Introduction to Artificial Intelligence

Raghava Mutharaju
Knowledgeable Computing and Reasoning Lab
IIIT Delhi

About Myself

Work Experience and Education

  • Assistant Professor (CSE), IIIT-D
  • Research Scientist, GE Research Center, New York
  • Internships at IBM Research, Bell Labs, Xerox Research, Stardog
  • Software Engineer, CA Technologies, Hyderabad

  • Ph.D from Wright State University, Dayton, OH, USA
  • M.Tech from MNNIT, Allahabad
  • B.Tech from JNTU, Hyderabad


  • Google search
    • Knowledge card on the right side
  • Facebook
    • Personalized advertisements
  • Games
    • Navigation of characters from one location to the other (avoiding obstacles, other characters)
    • Adapting the behaviour of certain characters with the way user is playing the game
    • Computer/Machine playing games such as Chess, Go, Poker, Jeopardy etc.
  • Intelligent Virtual Assistants
    • Google Assistant, Amazon Alexa, Siri, Cortana etc.
  • Healthcare
    • Analyzing medical images (radiology), wearable device data etc.

Natural Intelligence

  • What is natural intelligence?
    • We receive inputs through the sensory organs
    • We take decisions based on these inputs
  • How are we able to take decisions?
    • our knowledge (reading books)
    • learning from others (living/non-living things around us)
      • Kids learn from parents, friends, teachers etc.
    • analogy
      • Birds fly. Ostrich is a bird.
    • gut feeling

Artificial Intelligence (AI)

  • It is the study of the general principles of building intelligent agents.
  • An agent is any device that can perceive its environment through sensors and react to it by taking action to achieve a stated goal.
  • Mimicking human senses, i.e., sight, sound, touch, smell, taste, is a form of perception.
  • Intelligent agents should be able to interpret and process other forms of input such as text, semi-structured, and structured data.
  • Output of an intelligent agent could be in multiple forms such as movement (reaching a destination), decision taken, sound etc.

How do we reach The Indian School from IIIT-D?

  • Multiple paths
  • Which path is the best?
    • Depends on multiple factors
    • Distance, time, road conditions, time of the day
  • Given an initial path and a final path, search algorithms find the best possible route

How can a machine engage with the world?

  • Perception: Gather information about the environment and react to it
  • Information is gathered with the help of sensors
    • Temperature, pressure, movement, air quality, images etc.
  • Machines analyze the sensor data and take actions (sometimes using actuators)


  • Detecting the movement of a person using images from multiple different cameras (with different angles)
  • Counting the number of tigers in India
  • Lip reading: Transcribe the text for a video
    • Automatic Speech Recognition
  • Generating captions for images

How can a machine observe the world and improve?

  • Learning: Machines can observe the world including its own experiences and improve its performance over time
  • Types of learning based on the feedback
    • Supervised learning
    • Unsupervised learning
    • Semi-supervised learning
    • Reinforcement learning

Supervised Learning

  • Agents learn from examples (labelled data)
    • This is a cat
    • This is a tiger
    • Person holding an umbrella
    • Dog is a mammal
  • Is this a cat?
    • I am 90% sure that this is a lion

Unsupervised Learning

  • Learn patterns without any explicit feedback
  • Clustering
  • Taxi driver can learn traffic time and non-traffic time, without explicit feedback

Semi-supervised Learning

  • Limited amount of labelled examples are given
  • Labels might not be accurate
  • Need to find patterns from unlabelled data
  • Guess a person's age from their picture
    • We can collect some limited labelled data
    • People might lie about their age

Reinforcement Learning

  • Agent learns from a series of reinforements - reward or punishment
  • Eg: Tip to a barber
  • A reward function exists. An action is taken if it leads to a reward
  • Exploration vs Exploitation trade-off
    • Picking the best possible ripe mango in 15 mins from a tall tree with many branches

Natural Language Processing

  • Extracting knowledge expressed in the natural language (books, blogs, articles, tweets)
  • Communicate with humans using natural language
  • Agents should be able to parse sentences and make sense out of it
  • Applications: Speech recognition (speech to text), machine translation (text to speech), information retrieval

Knowledge Representation and Reasoning (KRR)

  • Techniques to capture knowledge about the world in a form that machines can understand
    • A Car is a type of Vehicle
    • Car has exactly four wheels
    • Car has at least two doors and at most four doors
  • Reasoning is the process of deriving new facts (knowledge) based on existing facts
    • All birds fly
    • Pigeon is a bird
    • Can Pigeon fly?
  • KRR is the field of AI that helps an agent to use what it knows (background knowledge) to decide what to do

Applications of KRR

  • Unstructured data to structured data
    • Search Engine Optimization:
    • Adding semantics to the data
  • Data Integration
  • Recommendations
    • Drug recommendation based on symptoms
  • Question Answering
    • Who is the president of India who was also a scientist?

Structured Data

  • <Delhi> <capitalOf> <India>
  • RDF triple is called a RDF Statement
  • Triple can be represented as a directed labelled graph
RDF Graph
Image source:

Knowledge Graph

Knowledge Graph
Image source:


  • Robots are physical agents that perform tasks in the physical world
  • Robots are generally equipped with sensors, arms, wheels/legs, joints
  • Social robots
    • Robots interact with kids with autism spectrum disorder (ASD)
  • Robots in industrial settings (assembly, welding, packaging and labelling)

Philosophical and Ethical Issues

  • Autonomous vehicles
  • Will we lose jobs?
  • Will robots take over the world?