Robots are real, but not like the movies! This is your AI starter guide.

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A (very) Basic Guide to AI (Artificial
Intelligence)
Understanding the Fundamentals
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Overview

• Introduction to AI
• Evolution of AI
• Types of AI
• Machine Learning
• Deep Learning
• Applications of AI
• Ethical Considerations
• Future of AI
• MAGES Institutes TIPP courses

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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.

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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
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Market Size Gen AI - Singapore

• The market size in the Artificial Intelligence
  market is projected to reach US3.88bn 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 US10.39bn by 2030.
• In global comparison, the largest market size
  will be in the United States (US106.50bn in
  2024).

Source https//www.statista.com/outlook/tmo/artif
icial-intelligence/singaporemarket-size
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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.

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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.

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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.

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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.

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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.

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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.

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hank You for taking the First Step.
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THE FUTURE IS HERE
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