Introduction to Artificial Intelligence

Artificial Intelligence

Definition

• Artificial Intelligence (AI) refers to the development of computer systems that can perform tasks that typically require human intelligence, such as:
  • Learning
  • Problem-solving
  • Reasoning
  • Perception

Types of AI

• Narrow or Weak AI: designed to perform a specific task, such as:
  • Virtual assistants (e.g., Siri, Alexa)
  • Image recognition systems
  • Language translation software
• General or Strong AI: designed to perform any intellectual task, similar to human intelligence (currently, no true General AI systems exist)

AI Subfields

• Machine Learning: development of algorithms that enable computers to learn from data and improve performance over time
• Natural Language Processing (NLP): study of how computers understand, interpret, and generate human language
• Robotics: development of intelligent robots that can interact with and adapt to their environment
• Computer Vision: development of algorithms that enable computers to interpret and understand visual information from images and videos

AI Techniques

• Supervised Learning: training AI models on labeled data to enable prediction or classification
• Unsupervised Learning: training AI models on unlabeled data to enable pattern discovery or clustering
• Reinforcement Learning: training AI models through trial and error to optimize behavior
• Deep Learning: use of neural networks to analyze complex data and make predictions or classify objects

AI Applications

• Healthcare: diagnosis, medical imaging analysis, and personalized medicine
• Finance: fraud detection, risk management, and portfolio optimization
• Transportation: autonomous vehicles, traffic management, and route optimization
• Customer Service: chatbots, virtual assistants, and customer sentiment analysis

Artificial Intelligence

Definition

• Artificial Intelligence (AI) is the development of computer systems that can perform tasks that typically require human intelligence.
• AI tasks include learning, problem-solving, reasoning, and perception.

Types of AI

Narrow or Weak AI

• Designed to perform a specific task.
• Examples include virtual assistants (e.g., Siri, Alexa), image recognition systems, and language translation software.

General or Strong AI

• Designed to perform any intellectual task, similar to human intelligence.
• Currently, no true General AI systems exist.

AI Subfields

Machine Learning

• Development of algorithms that enable computers to learn from data and improve performance over time.

Natural Language Processing (NLP)

• Study of how computers understand, interpret, and generate human language.

Robotics

• Development of intelligent robots that can interact with and adapt to their environment.

Computer Vision

• Development of algorithms that enable computers to interpret and understand visual information from images and videos.

AI Techniques

Supervised Learning

• Training AI models on labeled data to enable prediction or classification.

Unsupervised Learning

• Training AI models on unlabeled data to enable pattern discovery or clustering.

Reinforcement Learning

• Training AI models through trial and error to optimize behavior.

Deep Learning

• Use of neural networks to analyze complex data and make predictions or classify objects.

AI Applications

Healthcare

• Diagnosis, medical imaging analysis, and personalized medicine.

Finance

• Fraud detection, risk management, and portfolio optimization.

Transportation

• Autonomous vehicles, traffic management, and route optimization.

Customer Service

• Chatbots, virtual assistants, and customer sentiment analysis.