Intelligent Agents in AI

• Artificial Intelligence (AI) is a computer science field focused on creating machines with intelligent behavior.
• AI systems analyze data, recognize patterns, make decisions, and automate tasks, typically requiring human intelligence.
• AI involves learning, reasoning, problem-solving, perception, and language understanding to improve performance over time.
• AI is defined as enabling machines to analyze data, make decisions, and improve their performance.

Different Definitions of AI

• Elaine Rich and Kevin Knight: AI is the study of making computers do things at which, at the moment, people are better.
• John McCarthy (1956, Dartmouth Conference): AI is the science and engineering of making intelligent machines, especially intelligent computer programs.
• Stuart Russell and Peter Norvig: AI is the study of agents that perceive their environment and take actions to maximize their chances of success.
• AI is a booming technology in computer science, creating a revolution by making intelligent machines.
• AI currently works with self-driving cars, playing chess, proving theorems, playing music, and painting.
• AI seeks to make machines work like humans.
• "Artificial" means "man-made," and "intelligence" means "thinking power".

Important reasons to Learn AI:

• Create software or devices to solve real-world problems like health, marketing, and traffic issues.
• Develop personal virtual assistants like Cortana, Google Assistant, and Siri.
• Build robots that can work in environments unsafe for humans.
• Open paths for new technologies, devices, and opportunities..

Goals of AI

• Replicate human intelligence.
• Solve knowledge-intensive tasks.
• Create an intelligent connection of perception and action.
• Build machines capable of tasks requiring human intelligence, like proving theorems, playing chess, surgical planning, and driving.
• Create systems exhibiting intelligent behavior, learning, demonstrating, explaining, and advising users.

Components of AI

• AI requires knowledge of reasoning, learning, problem-solving, perception, and language.
• Needed disciplines include Mathematics, Biology, Psychology, Sociology, Computer Science, and Neurons Study.

Advantages of AI

• High Accuracy: AI machines have fewer errors due to pre-experience or information.
• High-Speed: AI systems are of high-speed and fast-decision making.
• High reliability: AI machines are highly reliable and can perform the same action multiple times with high accuracy.
• Helpful for risky areas: AI machines can be helpful in situations such as defusing a bomb, and exploring the ocean floor.
• The digital assistant AI can be very useful to provide digital assistant to the users.
• AI can be useful for public utilities: a self-driving car and facial recognition.

Disadvantages of AI

• High Cost: The hardware and software of AI is very costly, so it requires lots of maintenance.
• AI machines cannot think out of the box: machines will only do that work for which they are trained, or programmed.
• No feelings and emotions: It does not have the feeling so it cannot make any kind of emotional attachment with the user.
• Increased Dependency on Machines: People are getting more dependent on devices and losing their mental capabilities.
• No Original Creativity: AI machines cannot beat this power of human intelligence and cannot be creative and imaginative

History of Artificial Intelligence:

• 1943: Warren McCulloch and Walter Pitts propose a model of artificial neurons.
• 1949: Donald Hebb introduces Hebbian learning for modifying connection strength between neurons.
• 1950: Alan Turing publishes "Computing Machinery and Intelligence," introducing the Turing test.
• 1955: Allen Newell and Herbert A. Simon create "Logic Theorist," the first AI program, proving 38 of 52 math theorems.
• 1956: John McCarthy coins the term "Artificial Intelligence" at the Dartmouth Conference, establishing AI as an academic field.
• 1966: Joseph Weizenbaum creates ELIZA, the first chatbot.
• 1972: Japan builds WABOT-1, the first intelligent humanoid robot.
• 1974-1980: First AI Winter caused by reduced government funding.
• 1980 After AI winter duration, AI came back with "Expert System".
• 1980: The first national AAAI conference is held at Stanford.
• 1987-1993: Second AI Winter due to high costs and inefficient results.
• 1997: IBM's Deep Blue beats Garry Kasparov.
• 2002: AI enters homes via Roomba.
• 2006: AI enters business with Facebook, Twitter, and Netflix deploying it.
• 2011: IBM's Watson wins Jeopardy!.
• 2012: Google launches "Google Now."
• 2014: Chatbot "Eugene Goostman" wins Turing test.
• 2018: IBM's "Project Debater" debates complex topics.

Types of AI

• Classification based on the machine's ability to mimic human cognition.

Weak AI (Applied AI or Narrow AI)

• Focuses on specific tasks.
• It Does not possess true understanding or consciousness.
• Examples: digital assistants (Siri, and Alexa) and chess program (IBM's Deep Blue).

Strong AI (General AI)

• Aims to replicates human intelligence in all aspects.
• Capable of reasoning, learning, and decision-making like a human.
• Hypothetical as of today.
• Based on the methodologies used in Al development.

Conventional AI (Symbolic AI, Logic-based AI)

• Uses explicit knowledge representation (rules, logic).
• It works efficiently in well-defined environments and poorly with uncertainty.
• Examples: expert systems and rule-based machine translation.

Computational AI (Soft Computing, Sub-symbolic AI)

• Inspired by human learning and adaptation.
• Uses machine learning, neural networks, and fuzzy logic.
• Examples: Deep Learning (Convolutional Neural Networks) and Reinforcement Learning (AlphaGo).

Knowledge-Based AI

• Relies on predefined rules and knowledge databases.
• Reasonings based on stored information.
• Example: expert systems.

Non-Knowledge-Based AI

• Learns from data without predefined rules.
• Employs statistical models and deep learning.
• Example: neural networks.

Applications of AI

• AI is being used in diverse sectors from processing natural language to robotics.

Natural Language Processing (NLP)

• Enables machines to understand, interpret, and generate human language.
• Applications include: Chatbots, Virtual Assistants, Machine Translation, Speech Recognition, and Text Summarization.

AI in Robotics

• Al-powered robots are used in industries, healthcare, and space exploration.
• Industrial Automation, Medical Robots, Autonomous Vehicles, and Humanoid Robots.

AI in Automation

• Optimization of AI-driven automation processes includes: business process automation, IT automation, and smart homes.

AI in Human Emotion Recognition

• AI can analyze human emotions: Sentiment Analysis, Facial Emotion Recognition, and AI in Mental Health

Types of Intelligent Agents

• Intelligent agents perceive their environment and act to achieve optimal outcomes.

Properties of Intelligent Agents

• Autonomy: Operates without human intervention.
• Reactive & Proactive: Responds to changes while planning for the future.
• Social Ability: Interacts with other agents or humans.

Structure of an Intelligent Agent

• Sensors: Gathers environmental data.
• Actuators: Performs actions.
• Agent Function: Maps perceptions to actions using AI algorithms.

Examples of Intelligent Agents

• Chatbots, Autonomous Robots and Recommendation Systems.

Agent Environment

• The agent-environment interaction is critical for designing AI systems.

Observable vs. Partially Observable

• Fully: Agents access complete environmental information at any time.
• Partially: agents have limited environmental knowledge.

Deterministic vs. Stochastic

• Deterministic: Fully determined by current actions.
• Stochastic: Involves randomness.

Static vs. Dynamic

• Static: The environment does not change while the agent is thinking
• Dynamic: The environment changes even without agent actions.

Discrete vs. Continuous

• Discrete: The environment has a finite number of states.
• Continuous: The environment has infinitely many possible states.

Single-Agent vs. Multi-Agent

• Single-Agent: The agent operates alone.
• Multi-Agent: Multiple agents.

Turing Test

• Turing suggests in 1950 that the test evaluates machine Intelligence.

Definition

• A machine exhibits intelligent behavior indistinguishable from a human.
• Human evaluator interacts with both a human and a machine via a text-based interface.
• If the evaluator cannot reliably distinguish, it passes through the test.

Significance

• It shifts the focus from how Al works to how it is perceived.
• It sets a benchmark for human-like Al communication.

Criticism and Limitations

• Chinese Room Argument (John Searle, 1980): A machine can simulate intelligence without true understanding.
• The test is not a true intelligence because AI can mimic human responses using statistical models without reasoning.

Modern Alternatives to the Turing Test

• Winograd Schema Challenge: Test Al's ability to understand context.

Lovelace Test

• Evaluates Al's creativity.

Test your knowledge on intelligent agents. Questions cover agent types, properties, components, and AI goals. Explore agent decision-making, environmental interaction, and the disciplines behind Artificial Intelligence.