Artificial Intelligence Overview

Artificial Intelligence (AI) Definition

• AI is a field of computer science dedicated to creating intelligent machines, able to perform tasks requiring human intelligence, including thinking, decision-making, and behavior.
• AI doesn't require pre-programmed machines. Instead, it involves developing machines with programmed algorithms that allow for intelligence.
• AI involves a computer's ability to think, learn, and mimic human mental processes like perceiving, reasoning, and learning.

Different Types of AI

• Acting humanly: Systems that act like humans.
• Thinking humanly: Systems that think like humans.
• Acting rationally: Systems that act rationally.
• Thinking rationally: Systems that think rationally.

Types of AI Based on Capabilities

• Weak or narrow AI: Performs predefined, narrow sets of instructions without exhibiting thinking.
• General AI: Capable of performing tasks like humans.
• Strong AI: Predicted to surpass human capabilities, potentially posing a threat to society.

AI Aims

• Simulate human intelligence: Replicate human intelligence in machines.
• Solve complex problems: Address challenges exceeding human ability.
• Improve life: Develop intelligent machines that benefit human lives (e.g., virtual assistants).
• Perform complex tasks: Allow machines to carry out tasks demanding human intelligence (e.g., surgery, theorem proving).
• Advance technologies: Drive innovation in new technologies, devices, and opportunities.

AI Timeline (Examples of milestones)

• 1950: Turing Test proposed.
• 1955: "Artificial intelligence" term coined.
• 1961: Unimate, the first industrial robot.
• 1964: ELIZA developed, a pioneering chatbot.
• 1966: Shakey, the first general-purpose mobile robot, developed.
• 1997: Deep Blue defeated a chess champion.
• 2011: Siri introduced. Watson won Jeopardy.
• 2014: Eugene Goostman chatbot. Others like Alexa released.
• 2016: AlphaGo beat human Go champion.

Core Components of Intelligence

• Learning: Knowledge acquisition, often through trial and error.
• Reasoning: Processes that enable judgment to make decisions or predictions (like the example of determining Fred's location).
• Perception: Scanning the environment using sensory organs (real or artificial) to decompose elements in spatial relationships.
• Problem-solving: Systematic, data-based search for solutions.
• Language intelligence: Computers process and parse language similarly to humans.

Turing Test

• The Turing test is a method of evaluating a machine's ability to exhibit intelligent behavior equivalent to, or indistinguishable from, that of a human.
• A computer passes the test if a human interrogator, unable to distinguish it from a human, cannot tell that the responses originate from a machine.
• To pass, a computer must have natural language processing, knowledge representation, and automated reasoning

AI and Employment

• Estimated proportions of employment exposure to AI automation varies across fields.
• Some roles have a higher risk of automation due to their procedures (office and administrative work, sales, computer and mathematical and many more).

Fields of Artificial Intelligence

• Deep learning
• Predictive analytics
• Machine learning
• Natural language processing (NLP)
• Expert systems
• Planning, scheduling & optimization
• Robotics
• Image recognition
• Machine vision
• Speech
• Classification & clustering
• Translation
• Information extraction
• Text to speech
• Speech to text

Core Areas of AI

• Machine Learning (ML): Fundamental area focused on developing algorithms letting computers learn from data and make predictions or decisions iteratively improving performance with new datasets.
• Deep Learning: A subset of machine learning using artificial neural networks to process vast amounts of data and extract complex patterns for accurate and efficient performance.

AI in Different Fields

• Robotics: Bridges digital and physical worlds, integrating AI with engineering and processing information for robot actions based on AI decisions.
• Expert Systems: Utilize AI to replicate human expertise in specific fields, supporting decisions and recommendations based on knowledge bases (finance, medicine, law).
• Natural Language Processing (NLP): Focuses on computer interaction with human language enabling machines to understand interpret and generate text or speech in meaningful forms.
• Computer Vision: Replicates human perception, enabling machines to interpret visual information from images and videos, extracting valuable insights (like in self-driving cars, traffic light recognition, etc.).

Examples of AI Applications

• Self-driving cars
• Facial recognition technology
• Machine translation
• Chatbots
• Medical diagnosis
• Fraud detection
• Personalized recommendations
• Virtual assistants
• Smart homes
• Industrial automation