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A quick peek into AI
SUBHENDU DEY
Executive Architect / Associate Partner, Cloud Advisory and AI solutions, IBM Services
August 1, 2020
Special thanks to:
Excel@AUR
Excel @ Alumni University Relationship
An initiative by Jadavpur Alumni Association – Singapore Chapter
2
Disclaimer
 The material presents authors' personal view. It does not necessarily present any
organization's official position.
3
Content
 History of AI
 What is AI
 How to approach AI
 The opportunity ahead for students
 Q&A
4
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
5
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)
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.
7
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.
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
9
(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
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
10
(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
How to approach AI – automated reasoning
 Deductive reasoning
 Inductive reasoning
 Example:
 Geospatial reasoning
 Temporal reasoning
 Relational reasoning
11
(Total) Turing Test
 natural language
processing
 knowledge
representation
 automated
reasoning
 machine learning
 computer vision
 robotics
Theory Hypothesis Patterns Confirmation
Observation Patterns Hypothesis Theory
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.
12
(Total) Turing Test
 natural language
processing
 knowledge
representation
 automated
reasoning
 machine learning
 computer vision
 robotics
x
x
x
x x
x
x
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
13
 Market forces
 Contactless interactions
 Cost optimization
 Bias reduction
 React faster
 Better risk assessment
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
14
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
Q&A 15
References
 A. M. Turing (1950) Computing Machinery and Intelligence. Mind 49: 433-460.
 Artificial Intelligence A Modern Approach – 3rd Edition
16
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.
17
1 Source © 2018 Gartner, Inc.
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:
18
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.

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A quick peek into the word of AI

  • 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 2
  • 3. Disclaimer  The material presents authors' personal view. It does not necessarily present any organization's official position. 3
  • 4. Content  History of AI  What is AI  How to approach AI  The opportunity ahead for students  Q&A 4
  • 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 5
  • 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. 7
  • 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 9 (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 10 (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 11 (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. 12 (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 13  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 14 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 16
  • 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. 17 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: 18 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.

Editor's Notes

  1. 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.
  2. 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.
  3. What do you mean by ”Improve business functions”? Business functions could be – - Topline growth, new business opportunity - bottom line improvement, automation, productivity improvement, cheaper
  4. 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
  5. Various types of knowledge: Declarative Procedural Meta Heuristic Structural Expectation from KR system Representational accuracy Inferential adequacy Inferential efficiency Acquisitional efficiency
  6. There are other reasoning which is not discussed here: Abductive reasoning Common sense reasoning Monotonic reasoning Non-monotonic reasoning