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.
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
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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If there were machines which bore a resemblance to our body and imitated our actions as far as it was morally possible to do so, we should always have two very certain tests by which to recognise that, for all that, they were not real men
that they could never use speech or other signs as we do when placing our thoughts on record for the benefit of others.
that although machines can perform certain things as well as or perhaps better than any of us can do, they infallibly fall short in others, by which means we may discover that they did not act from knowledge, but only for the disposition of their organs.
If we are going to say that a given program thinks like a human, we must have some way of determining how humans think. We need to get inside the actual workings of human minds. There are three ways to do this:
through introspection—trying to catch our own thoughts as they go by
through psychological experiments—observing a person in action and
through brain imaging—observing the brain in action.
What do you mean by ”Improve business functions”?
Business functions could be –
- Topline growth, new business opportunity
- bottom line improvement, automation, productivity improvement, cheaper
Inverted index
- posting list vs incident matrix
- scan strategy, sequential scan vs skip pointers
- unigram, bi-gram, tri-gram index
- k-gram index helps in partial search as well
Spell correction / approximation
- edit distance
- soundex
Vector space model
- tf-idf
Classification
- KNN
- NaiveBayes
Various types of knowledge:
Declarative
Procedural
Meta
Heuristic
Structural
Expectation from KR system
Representational accuracy
Inferential adequacy
Inferential efficiency
Acquisitional efficiency
There are other reasoning which is not discussed here:
Abductive reasoning
Common sense reasoning
Monotonic reasoning
Non-monotonic reasoning