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Artificial intelligence

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Artificial intelligence

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What is Artificial Intelligence(AI)? , Evolution , Applications of AI? , Features of AI , What is Intelligence and its types?,
What are Agents and Environment? , Fear of AI , Machine Learning , Difference between AI, ML and Deep Learning ,
Applications of ML , Algorithms of AL and ML , Future of AI

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What is Artificial Intelligence(AI)? , Evolution , Applications of AI? , Features of AI , What is Intelligence and its types?,
What are Agents and Environment? , Fear of AI , Machine Learning , Difference between AI, ML and Deep Learning ,
Applications of ML , Algorithms of AL and ML , Future of AI

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Artificial intelligence

  1. 1. ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING Dr.M.Inbavalli Vice Principal Marudhar Kesari Jain College for Women Vaniyambadi-635751 1
  2. 2. Overview • What is Artificial Intelligence(AI)? • Evolution • Applications of AI? • Features of AI • What is Intelligence and its types? • What are Agents and Environment? • Fear of AI • Machine Learning • Difference between AI, ML and Deep Learning • Applications of ML • Algorithms of AL and ML • Future of AI 2
  3. 3. • Artificial Intelligence • Ability for a machine to perform tasks that would normally human do. • artificial intelligence is making machines "intelligent“ - acting as we would expect people to act. • Capability of machine to imitate intelligent human behavior-Merriam Webster. • The inability to distinguish computer responses from human responses is called the Turing test. • Intelligence requires knowledge . 3 3
  4. 4. • Purpose of AI 4 4
  5. 5. • Artificial Intelligence • 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 includes the study of symbolic programming, problem solving, and search. • Typically AI programs focus on symbols rather than numeric processing. or Problem solving - achieve goals. • Search - seldom access a solution directly. Search may include a variety of techniques. • include: • – LISP, developed in the 1950s • LISP is a functional programming language with procedural extensions 5 5
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  9. 9. S. No Programming Languages Features 1 LISP developed in 1950s A functional programming language with procedural extensions specifically designed for processing heterogeneous lists -- typically a list of symbols. Features of LISP are run- time type checking, recursion, dynamic typing, Automatic storage management, High-order functions, self-hosting compiler, and tree data structure. 2 PROLOG developed in 1970s Prolog is a rule-based and declarative language containing facts and rules based on first order logic,Features- pre-designed search mechanism, recursive nature, abstraction, non determinism, backtracking mechanism, and pattern matching. 3 Object- oriented languages - Smalltalk, Objective C, C++ Object oriented extensions to LISP (CLOS - Common LISP Object System) and PROLOG (L&O - Logic & Objects) are also used. 9 AI programming languages 9
  10. 10. • Artificial Intelligence • Python. • R Programming-Data Science & Big Data Analytics • Java • Haskel • Julla • Scala • Rust • Erlang • Mathematica • php 10 10
  11. 11. What is Artificial Intelligence Input: Data Sensors Images Artificial Intelligence Output: Action Movement Text 11
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  13. 13. • Applications of AI • Game Playing- video games • Speech Recognition • Understanding Natural Languages • Image Recognition • Automated customer support-Sending reminders, notifications, timing alerts, messages, currencies to Rs. Conversion • Health care-accuracy in diagnosing • Finance- accuracy in decision-stock market • Smart cars and drones • Travel and navigation-book Trips/google maps 13
  14. 14. • Applications of AI • Social Media • Smart home • Creative arts/Animations • Security and Survillenace • Uber • Loan and Credit card processing • Online banking • Spam filters • Identification Technologies-Biometric • Intrusion Detection • Agriculture 14
  15. 15. • Applications of AI • Customer Preferences - based on previous searches Eg.Netflix • Chat boxes-NLP, virtual assistant google duplex • Space Exploration-Kepler telescope in order to identify a distant eight- planet solar system. 15
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  18. 18. • Artificial Intelligence Subfields - AI is EVERYWHERE – • Machine Translation • - Google Translate • - Spam Filters • Digital Personal Assistants • - Siri - Google Assistant • - Cortana • - Alexa 18
  19. 19. • Artificial Intelligence Subfields - AI is EVERYWHERE • - Game players • - DeepBlue • - AlphaGo • - “The Computer” in video games • - Speech Recognition Systems • - IBM • - Dragon • - Image Recognitions Systems • - AlgorithmicTrading Systems • - Black-Scholes Model (Caused crash in 1987) • - AutomatedTrading Services • - Recommender Systems • - Amazon’s Suggestions • - Google Ads 19
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  23. 23. • What is Intelligence? • ability of a system to calculate, reason, perceive relationships and analogies, learn from experience, store and retrieve information from memory, solve problems, comprehend complex ideas, use natural language fluently, classify, generalize, and adapt new situations • Types of Intelligence 23
  24. 24. Intelligence Description Example Linguistic intelligence The ability to speak, recognize, and use mechanisms of phonology (speech sounds), syntax (grammar), and semantics (meaning). Narrators, Orators Musical intelligence The ability to create, communicate with, and understand meanings made of sound, understanding of pitch, rhythm. Musicians, Singers, Composers Logical-mathematical intelligence The ability of use and understand relationships in the absence of action or objects. Understanding complex and abstract ideas. Mathematicians, Scientists Spatial intelligence The ability to perceive visual or spatial information, change it, and re-create visual images without reference to the objects, construct 3D images, and to move and rotate them. Map readers, Astronauts, Physicists 24
  25. 25. Intelligence Description Example Bodily-Kinesthetic intelligence The ability to use complete or part of the body to solve problems or fashion products, control over fine and coarse motor skills, and manipulate the objects. Players, Dancers Intra-personal intelligence The ability to distinguish among one’s own feelings, intentions, and motivations. Gautam Buddhha Interpersonal intelligence The ability to recognize and make distinctions among other people’s feelings, beliefs, and intentions. Mass Communicators, Interviewers 25
  26. 26. • What is Intelligence Composed of? 26
  27. 27. • Intelligence • Reasoning − It is the set of processes that enables us to provide basis for judgement, making decisions, and prediction. • Inductive Reasoning-specific observations to makes broad general statements • Example − "Nita is a teacher. Nita is studious. Therefore, All teachers are studious." • Deductive Reasoning-It starts with a general statement and examines the possibilities to reach a specific, logical conclusion. • Example − "All women of age above 60 years are grandmothers. Shalini is 65 years. Therefore, Shalini is a grandmother.“ Learning-gaining knowledge or skill by studying, practising, being taught, or experiencing something. Types-Auditory,stimulus,perceptutional, observational etc Problem Solving -Decision making Perception-sensor Linguistic – Ability to speak,listen,write 27
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  29. 29. • Types of Learning Based on Ability • Artificial Narrow Intelligence-does not posses any thinking ability. Eg.-Siri, Alexa, Self-driving cars, Alpha-Go, Sophia the humanoid and so on • Artificial General Intelligence-ability to think and make decisions • Eg-Biological , agricultural, drowns , scientific etc • Artificial Super Intelligence-super pass humans • Eg.film, fictions • Types of Learning Based on Functionality • Reactive Intelligence- operate solely based on the present data, taking into account only the current situation • cannot form inferences from the data to evaluate their future actions. • perform a narrowed range of pre-defined tasks.Eg. IBM Chess program 29
  30. 30. • Limited Memory AI • used to store past experiences and hence evaluate future actions. Eg.Self Driving car-use sensor for decision • Theory of Mind AI • major role in psychology • emotional intelligence so that human believes and thoughts can be better comprehended. • Self Aware • own consciousness and become self-aware. • Superintelligence • Futuristics 30
  31. 31. • Branches of AI • Logical AI-mathematical logical language, do by inferring • Search —large numbers of possibilities Eg.chess • Pattern Recognition-try to match a pattern of eyes and a nose in a scene in order to find a face. Eg.Fraud detection • Representation-Visuals using logics • Inference-Mathematical logical deduction Eg. when we hear of a bird, we infer that it can fly, monotonic • Common sense knowledge and Reasoning- futuristic • Learning from experience-types of learning • Planning – scheduling , drawings • Ontology-Deals with objects and its properties • Heuristic-search or to measure how far a node in a search tree • Genetic-Hierarchial and high level problem solving 31
  32. 32. • Difference between Humans and Machines S.No Humans Machines 1 Perceive by patterns perceive by set of rules and data. 2 store and recall information by patterns Eg:40404040 searching algorithms 3 figure out the complete object even if some part of it is missing machines cannot do 32
  33. 33. Task Domains of Artificial Intelligence Mundane (Ordinary) Tasks Formal Tasks Expert Tasks •Perception • Computer Vision • Speech, Voice •Mathematics •Geometry •Logic •Integration and Differentiation •Engineering •Fault Finding •Manufacturing •Monitoring •Natural Language Processing • Understanding • Language Generation • Language Translation •GamesGo •Chess (Deep Blue) •Ckeckers Scientific Analysis Common Sense Verification Financial Analysis Reasoning Theorem Proving Medical Diagnosis Planing Creativity •Robotics • Locomotive 33
  34. 34. Types Machine Learning Deep Learning Natural Language Processing Robotics Expert Systems Fuzzy Logic 34
  35. 35. • What are Agent and Environment? • Artificial intelligence is defined as a study of rational agents A rational agent could be anything which makes decisions, as a person, firm, machine, or software. It carries out an action with the best outcome after considering past and current percepts • Human Agent, Robotic agent, Software Agent 35
  36. 36. • An AI system is composed of an agent and its environment. The agents act in their environment. The environment may contain other agents. An agent is anything that can be viewed as : • perceiving its environment through sensors and • acting upon that environment through actuators • Agent Terminology • Performance Measure of Agent • Behavior of Agent • Percept –perceptual instance at a given instance • Percept Sequence-perceived till date • Agent Function-map from percept sequence to an action 36
  37. 37. • Exampes:AI assistants, like Alexa and Siri, • they use sensors to perceive a request made by the user and the automatically collect data from the internet without the user's help. They can be used to gather information about its perceived environment such as weather and time. 37
  38. 38. • Examples of Agent:- A software agent has Keystrokes, file contents, received network packages which act as sensors and displays on the screen, files, sent network packets acting as actuators. A Human agent has eyes, ears, and other organs which act as sensors and hands, legs, mouth, and other body parts acting as actuators. A Robotic agent has Cameras and infrared range finders which act as sensors and various motors acting as actuators. • Types of Agents • Simple Reflex Agents • Model-Based Reflex Agents • Goal-Based Agents • Utility-Based Agents • Learning 38
  39. 39. • Fear Over AI • - a good example rajini enthiran movie • AI will produce biased outcomes • Algorithms are only as good as the data that they are trained on. So if a dataset includes the historical biases of an organization, then the predictions it makes will reflect that historical behavior.– ignore expert who belong to other behaviour • We (will) have no idea why AI does what it does-black box • Fear of unforseen - automatic vehicle driving • AI is a Job killer • Bad people do bad things • Privacy Considerations-Automatic recording • Lacking out of box thinking 39
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  41. 41. • Machine Learning • Machine learning is concerned with algorithms which train a machine learning model to learn how to perform tasks using data rather than hand-coded rules. • Machine learning data most frequently takes the form of input-label pairs (x, y) where x is the input to a machine learning model and y is the label or expected output • Data is often split into three partitions: training data, validation/development data, and testing data 41 41
  42. 42. Artificial Intelligence Machine learning Artificial intelligence is a technology which enables a machine to simulate human behavior. Machine learning is a subset of AI which allows a machine to automatically learn from past data without programming explicitly. The goal of AI is to make a smart computer system like humans to solve complex problems. The goal of ML is to allow machines to learn from data so that they can give accurate output. In AI, we make intelligent systems to perform any task like a human. In ML, we teach machines with data to perform a particular task and give an accurate result. Machine learning and deep learning are the two main subsets of AI. Deep learning is a main subset of machine learning. AI has a very wide range of scope. Machine learning has a limited scope. AI is working to create an intelligent system which can perform various complex tasks. Machine learning is working to create machines that can perform only those specific tasks for which they are trained. AI system is concerned about maximizing the chances of success. Machine learning is mainly concerned about accuracy and patterns. 42 42
  43. 43. Artificial Intelligence Machine learning The main applications of AI are Siri, customer support using catboats, Expert System, Online game playing, intelligent humanoid robot, etc. The main applications of machine learning are Online recommender system, Google search algorithms, Facebook auto friend tagging suggestions, etc. On the basis of capabilities, AI can be divided into three types, which are, Weak AI, General AI, and Strong AI. Machine learning can also be divided into mainly three types that are Supervised learning, Unsupervised learning, and Reinforcement learning. It includes learning, reasoning, and self-correction. It includes learning and self-correction when introduced with new data. AI completely deals with Structured, semi-structured, and unstructured data. Machine learning deals with Structured and semi- structured data. 43 43
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  45. 45. Learning Proceeding Cont. 7/22/2020 45 Machine Learning is a type of Artificial Intelligence that provides computers with the ability to learn without being explicitly programmed AI M L DL Part of the machine learning field of learning representations of data. Exceptional effective at learning Utilizes learning algorithms that derive meaning out of data by using a hierarchy of multiple layers that mimic the neural networks of our brain If the system is provided with tons of information, it begins to understand it and respond in useful ways 45
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  47. 47. Learning Proceeding Cont. 7/22/2020 47 Machine Learning SOURCE ANDREW NG © HBR.ORG47
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  49. 49. • Searching is the universal technique of problem solving • Popular AI Search Algorithms • Single Agent Pathfinding Problems • Travelling Salesman Problem, Rubik’s Cube, and Theorem Proving. 49 49
  50. 50. • Search Terminology • Problem Space − It is the environment in which the search takes place. (A set of states and set of operators to change those states) • Problem Instance − It is Initial state + Goal state. • Problem Space Graph − It represents problem state. States are shown by nodes and operators are shown by edges. • Depth of a problem − Length of a shortest path or shortest sequence of operators from Initial State to goal state. • Space Complexity − The maximum number of nodes that are stored in memory. • Time Complexity − The maximum number of nodes that are created. • Admissibility − A property of an algorithm to always find an optimal solution. • Branching Factor − The average number of child nodes in the problem space graph. • Depth − Length of the shortest path from initial state to goal state. 50 50
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  54. 54. • THE FUTURE OF ARTIFICIAL INTELLIGENCE • Artificial intelligence is impacting the future of virtually every industry and every human being. Artificial intelligence has acted as the main driver of emerging technologies like big data, robotics and IoT, and it will continue to act as a technological innovator for the foreseeable future. 54 54
  55. 55. ThankYou 7/22/2020 55 ThankYou 55

Notas del editor

  • But what is Artificial Intelligence. In general, it is the ability for a machine to perform tasks that would normally require a person to do. And just like people, it’s the ability to take information, make decisions based on it and cause an action to be taken. Such as moving an arm, creating and image or text, or providing a suggestion.

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