Robotics and Robot Applications
Lecture 30: Introduction to Artificial Intelligence
Shyamanta M Hazarika
Biomimetic Robotics and Artificial Intelligence Lab
Mechanical Engineering
Indian Institute of Technology Guwahati
s.m.hazarika@iitg.ac.in
What is Artificial Intelligence?
Artificial Intelligence is the ability of machines to seemingly
think for themselves.Artificial Intelligence is
demonstrated when a task performed by a human
and thought of as requiring the ability to learn,
reason and solve problems can be done by a
machine.
© Shyamanta M Hazarika, ME, IIT Guwahati
What is Artificial Intelligence?
AI is the field devoted to building artifacts capable
of displaying, in controlled, well-understood environments,
and over sustained periods of time, behaviors that we
consider to be intelligent, or more generally,
behaviors that we take to be at the heart of what it
is to have a mind.
© Shyamanta M Hazarika, ME, IIT Guwahati
What is Artificial Intelligence?
This gives rise to further questions.
1. What exactly constitutes intelligent behavior?
2. What it is to have a mind?
3. How humans actually manage to behave intelligently?
© Shyamanta M Hazarika, ME, IIT Guwahati
What is Artificial Intelligence?
How humans actually manage to behave intelligently?
question is empirical. It is predominantly for
This
psychology and cognitive science to answer.
insight
However, this question is pertinent! This is because any
into human thought might help build machines
that work similarly.
© Shyamanta M Hazarika, ME, IIT Guwahati
What is Artificial Intelligence?
What it is to have a mind?
This question on what is the mark of the mental, is
philosophical. Thrust on Artificial Intelligence has
lent significant urgency to it. Careful philosophical
contemplation of this question has influenced the
course of Artificial Intelligence itself.
© Shyamanta M Hazarika, ME, IIT Guwahati
What is Artificial Intelligence?
What exactly constitutes intelligent behavior?
specifying precisely what is to
The first question of
count as intelligent behavior, has traditionally been met
by proposing particular behavioral tests whose
successful passing would signify the presence of
intelligence.
© Shyamanta M Hazarika, ME, IIT Guwahati
Dimensions of Artificial Intelligence
Thinking
Rationally
Thinking Humanly Thinking Rationally
Humanly
Acting Humanly Acting Rationally
Acting
© Shyamanta M Hazarika, ME, IIT Guwahati
Dimensions of Artificial Intelligence
1. Think like Human
– model human cognition
2. Think Rationally
– formalize the inference process.
3. Act Rationally
– doing the right thing
4. Act like Human
– exhibit human behaviour
© Shyamanta M Hazarika, ME, IIT Guwahati
Dimensions of Artificial Intelligence
1. Think like Human
– model human cognition
1960s "cognitive revolution": information-processing
psychology
Requires scientific theories of internal activities
of the brain
Example: Newell & Simon’s GPS (General Problem Solver)
© Shyamanta M Hazarika, ME, IIT Guwahati
Dimensions of Artificial Intelligence
2. Think Rationally
– formalize the inference process.
Several Greek schools developed various forms of
logic: notation and rules of derivation for
thoughts;; may or may not have proceeded to the idea of
mechanization
Direct line through mathematics and philosophy to modern AI
© Shyamanta M Hazarika, ME, IIT Guwahati
Dimensions of Artificial Intelligence
3. Act Rationally
– doing the right thing
Rational behavior: doing the right thing - one which is
expected to maximize goal achievement, given the available
information
Does not necessarily involve thinking
– e.g., bionic reflex.
© Shyamanta M Hazarika, ME, IIT Guwahati
Dimensions of Artificial Intelligence
4. Act like Human
– exhibit human behaviour
machines that perform functions
Creating
that require intelligence when the same
tasks are performed by people.
© Shyamanta M Hazarika, ME, IIT Guwahati
Machines with True Intelligence
In 1950 Alan Turing published a
landmark paper in which he speculated
about the possibility of creating
machines with true intelligence. He
noted that "intelligence" is difficult to define and
devised his famous Turing Test.
The Turing Test was the first serious
proposal in the philosophy of
artificial intelligence.
A. M. Turing (1950) Computing Machinery and Intelligence. Mind 49: 433-460.
© Shyamanta M Hazarika, ME, IIT Guwahati
Can machines think?
Alan Turing, laying
the ground for what later became known
as artificial intelligence, starts his landmark paper
Computing Machinery and Intelligence with the words:
I propose to consider the question, ‘Can machines think?’
.
A. M. Turing (1950) Computing Machinery and Intelligence. Mind 49: 433-460.
© Shyamanta M Hazarika, ME, IIT Guwahati
Imitation Game
Turing test or Imitation Game as it was called in the paper, was
put forth as a simple test that could be used to prove that machines
could think.
Human
Human Interrogator
AI System
© Shyamanta M Hazarika, ME, IIT Guwahati
Early Days of AI
In late 1955, Allen Newell and
Herbert Simon developed The
Logic Theorist, considered by
many to be the first AI program.
The program, representing
each problem as a tree
model, would attempt to solve it by
selecting the branch that would most
result in the correct
likely
conclusion.
© Shyamanta M Hazarika, ME, IIT Guwahati
Early Days of AI
In 1956 John McCarthy organized
a conference to draw the talent and
expertise in machine intelligence for a
month of brainstorming. He invited them
to Vermont for The Dartmouth
Summer Research Project on
Artificial Intelligence. From that
point on, the field would be known as
Artificial intelligence. The Dartmouth
conference served lay the
to
groundwork for the future of AI
research.
© Shyamanta M Hazarika, ME, IIT Guwahati
Term ‘Artificial Intelligence’
We propose that a 2 month, 10 man study ofartificial intelligence be
carried out during the summer of 1956 at Dartmouth College in
Hanover, New Hampshire. The study is to proceed on the basis of the
conjecture that every aspect of learning or any other feature of
intelligence can in principle be so precisely described that a
machine can be made to simulate it.
J. McCarthy, M. L. Minsky, N. Rochester, and C.E. Shannon. August 31, 1955.
John McCarthy is one of the "founding fathers" of artificial intelligence,
together with Marvin Minsky, Allen Newell, and Herbert A. Simon.
© Shyamanta M Hazarika, ME, IIT Guwahati
What is involved?
Interaction with the real world i.e., perceive, understand, and
act. For example: a. speech recognition and b. image
understanding.
Reasoning and planning involving a. modeling the external world
b. planning and decision making and c. deal with unexpected
problems and uncertainties.
Learning and Adaptation through Internal models being always
updated such as a baby learning to categorize and recognize
animals
© Shyamanta M Hazarika, ME, IIT Guwahati
What is involved?
Philosophy Logic, Methods of reasoning, Mind as physical
system, Foundations of Learning / Language
Rationality.
Mathematics Formal representation and Proof Theory
Algorithms: computation – Decidability /
Tractability
Statistics/ Modeling uncertainty, Learning from data
Probability
Economics Utility, Decision Theory
© Shyamanta M Hazarika, ME, IIT Guwahati
What is involved?
Neuroscience Neurons as information processing units.
Psychology/ How do people behave, perceive, process
Cognitive Science cognitive information, represent knowledge
Computer Engg. Building fast computers
Control theory Design systems that maximize an objective
function over time
Linguistics Knowledge Representation, Grammars
© Shyamanta M Hazarika, ME, IIT Guwahati
Weak vs. Strong AI
Weak AI aims at building machines that act
intelligently, without taking a position on whether or not the
machines actually are intelligent.
Strong AI is the field devoted to building persons!
Charniak and McDermott (1985) concede in their classic introduction
to AI that we are very far from achieving strong AI.
© Shyamanta M Hazarika, ME, IIT Guwahati
History of AI
Figure 2. Narrow AI's Place in the Long History of AI
SOURCE: GARTNER (OCTOBER 2017)
© Shyamanta M Hazarika, ME, IIT Guwahati
Artificial Intelligence
Artificial Intelligence is the ability of machines to
Artificial
seemingly think for themselves.
Intelligence is demonstrated
when a task, formerly performed by a human
and thought of as requiring the ability
to learn, reason and solve problems, can be
done by a machine.
© Shyamanta M Hazarika, IIT Guwahati
Artificial Intelligence
Intelligence is the ability to solve
problems!
Two strands of AI activity
1. The cognitive approach seeks to understand
how intelligent behaviour arises.
2. The other is an engineering approach and
the goal is to construct intelligent machines.
© Shyamanta M Hazarika, IIT Guwahati
Intelligence requires Knowledge
Knowledge accrues through a
process of learning.
Machines need the ability
to explore the
world and acquire the requisite
knowledge they need for problem solving on
their own.
© Shyamanta M Hazarika, IIT Guwahati
Machine Learning
Artificial Intelligence and machine learning are often used
interchangeably.Machine learning is a
subset of Artificial Intelligence and
focuses on the ability of machines to
receive a set of data and learn for themselves,
changing algorithms as they learn
more about the information they are processing.
© Shyamanta M Hazarika, IIT Guwahati
Machine Learning
© Shyamanta M Hazarika, IIT Guwahati
Machine Learning Philosophy
The philosophy is to automate the
creation of analytical models in
order to enable algorithms to learn
continuously with the help of available data.
The model may be predictive to make
predictions in the future, or descriptive to gain knowledge
from data or both.
© Shyamanta M Hazarika, IIT Guwahati
Machine Learning
Source: Isazi Consulting, 2015. Available from http://www.isaziconsulting.co.za/ machinelearning.html.
© Shyamanta M Hazarika, IIT Guwahati
Supervised Learning
Learn to predict output when given an
input vector
© Shyamanta M Hazarika, IIT Guwahati
Supervised Learning
Classification is a process of categorizing a
given set of data into classes. The process
starts with predicting the class of given data
points. The classes are often referred to as
target, label or categories
Regression models a target prediction value
based on independent variables. It is mostly
used for finding out the relationship between
variables and forecasting
© Shyamanta M Hazarika, IIT Guwahati
Unsupervised Learning
Create an internal representation of
the input e.g. form clusters; extract features
© Shyamanta M Hazarika, IIT Guwahati
Unsupervised Learning
Dimensionality reduction, is the transformation of
data from a high-dimensional space into a low-
dimensional space so that the low-dimensional
representation retains some meaningful properties
of the original data,
Clustering is a Machine Learning technique that
involves the grouping of data points. Given a set of
data points, we can use a clustering algorithm to
classify each data point into a specific group.
© Shyamanta M Hazarika, IIT Guwahati
Reinforcement Learning
Learn action to maximize payoff.
© Shyamanta M Hazarika, IIT Guwahati
Reinforcement Learning
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Evolution of Robotics Research
From Industrial to Service Robots
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Industrial Robots
Robot Manipulators
• Kinematic Calibration
• Motion Planning
• Control
• Teleoperation Systems
• Intelligence
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Mobile Robots
Automated Guided Vehicles
• Locomotive Systems
• Robot Localization
• Robotic Mapping
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Mobile Robots
Mapping and Localization
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Mobile Robots
Walking Robots
• Stability
• Walking Gait
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Mobile Robots
Stability and Gait
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Mobile Robots
Latest Biped Robots
Latest biped robots. ASIMO - Honda Motor Co. HRP-2 - Kawada Industries, Inc. QRIO -
Sony Entertainment. Research in humanoid robotics is currently shifting from locomotion
issues to interaction between humans and robots.
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Robotics + AI and ML – The Impact
Image Source: Internet; Usage: Non-commercial ; Not for publication.
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Digital Twins
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Computer-Assisted Surgery
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Computer-Assisted Surgery
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Robotic Surgery
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daVinci – A success Story
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Automated Patient Monitoring
IoT Elements
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