Artificial Intelligence: Introduction - PDF

UNIT I- Introduction to Artificial Intelligence 1. Introduction Artificial Intelligence (AI) is the field of computer science focused on creating machines capable of intelligent behaviour. AI systems can analyse data, recognize patterns, make decisions, and automate tasks that typically require hum...

UNIT I- Introduction to Artificial Intelligence 1. Introduction Artificial Intelligence (AI) is the field of computer science focused on creating machines capable of intelligent behaviour. AI systems can analyse data, recognize patterns, make decisions, and automate tasks that typically require human intelligence. Artificial Intelligence (AI) is a branch of computer science that focuses on creating systems capable of performing tasks that typically require human intelligence. These tasks include learning, reasoning, problem solving, perception, and language understanding. AI enables machines to analyse data, make decisions, and improve their performance over time. Definition of AI Various researchers and experts have defined AI in different ways:  Elaine Rich and Kevin Knight: "Artificial Intelligence is the study of how to make 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." In today's world, technology is growing very fast, and we are getting in touch with different new technologies day by day. Here, one of the booming technologies of computer science is Artificial Intelligence, which is ready to create a new revolution in the world by making intelligent machines. The Artificial Intelligence is now all around us. It is currently working with a variety of subfields, ranging from general to specific, such as self-driving cars, playing chess, proving theorems, playing music, Painting, etc. AI is one of the fascinating and universal fields of Computer science that has a great scope in future. AI holds a tendency to cause a machine to work as a human. Artificial Intelligence is composed of two words Artificial and Intelligence, where Artificial defines "man-made," and intelligence defines "thinking power", hence AI means "a man-made thinking power." Artificial Intelligence exists when a machine can have human based skills such as learning, reasoning, and solving problems With Artificial Intelligence you do not need to preprogram a machine to do some work, despite that you can create a machine with programmed algorithms, which can work with, own intelligence, and that is the awesomeness of AI. It is believed that AI is not a new technology, and some people says that as per Greek myth, there were Mechanical men in early days which can work and behave like humans. Why Artificial Intelligence? Before Learning about Artificial Intelligence, we should know that what is the importance of AI and why should we learn it. Following are some main reasons to learn about AI: o With the help of AI, you can create such software or devices, which can solve real-world problems very easily and with accuracy such as health issues, marketing, traffic issues, etc. o With the help of AI, you can create your personal virtual Assistant, such as Cortana, Google Assistant, Siri, etc. o With the help of AI, you can build such Robots, which can work in an environment where survival of humans can be at risk.  o AI opens a path for other new technologies, new devices, and new Opportunities. Goals of Artificial Intelligence: Following are the main goals of Artificial Intelligence: 1. Replicate human intelligence 2. Solve Knowledge-intensive tasks 3. An intelligent connection of perception and action 4. Building a machine which can perform tasks that requires human intelligence such as: o Proving a theorem o Playing chess o Plan some surgical operation o Driving a car in traffic 5. Creating some system which can exhibit intelligent behaviour, learn new things by itself, demonstrate, explain, and can advise to its user. What Comprises to Artificial Intelligence? Artificial Intelligence is not just a part of computer science even it's so vast and requires lots of other factors which can contribute to it. To create the AI first we should know that how intelligence is composed, so the Intelligence is an intangible part of our brain, which is a combination of Reasoning, learning, problem-solving perception, language understanding, etc. To achieve the above factors for a machine or software Artificial Intelligence requires the following discipline: o Mathematics o Biology o Psychology o Sociology o Computer Science o Neurons Study  o Statistics Advantages of Artificial Intelligence: Following are some main advantages of Artificial Intelligence: o High Accuracy with less error: AI machines or systems are prone to less errors and high accuracy as it takes decisions as per pre-experience or information. o High-Speed: AI systems can be of high-speed and fast-decision making, because of those AI systems can beat a chess champion in the Chess game. o High reliability: AI machines are highly reliable and can perform the same action multiple times with high accuracy. o Useful for risky areas: AI machines can be helpful in situations such as defusing a bomb, exploring the ocean floor, where to employ a human can be risky. o Digital Assistant: AI can be very useful to provide digital assistant to the users such as AI technology is currently used by various E- commerce websites to show the products as per customer requirement. o Useful as a public utility: AI can be very useful for public utilities such as a self-driving car that can make our journey safer and hassle-free, facial recognition for security purpose, Natural language processing to communicate with the human in human-language, etc. Disadvantages of Artificial Intelligence: Every technology has some disadvantages, and the same goes for Artificial intelligence. Being so advantageous technology still, it has some disadvantages that we need to keep in our mind while creating an AI system. Following are the disadvantages of AI: o High Cost: The hardware and software requirement of AI is very costly, as it requires lots of maintenance to meet current world requirements. o Cannot think out of the box: Even we are making smarter machines with AI, but still they cannot work out of the box, as the robot will only do that work for which they are trained, or programmed. o No feelings and emotions: AI machines can be an outstanding performer, but still it does not have the feeling so it cannot make any kind of emotional attachment with human, and  may sometime be harmful for users if the proper care is not taken. o Increase dependency on machines: With the increment of technology, people are getting more dependent on devices and hence they are losing their mental capabilities. o No Original Creativity: As humans are so creative and can imagine some new ideas but still AI machines cannot beat this power of human intelligence and cannot be creative and imaginative ----------------------------------------------------------------------------------------------------------------------------- 2. History of Artificial Intelligence: Artificial Intelligence is not a new word and not a new technology for researchers. This technology is much older than you would imagine. Maturation of Artificial Intelligence (1943-1952) o Year 1943: Warren McCulloch did the first work, which is now recognized as AI, and Walter pits in 1943. They proposed a model of artificial neurons. o Year 1949: Donald Hebb demonstrated an updating rule for modifying the connection strength between neurons. His rule is now called Hebbian learning. o Year 1950: The Alan Turing who was an English mathematician and pioneered Machine learning in 1950. Alan Turing publishes "Computing Machinery and Intelligence" in which he proposed a test. The test can check the machine's ability to exhibit intelligent behavior equivalent to human intelligence, called a Turing test. The birth of Artificial Intelligence (1952-1956) o Year 1955: An Allen Newell and Herbert A. Simon created the "first artificial intelligence program"Which was named as "Logic Theorist". This program had proved 38 of 52 Mathematics theorems, and find new and more elegant proofs for some theorems. o Year 1956: The word "Artificial Intelligence" first adopted by American Computer scientist John McCarthy at the Dartmouth Conference. For the first time, AI coined as an academic field. o At that time high-level computer languages such as FORTRAN, LISP, or COBOL were invented. And the enthusiasm for AI was very high at that time. The golden years-Early enthusiasm (1956-1974) o Year 1966: The researchers emphasized developing algorithms that can solve mathematical problems. Joseph Weizenbaum created the first chatbot in 1966, which was named as ELIZA. o Year 1972: The first intelligent humanoid robot was built in Japan, which was named as WABOT-1. The first AI winter (1974-1980)  o The duration between years 1974 to 1980 was the first AI winter duration. AI winter refers to the time where computer scientist dealt with a severe shortage of funding from government for AI researches. o During AI winters, an interest of publicity on artificial intelligence was decreased. A boom of AI (1980-1987) o Year 1980: After AI winter duration, AI came back with "Expert System". Expert systems were programmed that emulate the decision- making ability of a human expert. o In the Year 1980, the first national conference of the American Association of Artificial Intelligence was held at Stanford University. The second AI winter (1987-1993) o The duration between the years 1987 to 1993 was the second AI Winter duration. o Again, Investors and government stopped in funding for AI research as due to high cost but not efficient result. The expert system such as XCON was very cost effective. The emergence of intelligent agents (1993-2011) o Year 1997: In the year 1997, IBM Deep Blue beats world chess champion, Gary Kasparov, and became the first computer to beat a world chess champion. o Year 2002: for the first time, AI entered the home in the form of Roomba, a vacuum cleaner. o Year 2006: AI came in the Business world till the year 2006. Companies like Facebook, Twitter, and Netflix also started using AI. Deep learning, big data and artificial general intelligence (2011-present) o Year 2011: In the year 2011, IBM's Watson won jeopardy, a quiz show, where it had to solve the complex questions as well as riddles. Watson had proved that it could understand natural language and can solve tricky questions quickly.  o Year 2012: Google has launched an Android app feature "Google now", which was able to provide information to the user as a prediction. o Year 2014: In the year 2014, Chatbot "Eugene Goostman" won a competition in the infamous "Turing test." o Year 2018: The "Project Debater" from IBM debated on complex topics with two master debaters and performed extremely well. o Google has demonstrated an AI program "Duplex" which was a virtual assistant and which had taken hairdresser appointment on call, and lady on other side didn't notice that she was talking with the machine. ---------------------------------------------------------------------------------------------------------------- 3. Types of AI AI can be categorized based on its capabilities and functionalities. Primarily classifies AI into the following types: 3.1. Strong AI vs. Weak AI This classification is based on the machine’s ability to mimic human cognition. Weak AI (Applied AI or Narrow AI)  Focuses on specific tasks.  Does not possess true understanding or consciousness.  Examples: o Voice assistants (Siri, Alexa). o Chess-playing programs (IBM Deep Blue). Strong AI (General AI)  Aims to replicate human intelligence in all aspects.  Capable of reasoning, learning, and decision-making like a human.  Hypothetical as of today. 3.2. Conventional AI vs. Computational AI This classification is based on the methodologies used in AI development. Conventional AI (Symbolic AI, Logic-based AI)  Uses explicit knowledge representation (rules, logic).  Works well in well-defined environments but struggles with uncertainty.  Examples: o Expert systems (MYCIN, DENDRAL). o 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: o Deep Learning (Convolutional Neural Networks). o Reinforcement Learning (AlphaGo). 3.3. Knowledge-Based AI vs. Non-Knowledge-Based AI This classification is based on how AI processes information. Knowledge-Based AI  Uses predefined rules and knowledge databases.  Performs reasoning based on stored information.  Example: o Expert systems (rule-based medical diagnosis). Non-Knowledge-Based AI  Learns from data without predefined rules.  Uses statistical models and deep learning.  Example: o Neural networks for image and speech recognition. ------------------------------------------------------------------------------------------------------------- 4. Applications of AI 4.1 AI in Natural Language Processing (NLP) NLP enables machines to understand, interpret, and generate human language. Key applications include:  Chatbots & Virtual Assistants – Siri, Google Assistant, ChatGPT.  Machine Translation – Google Translate.  Speech Recognition – Alexa, Cortana.  Text Summarization – AI-based content summarization tools. 4.2 AI in Robotics AI-powered robots are used in industries, healthcare, and space exploration:  Industrial Automation – AI-driven robotic arms in manufacturing.  Medical Robots – AI-powered surgical assistants.  Autonomous Vehicles – AI-driven self-driving cars (Tesla, Waymo).  Humanoid Robots – AI-powered robots like Sophia. 4.3 AI in Automation AI-driven automation optimizes processes by reducing human intervention:  Business Process Automation – Automating routine business tasks.  IT Automation – AI-driven network management, cybersecurity monitoring.  Smart Homes – AI-powered home automation systems (Google Nest, Amazon Echo). 4.4 AI in Human Emotion Recognition AI can analyze human emotions using facial expressions, voice, and physiological signals:  Sentiment Analysis – Detecting emotions in text (used in social media monitoring).  Facial Emotion Recognition – AI-powered emotion detection systems.  AI in Mental Health – AI-based tools for detecting depression and stress levels. --------------------------------------------------------------------------------------------------------------- 5. Intelligent Agents in AI 5.1 Simple Reflex Agents  Act based on the current percept only.  No memory or history tracking.  Follow condition-action rules (if-then rules).  Suitable for fully observable environments.  Example: o Thermostat: Turns heating on/off based on temperature. 5.2 Model-Based Reflex Agents  Maintain an internal model of the environment.  Can handle observable environments.  Store past precepts and use them for decision-making.  Example: o Self-driving cars tracking lane positions over time. 5.3 Goal-Based Agents  Make decisions based on desired goals.  Use search and planning algorithms to achieve objectives.  More flexible than reflex agents.  Example: o GPS navigation system calculating the shortest route. 5.4 Utility-Based Agents  Consider utility functions to measure performance.  Choose actions that maximize long-term benefits.  Useful for decision-making under uncertainty.  Example: o Autonomous trading systems maximizing financial gains. 5.5 Learning Agents   Improve performance by learning from experience.  Use machine-learning techniques.  Have four components: 1. Learning element – Improves agent’s behaviour. 2. Performance element – Makes decisions. 3. Critic – Evaluates agent’s success. 4. Problem generator – Suggests new actions for exploration.  Example: o AI game bots learning from previous matches. -------------------------------------------------------------------------------------------------------------- 6. Intelligent Agents in AI An intelligent agent perceives its environment and acts to achieve optimal outcomes. 6.1 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. 6.2 Structure of an Intelligent Agent 1. Sensors – Gather environmental data (e.g., cameras, microphones). 2. Actuators – Perform actions (e.g., robot arms, speakers). 3. Agent Function – Maps percepts to actions using AI algorithms. 6.3 Examples of Intelligent Agents  Chatbots: AI-powered assistants like Siri.  Autonomous Robots: Warehouse robots optimizing logistics.  Recommendation Systems: Netflix suggesting personalized content. -------------------------------------------------------------------------------------------------------------- 7. Agent Environment The environment in which an agent operates determines how it perceives inputs and makes decisions. The agent-environment interaction is critical for designing effective AI systems. Properties of Agent Environments Agent environments can be classified based on different properties: 7.1. Fully Observable vs. Partially Observable  Fully Observable: The agent has access to complete environmental information at any time. o Example: Chess (all board positions are visible).   Partially Observable: The agent has limited knowledge of the environment. o Example: Self-driving cars (fog, obstacles limit visibility). 7.2. Deterministic vs. Stochastic  Deterministic: The next state of the environment is fully determined by current actions. o Example: Solving a maze.  Stochastic: The next state involves randomness or uncertainty. o Example: Stock market prediction. 7.3. Static vs. Dynamic  Static: The environment does not change while the agent is deliberating. o Example: Crossword puzzle solving.  Dynamic: The environment changes over time, even without agent actions. o Example: Real-time traffic navigation. 7.4. Discrete vs. Continuous  Discrete: The environment has a finite number of states. o Example: Turn-based board games.  Continuous: The environment has infinitely many possible states. o Example: Robot navigation in a physical space. 7.5. Single-Agent vs. Multi-Agent  Single-Agent: The agent operates alone. o Example: A chess-playing AI against a human.  Multi-Agent: Multiple agents interact, competing or cooperating. o Example: Autonomous drone swarms. --------------------------------------------------------------------------------------------------------------- 8. Turing Test in AI The Turing Test, proposed by Alan Turing in 1950, is a method to evaluate machine intelligence. 8.1 Definition The test determines whether a machine can exhibit intelligent behavior indistinguishable from a human. 8.2 How the Turing Test Works  A human evaluator interacts with both a human and a machine via a text-based interface.   If the evaluator cannot reliably distinguish the machine from the human, the AI passes the test. 8.3 Significance of the Turing Test  It shifts the focus from how AI works to how it is perceived.  It sets a benchmark for human-like AI communication. 8.4 Criticism and Limitations  Chinese Room Argument (John Searle, 1980): o A machine can simulate intelligence without true understanding.  Passing the test ≠ true intelligence: o AI can mimic human responses using statistical models (e.g., ChatGPT) without true reasoning. 8.5 Modern Alternatives to the Turing Test  Winograd Schema Challenge: Tests AI’s ability to understand context.  Lovelace Test: Evaluates AI’s creativity. ----------------------------------------------------------------------------------------------------------------