This is the lecture delivered at Jadavpur University for the engineering students. The lecture was organised by the JU Entrepreneurship Cell and Alumni Association, Singapore Chapter.

1. A quick peek into AI
SUBHENDU DEY
Executive Architect / Associate Partner, Cloud Advisory and AI solutions, IBM Services
August 1, 2020

2. Special thanks to:
Excel@AUR
Excel @ Alumni University Relationship
An initiative by Jadavpur Alumni Association – Singapore Chapter
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3. Disclaimer
 The material presents authors' personal view. It does not necessarily present any
organization's official position.
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4. Content
 History of AI
 What is AI
 How to approach AI
 The opportunity ahead for students
 Q&A
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5. History of AI
 1637: Descartes – talks about two tests that
distinguish intelligent machines from real
human.
 1950: Turing Test – operationalizes linguistic
indistinguishability
 1956: the term AI was coined, and Logic theorist
was revealed
 1997: Deep Blue won against Kasparov
 2011: Watson competed on Jeopardy
 2016: AlphaGo wone over Lee Sedol
 2017: Sophia – the first humanoid Citizen
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6. What is AI 6
Human based Ideal Rationality
Reasoning based Thinking Humanly Thinking Rationally
Behavior based Acting Humanly Acting Rationally
(Total) Turing Test
 natural language processing
 knowledge representation
 automated reasoning
 machine learning
 computer vision
 robotics
x Informal (and often non-certain)
knowledge cannot be always
codified in correct logical
notation.
x Practical solving is constrained
by computational resources.
 Weak AI hypothesis - the assertion
that machines could act as if they
were intelligent
 Strong AI hypothesis - the
assertion that machines that do so
are actually thinking (not just
simulating thinking)

7. Summing it up all
AI is the specialized branch of computer science that helps develop software systems
endowed with the intellectual characteristic of humans, such as the ability to understand
and extract meaning from unstructured content, reason, generalize, learn and react
(natural way) from experience.
Often AI enabled software uses foundational technologies like natural language
processing, computer vision, machine/deep learning, robotics and others to provide
manifestation of intellectual characteristics in the form of deep question answering, search
and planning, knowledge representation, process automation and decisioning.
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8. How to approach AI 8
Logicist Approach Non-Logicist Approach
Probabilistic Technique Neuro-Computational Technique
• Classical deductive logic is
monotonic but
commonsense is not.
• Addition of new
information can cause the
previous inferences to fail
• Logic-based AI have
reached an impressive
maturity
• Use conditional
joint/probability of
events.
• Works on maximum
likelihood functions and
a-priori estimates
prediction.
• Example: Naïve based
classification.
• Non-linear functions, easy to
implement with large amount of
data.
• Inspired by the way neurons
work.
• Comprised of serial wiring of
input-activation-output
functions.
• Training is expensive but can be
pre-trained and used in business
functions.

9. How to approach AI – natural language processing
 Broadly divided into two parts
 Information Extraction: automatically extracts structured information from
unstructured and/or semi-structured machine-readable documents and other
electronically represented sources.
 Information Retrieval: obtains information system resources that are relevant to
an information need from a collection of those resources.
 Intermediate storage (inverted index)
 Spell correction / approximation
 Vector space model
 Text classification and clustering
 Document rank / PageRank
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(Total) Turing Test
 natural language
processing
 knowledge
representation
 automated
reasoning
 machine learning
 computer vision
 robotics
Language
detection
Document
segregation
POS
Tagging
Stop-ward
removal
Tokenization
Stemming
Lemmatization
Entity +
Relationship
Recognition

10. How to approach AI – knowledge representation
 While the NLP takes care of decoding the data, it needs to be represented to
generate appropriate output
 Approach to representation
 Simple Rational Knowledge
 Inheritable Knowledge
 Inferential Knowledge
 Procedural Knowledge
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(Total) Turing Test
 natural language
processing
 knowledge
representation
 automated
reasoning
 machine learning
 computer vision
 robotics
Name Age LANG
X 20 EN
Y 34 HN
Simple Relational Knowledge Inferential Knowledge
Perception
Learning
KR Reasoning
Planning
Execution
Lives at
Works at
Spouse of
Happened
at
Person
Organization
Loc ation
Event

11. How to approach AI – automated reasoning
 Deductive reasoning
 Inductive reasoning
 Example:
 Geospatial reasoning
 Temporal reasoning
 Relational reasoning
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(Total) Turing Test
 natural language
processing
 knowledge
representation
 automated
reasoning
 machine learning
 computer vision
 robotics
Theory Hypothesis Patterns Confirmation
Observation Patterns Hypothesis Theory

12. How to approach AI – machine learning
 Supervised learning: A form of learning in which the software tries to learn a function f
given examples, the training data T, of its values at various points in its domain
 𝑻 = {⟨𝑥1, 𝒇(𝑥1)⟩, ⟨𝑥2, 𝒇(𝑥2)⟩, … , ⟨𝑥 𝑛, 𝒇(𝑥 𝑛)⟩}
 Learn function h so that error = 𝑥∈𝑇 𝛿 (𝒇 𝑥 − 𝒉(𝑥)) is minimized
 Unsupervised learning: tries to find useful knowledge out of raw data without any
function associated with input.
 Clustering
 PageRank
 Reinforcement learning: suitable when the machine has to learn over a period of time
and the performance is not judged on one action but a series of actions and their
effect on environment.
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(Total) Turing Test
 natural language
processing
 knowledge
representation
 automated
reasoning
 machine learning
 computer vision
 robotics
x
x
x
x x
x
x

13. Top few opportunities ahead for students
 Virtual assistants – textual + voice based
 Computer vision techniques for image /
video processing
 Text mining and assisted intelligence
 Enterprise search
 Intelligent devices
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 Market forces
 Contactless interactions
 Cost optimization
 Bias reduction
 React faster
 Better risk assessment

14. Opportunity is wide
 Successful AI projects need a variety of roles, not just data science and domain
knowledge to build statistical / machine learning models.
 A typical team composition
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Role Responsibility
Exec sponsor Ensure the AI projects are aligned with the strategy. Obtain startup
funding.
System architect Operationalize the entire suite of machine learning and deep
learning models within the IT framework
Data engineer Define and implement the integration of data into the overall AI
architecture, while ensuring data quality
Data scientist Explore data to extract actionable information for making business
decisions. Typically from STEM field.
DevOps engineer Work with architects, developers, data engineers and the data
scientist to ensure solutions are rolled out and managed.
Business analyst Act as “translators” between the business users and the machine
learning team
Visualization expert Design/Build user interface for AI output
Application developer Build application for embedding AI
Typical team composition
Exec sponsor System architect Data engineer
Data scientist DevOps Engineer Business Analyst
Visualizationexpert Application Developer
Typical team composition

15. Q&A 15

16. References
 A. M. Turing (1950) Computing Machinery and Intelligence. Mind 49: 433-460.
 Artificial Intelligence A Modern Approach – 3rd Edition
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17. Appendix-I: Ability to explain and ethical questions
 Algorithmic decisions being used in various business functions brings in the risk of low
explainability. This has strong legal implication in case ethical questions (e.g. gender
bias, racial bias, or any discriminatory action).
 Explainability is something that needs early attention
 Essentially it needs a set of capabilities that describes a model, highlights its strengths
and weaknesses, predicts its likely behavior, and identifies any potential biases.
 By 2025, 30% of government and large- enterprise contracts for purchase of digital
products and services using AI will require the use of explainable and ethical AI1.
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1 Source © 2018 Gartner, Inc.

18. Appendix-II: Protect from security vulnerability
 AI presents new attack surfaces and thus increases security risks.
 Machine leaning algorithms and the data they they use should be monitored as the
traditional app scan and vulnerability check.
 Security concerns are of various nature1 the project architecture / method should plan
for means to combat with these:
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1 Source © 2018 Gartner, Inc.
SECURITY CONCERNS ACTIONS TO BE TAKEN
Training Data poisoning and bias
injection
Reduce data-poisoning risk by limiting the amount of training
data each user contributes and examining output for shifts in
predictions after each training cycle.
Model theft by reverse engineering
ML algorithms
Detect theft by examining logs for unusual quantities of
or a higher diversity of queries. Block attackers and prepare a
backup plan.
Adversarial samples – a clever
alteration of input data can cause a
misclassification
Proactively defend against adversarial samples by deploying a
diverse set of prediction machines. Generate adversarial
and include them in your training dataset.