AI: Not so scary after all! Learn the basics here.

1. A (very) Basic Guide to AI (Artificial Intelligence) Understanding the Fundamentals Private & Confidential

2. Overview Introduction to AI ● Evolution of AI ● Types of AI ● Machine Learning ● Deep Learning ● Applications of AI ● Ethical Considerations ● Future of AI ● MAGES Institute’s TIPP courses ● 2 Private & Confidential

3. Introduction to AI Definition of AI Artificial Intelligence (AI) refers to the simulation of human intelligence in machines. Purpose of AI ○ "AI aims to perform tasks that typically require human intelligence, such as visual perception, speech recognition, decision-making, and language translation." ● Example: Virtual assistants like Siri and Alexa utilize AI to understand and respond to user commands. 3 Private & Confidential

4. Evolution of AI Historical Overview ○ "AI research began in the 1950s, with early focus on symbolic reasoning and problem-solving." Modern AI ○ "Recent advancements in data availability and computational power have led to the rise of machine learning and deep learning." ● ● Example: IBM's Deep Blue defeating chess champion Garry Kasparov in 1997 marked a significant milestone in AI's evolution. Source: Statista 4 Private & Confidential

5. Market Size Gen AI - Singapore The market size in the Artificial Intelligence market is projected to reach US$3.88bn in 2024. The market size is expected to show an annual growth rate (CAGR 2024- 2030) of 17.84%, resulting in a market volume of US$10.39bn by 2030. In global comparison, the largest market size will be in the United States (US$106.50bn in 2024). ● ● ● Source: https://www.statista.com/outlook/tmo/artificial- intelligence/singapore#market-size 5 Private & Confidential

6. Types of AI Narrow AI ○ ○ ● "Also known as Weak AI." "Designed to perform specific tasks, such as virtual assistants, recommendation systems, and image recognition." General AI ○ "Also known as Strong AI." ○ "Hypothetical AI with human-like cognitive abilities, capable of understanding, learning, and reasoning across various domains." ● Example: Narrow AI includes self-driving cars, while General AI remains a theoretical concept depicted in science fiction. 6 Private & Confidential

7. Machine Learning A subset of AI that enables machines to learn from data without being explicitly programmed. Types of Machine Learning ○ Supervised Learning: Training a model with labeled data. ○ Unsupervised Learning: Discovering patterns in unlabeled data. ○ Reinforcement Learning: Learning through trial and error. ● Example: Email spam filters use supervised learning to classify emails as spam or not spam based on past examples. 7 Private & Confidential

8. Deep Learning A subset of machine learning that utilizes artificial neural networks to model and understand complex patterns in data. Applications ○ Image Recognition: Identifying objects in images or videos. ○ Natural Language Processing: Understanding and generating human language. ○ Speech Recognition: Converting spoken language into text. ● Example: Deep learning powers facial recognition technology used in smartphones and security systems. 8 Private & Confidential

9. Applications of AI Healthcare ○ ● Disease Diagnosis: AI can analyze medical images for early detection of diseases like cancer. Drug Discovery: AI accelerates drug development by predicting molecular interactions. Finance ○ Fraud Detection: AI algorithms detect suspicious patterns in financial transactions. ○ Algorithmic Trading: AI predicts market trends and executes trades autonomously. Transportation ○ Autonomous Vehicles: AI enables self-driving cars to navigate roads safely. ○ Traffic Management: AI optimizes traffic flow and reduces congestion in urban areas. ○ ● ● Example: Tesla's Autopilot feature demonstrates the potential of AI in enabling semi-autonomous driving. 9 Private & Confidential

10. Ethical Considerations Bias in AI ○ ● "AI algorithms can inherit biases from the data they are trained on, leading to unfair or discriminatory outcomes." Privacy Concerns ○ "AI systems often rely on vast amounts of personal data, raising questions about data privacy and security." ● Example: Facial recognition technology has faced criticism for its potential to perpetuate racial biases in law enforcement. 10 Private & Confidential

11. Future of AI Advancements ○ "Continued improvements in AI algorithms, hardware, and data availability." Challenges ○ "Ethical and regulatory concerns." ○ "Ensuring transparency and accountability in AI systems." ● ● Example: Research in quantum computing holds promise for enhancing AI capabilities beyond current limitations. 11 Private & Confidential

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13. THE FUTURE IS HERE Private & Confidential