There is a big confusion between Ai and ML. The efforts towards achieving Ai will become futile due to the lack of understanding of the basic Ai

1. — 1
Introduction to Ai & Machine Learning
Madan Dabbeeru

2. — 2
AI is the reproduction of human reasoning and intelligent behavior by computational methods
Intelligent
behavior
Humans
Computer
What is AI?

3. — 3
AI is the reproduction of human reasoning and intelligent behavior by computational methods
What is AI?
Apple Siri
Google Navigation Systems
Roomba Vacuum Cleaner
IBM Watson
Deep Blue
Credit Card Fraud Prediction

4. — 4
Artificial Intelligence in the Movies

5. — 5
Artificial Intelligence in Real Life
A young science (≈ 50 years old)
– Exciting and dynamic field, lots of uncharted territory left
– Impressive success stories
– “Intelligent” in specialized domains
– Many application areas
Face detection AlphaGo Medical Imaging

6. — 6
AI has roots in a number of scientific disciplines
– Computer science and engineering (hardware and software)
– Philosophy (rules of reasoning)
– Mathematics (logic, algorithms, optimization)
– Cognitive science and psychology (modeling high level human/animal thinking)
– Neural science (model low level human/animal brain activity)
– Linguistics
The birth of AI (1943 – 1956)
– McCulloch and Pitts (1943): simplified mathematical model of neurons (resting/firing
states) can realize all propositional logic primitives (can compute all Turing computable
functions)
– Alan Turing: Turing machine and Turing test (1950)
– Claude Shannon: information theory; possibility of chess playing computers
– Boole, Aristotle, Euclid (logics, syllogisms)
History of AI*
*https://www.scaruffi.com/mind/ai.html

7. — 7
Early enthusiasm (1952 – 1969)
– 1956 Dartmouth conference
– John McCarthy (Lisp);
– Marvin Minsky (first neural network machine);
– Alan Newell and Herbert Simon (GPS);
– Emphasis on intelligent general problem solving
– GSP (means-ends analysis);
– Lisp (AI programming language);
Resolution by John Robinson (basis for automatic theorem proving);
– heuristic search (A*, AO*, game tree search)
Emphasis on knowledge (1966 – 1974)
– domain specific knowledge is the key to overcome existing difficulties
– knowledge representation (KR) paradigms
– declarative vs. procedural representation
https://ocs.aaai.org/ojs/index.php/aimagazine/article/download/1848/1746

8. — 8
Weak and Strong AI Claims
Weak AI:
– Machines can be made to act as if they were intelligent.
Strong AI:
– Machines that act intelligently have real, conscious minds.

9. — 9
What is Intelligence?
The TuringTest
A machine can be described as a thinking machine if it
passes the Turing Test. i.e. If a human agent is engaged
in two isolated dialogues (connected by teletype say); one
with a computer, and the other with another human and
the human agent cannot reliably identify which dialogue is
with the computer.
Turing Test: A human communicates with a computer
via a teletype. If the human can’t tell he is talking to a
computer or another human, it passes.
– Natural language processing
– knowledge representation
– automated reasoning
– machine learning
Add vision and robotics to get the total Turing test.

10. — 10
Physical Symbol System Hypothesis
A physical symbol system has the necessary and sufficient means for intelligent action
– a system, embodied physically, that is engaged in the manipulation of symbols
– an entity is potentially intelligent if and only if it instantiates a physical symbol
system
– symbols must designate
– symbols must be atomic
– symbols may combine to form expressions
Newell & Simon 1976
SymbolicAI: Rule-Based Systems
BadElecSys:
IF car:SparkPlusCondition #= Bad Or
car:Timing #= OutOfSynch Or
car:Battery #= Low;
THENcar:ElectricalSystem = Bad;
GoodElecSys:
IF car:SparkPlugCondition #= Ok And
car:Timing #= InSynch And
car:Battery #= Charged;
THENcar:ElectricalSystem = Ok;
If A and B then F
If C and D
and E then K
If F and K then G
If J and G then Goal
A
B
C
D
E
F
G
K
Goal
J
Car-Maintenance Rule-based Systems

11. — 11
Branches of AI
• Logical AI
• Search
• Natural language processing
• Computer vision
• Pattern recognition
• Knowledge representation
• Inference From some facts, others can be inferred.
• Reasoning
• Learning
• Planning To generate a strategy for achieving some 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.
• Agents
• Games
• Artificial life / worlds?
• Emotions?
• Knowledge Management?
• Socialization/communication?
• …

12. — 12
Approaches to AI
• Searching
• Learning
• From Natural to Artificial Systems
• Knowledge Representation and Reasoning
• Expert Systems and Planning
• Communication, Perception, Action
“All AI is search”
– Game theory
– Problem spaces
Every problem is a feature space of all possible (successful or unsuccessful) solutions.
The trick is to find an efficient search strategy.

13. — 13
Learning
Explanation
– Discovery
– Data Mining
No Explanation
– Neural Nets
– Case Based Reasoning
• Searching
• Learning
• From Natural to Artificial Systems
• Knowledge Representation and Reasoning
• Expert Systems and Planning
• Communication, Perception, Action
Explanation No Explanation

14. — 14
Approaches to AI
• Searching
• Learning
• From Natural to Artificial Systems
• Knowledge Representation and Reasoning
• Expert Systems and Planning
• Communication, Perception, Action
Swarm Intelligence
Artificial Neural Networks

15. — 15
Approaches to AI
• Searching
• Learning
• From Natural to Artificial Systems
• Knowledge Representation and Reasoning
• Expert Systems and Planning
• Communication, Perception, Action
• Logic Languages
– Prolog, Lisp
• Knowledge bases
• Inference engines
Father(abraham, isaac). Male(isaac).
Father(haran, lot). Male(lot).
Father(haran, milcah). Female(milcah).
Father(haran, yiscah). Female(yiscah).
Son(X,Y) ¬ Father(Y,X), Male(X).
Daughter(X,Y) ¬ Father(Y,X), Female(X).
Son(lot, haran)?

16. — 16
Approaches to AI
• Searching
• Learning
• From Natural to Artificial Systems
• Knowledge Representation and Reasoning
• Expert Systems and Planning
• Communication, Perception, Action

17. — 17
Approaches to AI
• Searching
• Learning
• From Natural to Artificial Systems
• Knowledge Representation and Reasoning
• Expert Systems and Planning
• Communication, Perception, Action
• Computer vision
• Natural language recognition
• Natural language generation
• Speech recognition
• Speech generation
• Robotics
• Games/Entertainment

18. — 18
environment
AI as an Agent
agent
?
sensors
actuators
?
?
?
model

19. — 19
Intelligent Agents in the World
Natural Language
Understanding
+
Computer Vision
Speech Recognition
+
Physiological Sensing
Mining of Interaction Logs
Knowledge Representation
Machine Learning
Reasoning +
Decision Theory
+
Robotics
+
Human Computer
/Robot
Interaction
Natural Language
Generation
abilities
19

20. — 20
20
Main Areas of AI
• Knowledge representation (including formal logic)
• Search, especially heuristic search (puzzles, games)
• Planning
• Reasoning under uncertainty, including probabilistic reasoning
• Learning
• Agent architectures
• Robotics and perception
• Natural language processing Search
Knowledge
rep.Planning
Reasoning
Learning
Agent
Robotics
Perception
Natural
language
... Expert
Systems
Constraint
satisfaction

21. — 21
Areas of AI and Some Dependencies
Search
Vision
Planning
Machine
Learning
Knowledge
RepresentationLogic
Expert
SystemsRoboticsNLP

22. — 22
What is Artificial Intelligence ?
• making computers that think?
• the automation of activities we associate with human thinking, like decision making, learning ... ?
• the art of creating machines that perform functions that require intelligence when performed by people ?
• the study of mental faculties through the use of computational models ?

23. — 23
What Is Machine Learning?
• Automating automation
• Getting computers to program themselves
• Writing software is the bottleneck
• Let the data do the work instead!

24. — 24
ML in a Nutshell
• Tens of thousands of machine learning algorithms
• Hundreds new every year
• Every machine learning algorithm has three components:
– Representation
– Evaluation
– Optimization
Decision trees
Sets of rules / Logic programs
Instances
Graphical models (Bayes/Markov nets)
Neural networks
Support vector machines
Model ensembles
Etc.
Accuracy
Precision and recall
Squared error
Likelihood
Posterior probability
Cost / Utility
Margin
Entropy
K-L divergence
Etc.
Combinatorial optimization
– E.g.: Greedy search
Convex optimization
– E.g.: Gradient descent
Constrained optimization
– E.g.: Linear programming
Representation Evaluation Optimization

25. — 25
Types of Learning
• Supervised (inductive) learning
– Training data includes desired outputs
• Unsupervised learning
– Training data does not include desired outputs
• Semi-supervised learning
– Training data includes a few desired outputs
• Reinforcement learning
– Rewards from sequence of actions
• Supervised learning
– Decision tree induction
– Rule induction
– Instance-based learning
– Bayesian learning
– Neural networks
– Support vector machines
– Model ensembles
– Learning theory
• Unsupervised learning
– Clustering
– Dimensionality reduction
• Given examples of a function (X, F(X))
• Predict function F(X) for new examples X
– Discrete F(X): Classification
– Continuous F(X): Regression
– F(X) = Probability(X): Probability estimation
Supervised (inductive) learning
• Understanding domain, prior knowledge, and goals
• Data integration, selection, cleaning,
pre-processing, etc.
• Learning models
• Interpreting results
• Consolidating and deploying discovered knowledge
• Loop
ML in Practice