UNIT _ I.pdf

There are numerous definitions of what artificial intelligence is. – Artificial intelligence is the study of systems that act in a way that to any observer would appear to be intelligent. - Artificial Intelligence involves using methods based on the intelligent behavior of humans and other animals...

There are numerous definitions of what artificial intelligence is. – Artificial intelligence is the study of systems that act in a way that to any observer would appear to be intelligent. - Artificial Intelligence involves using methods based on the intelligent behavior of humans and other animals to solve complex problems. - Artificial Intelligence is the study of human intelligence and actions replicated artificially, such that the resultant bears to its design can think and act like humans rationality (doing the right thing). - The art of creating machines that performs functions that require intelligence when performed by humans. - Artificial Intelligence is a branch of Science which deals with helping machines finds solutions to complex problems in a more human-like fashion. - This generally involves borrowing characteristics from human intelligence, and applying them as algorithms in a computer friendly way. - GPS - General Problem Solver. Exactly what is AI? AI means Artificial Intelligence which is a branch of computer science concern with the study and creation of computer systems that exhibit some form of intelligence as: - systems that learn new concepts and tasks - Systems that can reason and draw useful conclusions about the world around us. - Systems that can understands a natural language or perceive(feel) and comprehend(understand or participate) a visual scene. - Systems that perform other type of feats that require human types of intelligence. Artificial intelligence can be viewed from a variety of perspectives. – From the perspective of intelligence, AI is making machines "intelligent" acting as we would expect people to act like knowledge, Expert problem solving. From a business perspective AI is a set of very powerful tools, and methodologies for using those tools to solve business problems. From a programming perspective, AI programs focus on symbols rather than numeric processing, problem solving (achieve goals) and search (BFS, DFS). AI programming languages include: LISP (List Processing), developed in the 1950s and PROLOG( Program Logic) was developed in the 1970s. They are the early programming language strongly associated with AI. Artificial Intelligence is a new electronic machine that stores large amount of information and process it at very high speed. AI requires an understanding of related terms such as intelligence, knowledge, reasoning, thought, cognition(gyan or bodh), learning and number of computer related terms. Meaning of Intelligence: Ability of acquire, understand and apply knowledge. Or the ability to exercise thought and reasons. Forms of AI Weak AI is specialized and limited to specific tasks. Strong AI aims for broad, human-like intelligence across various domains. Super AI envisions intelligence that exceeds human capabilities in every way. AI and related fields: AI is generally associated with Computer Science, but it has many important links with other fields such as Math, Psychology, Cognition, Biology and Philosophy, among many others. Our ability to combine knowledge from all these fields will ultimately benefit our progress in the quest of creating an intelligent artificial being. Some other fields of AI are- Logical AI What a program knows about the world in general the facts of the specific situation in which it must act, and its goals are all represented by sentences of some mathematical logical language. The program decides what to do by inferring that certain actions are appropriate for achieving its goals. Search AI programs often examine large numbers of possibilities, e.g. moves in a chess game or inferences by a theorem proving program. Pattern Recognition When a program makes observations of some kind, it is often programmed to compare what it sees with a pattern. For example, a vision program may try to match a pattern of eyes and a nose in a scene in order to find a face. Representation Facts about the world have to be represented in some way. Inference Like when we hear of a bird, we can infer that it can fly, but this conclusion can be reversed when we hear that it is a penguin. Common sense knowledge and reasoning This is the area in which AI is farthest from human-level, in spite of the fact that it has been an active research area since the 1950s. Learning from experience Programs can only learn what facts or behaviors their formalisms can represent Planning Planning programs start with general facts, they generate a strategy for achieving the goal. Epistemology This is a study of the kinds of knowledge that are required for solving problems in the world. Ontology Ontology is the study of the kinds of things that exist. In AI, the programs and sentences deal with various kinds of objects, and we study what these kinds are and what their basic properties are. Heuristics A heuristic is a way of trying to discover something or an idea imbedded in a program. Heuristic functions are used in some approaches to search to measure how far a node in a search tree seems to be from a goal. Heuristic predicates that compare two nodes in a search tree to see if one is better than the other, i.e. constitutes an advance toward the goal, may be more useful. Genetic Programming Genetic programming is a technique for getting programs to solve a task by mating random Lisp programs and selecting fittest in millions of generations. Applications of AI 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. Even there are the myths of Mechanical men in Ancient Greek and Egyptian Myths. Following are some milestones in the history of AI which defines the journey from the AI generation to till date development. Maturation of Artificial Intelligence (1943-1952) o Year 1943: The first work which is now recognized as AI was done by Warren McCulloch 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. 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 which 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 period 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 also 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. Issues of Artificial Intelligence : Artificial Intelligence has the potential to bring many benefits to society, but it also raises some important issues that need to be addressed, including: 1. Bias and Discrimination: AI systems can perpetuate and amplify human biases, leading to discriminatory outcomes. 2. Job Displacement: AI may automate jobs, leading to job loss and unemployment. 3. Lack of Transparency: AI systems can be difficult to understand and interpret, making it challenging to identify and address bias and errors. 4. Privacy Concerns: AI can collect and process vast amounts of personal data, leading to privacy concerns and the potential for abuse. 5. Security Risks: AI systems can be vulnerable to cyber attacks, making it important to ensure the security of AI systems. 6. Ethical Considerations: AI raises important ethical questions, such as the acceptable use of autonomous weapons, the right to autonomous decision making, and the responsibility of AI systems for their actions. 7. Regulation: There is a need for clear and effective regulation to ensure the responsible development and deployment of AI. It’s crucial to address these issues as AI continues to play an increasingly important role in our lives and society. The Future of AI Technologies: 1. Reinforcement Learning: Reinforcement Learning is an interesting field of Artificial Intelligence that focuses on training agents to make intelligent decisions by interacting with their environment. 2. Explainable AI: this AI techniques focus on providing insights into how AI models arrive at their conclusions. 3. Generative AI: Through this technique AI models can learn the underlying patterns and create realistic and novel outputs. 4. Edge AI:AI involves running AI algorithms directly on edge devices, such as smartphones, IoT devices, and autonomous vehicles, rather than relying on cloud-based processing. 5. Quantum AI: Quantum AI combines the power of quantum computing with AI algorithms to tackle complex problems that are beyond the capabilities of classical computers