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Introduction to Artificial Intelligence

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Introduction to Artificial Intelligence

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This is the first lecture of the AI course offered by me at PES University, Bangalore. In this presentation we discuss the different definitions of AI, the notion of Intelligent Agents, distinguish an AI program from a complex program such as those that solve complex calculus problems (see the integration example) and look at the role of Machine Learning and Deep Learning in the context of AI. We also go over the course scope and logistics.

This is the first lecture of the AI course offered by me at PES University, Bangalore. In this presentation we discuss the different definitions of AI, the notion of Intelligent Agents, distinguish an AI program from a complex program such as those that solve complex calculus problems (see the integration example) and look at the role of Machine Learning and Deep Learning in the context of AI. We also go over the course scope and logistics.

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Introduction to Artificial Intelligence

  1. 1. Artificial Intelligence Palacode Narayana Iyer Anantharaman Gmail id: narayana dot anantharaman 9 Aug 2017
  2. 2. What is this course about? • Ultimate AI dream: Build a machine that is indistinguishable from humans. • Google’s CEO Sundar Pichai: “AI First” • This course will cover Applied AI using modern techniques • After taking this course you can: • Develop a formal understanding of AI techniques • Build products that require cutting edge techniques that include and not limited to deep learning techniques Copyright 2016 JNResearch, All Rights Reserved
  3. 3. Human Cognitive tasks and AI applications Humans Can: Typical AI Applications See Image classification, Face Recognition Speak Text to speech Understand speech Speech Recognition : Speech to text Write Handwriting generation, Draw Understand written text Handwriting Recognition, Digit Recognition Drive Self Driving cars Physical tasks: Walk, Run, … Robotics
  4. 4. What AI can do?
  5. 5. AI Definition
  6. 6. AI : Working Definition for our course (Ref: Russel and Norvig)
  7. 7. Four schools of thought (Ref: Russel and Norvig) • Think humanly • Understand how humans think and model this process • Cognitive Science • Act humanly • Turing Test • Knowledge, Reasoning, Language, Understanding, learning • Think rationally • Rational implies thinking or doing the right thing • Use logic to encode the right thing and process inputs with this framework • Act rationally • Define the right thing as: “Maximizing the goal achievement” • A rational agent achieves the best or optimal outcome
  8. 8. So, what is an agent? Agent EnvironmentSensors Actuators Internal StateInternal State • Agent perceives the environment (Percepts) and acts upon the environment in order to maximize achievement of the required goal (Actions) • Looked at from this perspective, an agent is a function that maps the percepts to actions
  9. 9. Intelligent Agents • Intelligent agents interact with the Environment • Interactions through: • Sensors • Actuators • The function f(.) that maps the sensor to actuators is the control policy • Determines how the agent makes the decisions • Those decisions take place many many times in a loop: Perception-Action cycle • The concept of intelligent agents is abstract. One can cast any real world problem using this model – for instance, it is possible to apply this model to search engine design. Fig Credit: Sebastian Thrun, Udacity
  10. 10. Modelling real world problems Can we model the following examples? Identify what the sensors are, what are the actuators and how would you describe the agent? • Home Security Systems: Suppose we have a system that can take pictures or record video on a continuous basis and we are interested in detecting an intruder. • The speech recognizer: Receive the speech input and transcribe • Receive the inputs from location sensors and turn the steering wheel • Look at the last 4 hours data on stock trend of a set of companies and buy the stock of the most promising company
  11. 11. What distinguishes AI programs? • Algorithms that perform complex mathematical operations do these much faster than a human • If a student is perceived more “intelligent” if he solves these problems faster, then why not these programs be termed “intelligent”? • In short, when do we call a program a AI program?
  12. 12. Terminology • Fully versus Partial Observability • Deterministic versus Stochastic • Discrete versus continuous • Benign versus Adversarial
  13. 13. Observability • Fully versus Partially Observable • If what the agent senses momentarily at any time from the environment is completely sufficient to make the decisions it is fully observable • Examples: Chess game, Tennis service, • Environment has an internal state • The agent may be able to fully observe the state or partial • The agent needs internal memory when dealing with partially observable situations • Markov models help us to structure such a memory Fig Credit: Sebastian Thrun, Udacity
  14. 14. Deterministic versus Stochastic • Deterministic: chess moves – outcome is pre determined • Stochastic: Moving the dice – the outcome is not pre determined
  15. 15. Discrete versus Continuous • Examples of actions modelled as a continuous variable • Throwing a dart : infinitely many ways to angle the dart • Turning the steering by an angle (0 to 360 deg, real valued) • Magnitude of acceleration to apply • Actions that are Discrete Variables • Choosing a gear (1, 2, 3, 4, Top, Reverse) in a car with manual transmission system • Deciding (buy, sell, wait) on stocks of a finite, small set of companies and acting • Deciding which elective course to sign up based on the data available and aptitude
  16. 16. Benign versus Adversarial • A benign system is not attempting to defeat the agent • Weather may affect the actions of a self driving car – e.g: reduced visibility and hence increased uncertainty of actions. But it is not an adversary • An adversarial system attempts to score over the agent. It tries to win over and not allow the agent to succeed. • A Generative Adversarial Network (GAN) is modelled as an adversarial game.
  17. 17. AI as uncertainty management • What to do when you don’t know what to do? • Reasons for uncertainty • Sensor faults, limitations • Adversaries : we don’t know what it will do • Stochastic Environments • Ignorance
  18. 18. Uncertainty Management : NLP example
  19. 19. Exercise Analyze wrt Observability, Stochasticity, Discrete/Continuous, Benign/Adversarial, Stationary/Dynamic environment • A “teacher agent” that decides the action to take (Go deeper, Give a break, Ask questions, Repeat the concept) based on its observation of the environment (classroom). • Game of chess • Self Driving car • Face recognition
  20. 20. Modelling Fig Credit: Percy Liang, Stanford
  21. 21. Modelling AI Problems Different types of problems may require different types of approaches • Some problems can be easily represented using state spaces • E.g. Robot navigation through the maze • Problems that can be solved using Machine Learning techniques • E.g. Face recognition • Probabilistic Graphical Models such as Bayes Networks, HMMs • E.g Speech Recognition • Problems that can be well addressed using deductive logic • Given certain propositions and input, perform logical inference – e.g. imagine a chatbot that encodes some knowledge and can reason with the user
  22. 22. Example#1 : Modelling as a Graph Search • Real World Problem • Suppose you are to reach the Bangalore Airport from PESIT (your current location). You prefer the route that leads you to the destination fastest. You are given the map of Bangalore and are provided with information on the traffic congestion along each route and distance. (Assume this doesn’t change till you reach the destination) • Model • Represent the landmarks as nodes (states) of a graph, the goal state being the Airport. Edges represent the connection between the landmarks. Edges are annotated with the time cost of moving from one landmark to the next. • Algorithm • Graph search algorithms such as BFS, DFS, Uniform Cost Search, A* Search etc
  23. 23. Example#2 – Modelling as a ML Problem • Real World Problem • Suppose you are to perform handwriting recognition, where the input is a English text written by hand. • Model • Two possibilities: If the handwriting is recorded using a sensor that captures the strokes, we can model the problem as a time series analysis problem and choose a suitable ML Classifier (HMM, RNN, …) • If the input is an image, we can model the problem as a text recognition in an image, where the input is a tensor and choose a convolutional neural network • Algorithm • Training: Supervised learning using stochastic gradient descent for the type of classifier chosen
  24. 24. Artificial Intelligence, Machine Learning and Deep Learning • The goal of AI is to build human-like intelligence on machines • ML is a core approach to achieve this goal. • Key idea behind ML: Learning from data • ML is narrower in scope relative to AI • DL is a suite of techniques that form a sub set of a broad suite of ML techniques • ML includes a broad variety of techniques like Probabilistic Graphical Models, Decision Trees, Neural Networks etc. The models can be shallow or deep. • Deep learning uses a large number of computing layers stacked vertically (output of one feeds in to the input of the next). • The depth can be spatial or temporal Copyright 2016 JNResearch, All Rights Reserved AI ML DL
  25. 25. Machine Learning • During the first edition of DARPA self driving cars challenge (2004), none of the participants succeeded • In the next edition (2005), 5 cars succeeded, with Stanford Stanley bagging the first position • Machine Learning made all the difference Copyright 2016 JNResearch, All Rights Reserved
  26. 26. When to apply Machine Learning? • Data • No data, no machine learning! • Patterns that exist in the data • If the data doesn’t contain definitive patterns, there is nothing to learn from • No satisfactory algorithm exists • If a satisfactory algorithm exists, no need to do statistical learning Copyright 2016 JNResearch, All Rights Reserved
  27. 27. Machine Learning Copyright 2016 JNResearch, All Rights Reserved
  28. 28. ML Approach Advantages • E.g hte-> hate versus hte->the • Example: tweets to correct words (Edit Distance > 2) • Potential hands on project
  29. 29. Learning From Data Fig Credit: Percy Liang, Stanford
  30. 30. ML – Some taxonomy Copyright 2016 JNResearch, All Rights Reserved Learning Taxonomy What is “learnt”? Parameters, Structure, Hidden Concepts Technique Supervised, Unsupervised, Reinforcement What for? Prediction, Diagnostics, Summarization How? Passive, Active, Online, Offline Outputs Classification, Regression Model Paradigm Generative, Discriminative
  31. 31. Can we learn better? • Can we achieve a human-like performance at least for some narrowly defined tasks? • If I happen to have lots of data, can my learning scale with data size? • If the problem solved by a machine learning classifier is narrowly scoped, how to use a ML approach to solve large, complex problems? • How much of domain expertise we need to have in order to apply ML to our problem? E.g. Should I be an expert in signal processing in order to design a speech recognition system? Should I be a linguist knowledgeable on Kannada in order to develop an English to Kannada machine translator? Copyright 2016 JNResearch, All Rights Reserved
  32. 32. Deep Learning • Large number of layers forming a deep network • The depth can be spatial or temporal • More complex models but less dependency on human experts crafting the best features • Due to the model’s higher capacity, can leverage the data better – more the data you give, better can be the learning Copyright 2016 JNResearch, All Rights Reserved
  33. 33. Feature Learning (fig from Y Bengio) • Representation Learning • Automatically learn the “right” features at each hidden layer • Learn multiple levels of representations increasing in abstraction • Allow effective sharing of the learned parameters across different tasks: Multitask learning
  34. 34. Three reasons to use deep learning • Performance • The difference between 93% to 96% can make all the difference • Make cool technologies usable for a common man. • Broad Applicability (Domain independence) • Not limited to a narrow set of problems • Minimize the need for domain specialized feature engineering • New class of applications • Applications that require higher level semantics as opposed to routine classification • Multimodal fusion • Generative models Copyright 2016 JNResearch, All Rights Reserved
  35. 35. ML Everywhere!: Text, Speech, Image, Video Copyright 2016 JNResearch, All Rights Reserved
  36. 36. Reflex Agents • Actions of a reflex agent depends only on the current inputs • The current percept determines the action • Example: • Imagine driving fast on a road in Bangalore. You suddenly notice a speed hump that is not painted, nor there was any sign board on the road to caution you. The moment you notice the hump that springs up unexpected, you just apply the brakes!
  37. 37. State Space Models • Real world problems are modelled as a graph • Example: Finding the low cost path between 2 cities when there are many paths that are possible • Solutions are represented as paths through the graph. • Our goal is to find the optimal path
  38. 38. Examples • Chinese words are written without spaces • Arabic written without vowels • English:rtfclntllgnc
  39. 39. State Space Models Fig Credit: Percy Liang, Stanford
  40. 40. Variable Based Models Fig Credit: Percy Liang, Stanford
  41. 41. Course Plan • We will use the broad framework used by Stanford AI course • We also draw heavily on the Sebastian Thrun’s courses on Self Driving Cars • We differ in the following aspects: • Deeper deep learning as a tool to build intelligent agents • Hands on: Projects based on TensorFlow • Emphasis on Applied AI : Core computer vision problems, Core NLP problems and discussions on self driving cars Fig Credit: Percy Liang, Stanford
  42. 42. Course Contents • Unit 1: Classical AI techniques • Unit 2: Machine Learning and Deep Learning • Unit 3: Applied AI: Computer Vision • Unit 4: Applied AI: Natural Language Processing • Unit 5: Applied AI: Robotics and Self Driving Cars
  43. 43. Why should I take this course? • AI is a hot topic in the industry • Every major technology company (Google, Apple, Facebook, Microsoft, Adobe, …) has made huge bets on AI • Large number of start ups working in this space and well funded • AI courses and degree programs are highly sought after in the academia • This course with focus on both sound theoretical principles as well as hands on development helps you master the basics
  44. 44. Course Assessment
  45. 45. What are the pre-requisites? • Technical • Probability theory • Calculus • Linear Algebra • Python Programming • Aptitude • Aptitude for AI, Machine Learning and willingness to experiment and a strong commitment to class policy.
  46. 46. How will be the course experience? • Is this an introductory course or a rigorous one? • Rigorous – the course is going to cover both classical AI as well as modern applied AI. • What way it will be rigorous? • Contemporary Research topics will be covered in addition to traditional approaches • Lab work will be intensive • Total effort you will put in during the lab/evaluations/final exam will be sizable. • Will it burn me out? • If you have the right aptitude, it will not. On the contrary you will find the course thrilling. (The best way is to find out if this is worth it is by consulting your seniors who have taken NLP or AML in the recent times!)
  47. 47. Course Timings • We will have 2 classes per week, each lasting 2 hours. One of the two classes will be on Saturday. • As I work in the industry, there may be a few rescheduling of the classes on some occasions. We will keep such disruptions to a minimum.

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