Managers 
• Enthusiasts

Cunoștințe și abilități inițiale

The following is a list of the minimal prerequisites required to attend the course:
● Basic technical literacy: familiarity with basic computer operations and the internet
○ No specific programming or technical skills required
● Understanding of basic business concepts
○ Especially useful to understand and asses AI’s impact on various industries and business functions
● Curiosity about technology: an interest in learning how AI technologies work and their potential applications
● Willingness to learn: openness to explore new concepts and ideas about technology and innovation

Prezentarea cursului

The main training objectives are:
● Learn an overview of the AI (Artificial Intelligence) domain and sub-domains
● Learn an overview of the most useful AI integration business domains
● Learn how and when to use Prompt Engineering, for interacting with AI solutions

Ce subiecte abordează cursul

Day 1: Understanding AI and its landscape, Machine Learning intro
1. Training overview
2. AI overview
○ Definitions
○ History and evolution
3. AI classification
○ Narrow AI
○ General AI
○ Generative and Generative Creative AI
○ SuperIntelligent AI
4. Machine Learning (ML)
○ Learning types:
■ Supervised
■ Unsupervised
■ Reinforcement
○ ML models business domains and applications
■ Natural Language Processing (NLP)
■ Computer vision
■ Speech recognition
■ Healthcare
■ Finance
■ Autonomous vehicles
■ Manufacturing and industry
■ E-commerce
■ Energy
○ Pretrained models
■ When and why to use a pretrained model?
■ Improving a pretrained model
5. Prompt engineering
○ Overview
○ Hands-on usage, using several LLMs (ChatGPT, Bard etc)
Day 2: Understanding Large Language Models (LLMs)
1. Understanding LLMs: how they work and their capabilities
○ What is an LLM?
○ LLMs overview - open-source and private
○ Pre-training and Fine-tuning
○ The Transformer architecture
○ Attention mechanism
○ Context understanding
2. Limitations of LLMs
○ Halucinations
○ Lack of common sense
○ Vulnerability to attacks
○ Context sensitivity
○ Over-reliance on data
○ Difficulty with nuances
○ Lack of real understanding
○ Resources intensive
3. The future of AI
○ Emerging trends and technologies in AI
○ LVM (Large Vision Models)
○ AI devices (agents)
○ Human-Machine collaboration
4. AI's impact on society and economy - overview
5. Optional topic (if needed): ethical considerations of LLMs
○ Bias and fairness
○ Misuse and manipulation
○ Privacy concerns
○ Security risks
○ Environmental impact
○ Explainability and transparency
○ Job displacement
Day 3: Workshop - hands-on with AI tools
● Hands-on work, established with the participants:
○ Interactive exercises using basic AI models: image recognition, chat bots
○ Building a simple ML model and exploring LLMs, through several platforms:
■ HuggingFace
■ Locally deployed LLMs
■ Other possibilities
● Course feedback