Prof. Garain discusses in brief on the backgrounds of learning algorithms & major breakthroughs that have been made in the field of machine perception in the last 50 yrs. He also discusses the role of statistical algorithms like artificial neural network, support vector machines, and other concepts related to Deep Learning algorithms. Along with the above, Prof. Garain touched upon the basics of CNN & RNN, Long Short-Term Memory Networks (LSTM) & attention network & illustrate all of these using real-life problems. Several state-of-the-art problems like image captioning, visual question answering, medical image analysis etc. were discussed to make the potential of deep learning algorithms understandable. Prof. Utpal Garain is one of the leading minds in Kolkata in the field of Neural Networks & Artificial Intelligence. His research interest is now focused on AI research, especially exploring deep learning methods for language, image and video analysis including NLP tools, OCRs, handwriting analysis, computational forensics and the like.

1. Advances in ML and rebirth of AI
Utpal Garain
Indian Statistical Institute
https://www.isical.ac.in/~utpal/
https://www.facebook.com/utpal.garain.5

2. Cognitive Computing
• Systems that learn at scale, reason with purpose and
interact with human naturally.
• Products of the field of Artificial Intelligence (AI)
• Man-machine symbiosis by JCR Licklider (1955)
• Aims
• To let computer facilitate formulative thinking as they now facilitate the
solution of formulated problems
• To enable man and computers to cooperate in making decisions and
controlling complex situations with flexible dependence on
predetermined programs

3. Birth of Cognitive Computing
• Lets revise the computing history
• The tabulating era (1900s till WW-II)
• The programming era (1950s till date)
• The cognitive era (21st Century ---)
• AI though introduced in 1955, could NOT show much promise till the
last century
• Remained as a hype
• Though some successful expert systems were developed

4. Catalysis of Progress
• In 1990s, some techniques like neural networks, genetic algorithms,
etc. received fresh attention.
• They could avoid some limitations of expert systems (features, rules)
• AI got rebirth
• Moore s la
• Doubling in capacity and speed every 18 months for six decades
• From mainframe to personal computer and to the smartphones and tablets

5. The rise of Cognitive
• Big data
• Volume of data is increasingly rapidly
• Social media, mobile devices, low- ost se sors, …
• The Internet and the cloud
• They make available vast amount of data and information to any Internet-
connected computing device
• New Algorithms
• Development in Machine Learning algorithms
• Neural Nets, SVMs, Deep Learning
• Advancement in Reasoning

6. AI, ML and DL
• Machine learning
• Feature description
• Domain expertise
• Character recognition, face
re og itio , …
• Deep learning
• Automatic feature learning
• Role of three things
• Big Data
• Computing resources
• Newer algorithms

7. Success Story: Watson at the Jeopardy
• Watson
• Open domain QA machine
• Jeopardy
• An American Quiz show
• 1964 – till date
• Answer and question format
• Open domain questions
• Clues are given
• Speed is a factor
• In 2011, Watson won a 2-game
Jeopardy Match against the all-time winners (Ken and Brad)
• Beginning of a new computing paradigm: Cognitive Computing
• Essence is: LEARN => UNLEARN => RELEARN
Inexact solutions for inexact problems

8. Brain Storming-I
• You are asked to design a technology (surely cognitive) for
• Measuring effectiveness of a workshop
• Come up with your design
• Could be very much hypothetical, fiction like..
• Fictions make reality today

9. Image Caption Generation (2015)

10. DL @ Indian Statistical Institute
• OCR for printed

11. DL @ Indian Statistical Institute
• Machine Recognition of handwritten text
Doctor’s Prescription:
Vocabulary based HOCR

12. Unconstrained Handwriting: use of RNN
• BLSTM
• 2 hidden layers
• 200 neurons in
each layer
• CTC layer consisting
of 917 nodes
• 2300 lines for
training
• Character
recognition
accuracy: 75.4%

13. In which script are you writing?

14. Evaluating Machine Comprehension
• Is machine smarter than grade-VI students?
• A textbook article
• Multiple choice questions
• 2 (true/false) to 7 options

15. Visual Question-Answering

16. Health Analytics: Psoriasis Analysis

19. Sensors are changing our life

20. Beyond Reading

22. Brain Storming - I
• You are asked to design a technology for
• Measuring effectiveness of a workshop
• Come up with your design
• Could be very much hypothetical, fiction like..
• Fictions make reality today

23. Four Principles of Today’s AI Technology
• Learn and Improve
• Inexact solutions to unsolved problems
• Learn from data and human
• LEARN -> UNLEARN -> RELEARN
• Speed and Scale
• Ma hi e s ad a tage o er hu a i deali g ith high
volume of data and complex calculations
• Assist and Augment Human Cognition
• Human cannot handle the volume of information and
penetrate the complexity
• Interact in a Natural way
• Adapt human approaches and interfaces
• Aims to deliver higher level of human cognition

24. Technical Requirements
• Probability and statistical Inference (automated
reasoning)
• Optimization techniques
• Pattern recognition principles
• Feature, clustering and classification
• Image processing, computer vision, speech
recognition, language understanding
• Knowledge graph
• Ontologies, Semantic web
• Neural NLP

25. Language Processing

26. Basics
• An attempt to understand natural language text
• Three dimensions
• Different languages
• E glish, Chi ese, “pa ish, Hi di, Be gali, …
• NLP tools
• Morphological analyser, POS tagger, Chunker, Parser, NER tagger,
A aphora ‘esolutio , …
• Algorithms and models
• HMM, MaxEnt Model, C‘F, PCFG, …

27. What is meant by language understanding
• If we can do
• Translation
• Summarization
• Question-answering

28. Methods
• Rule based
• Statistical
• Example
• POS tag
• I am going to make some tea
• I do t like the ake of this shirt
• Rule based
• Rules are needed
• Statistical
• Annotated data in large volume

29. Neural NLP
• Developments in neural network is redefining NLP
• Recurrent Neural Network
• Convolutional Neural Network
• Reasons
• Unmanned feature extraction (CNN)
• New way of using context (RNN)
• Requirement
• Numerical representation of words

30. Word Embedding

31. Word embeddings: redefining NLP

32. Word embeddings: redefining NLP
• Language model
• New Delhi is our capital city
• I dia s o er ial it is Mu ai
• Kolkata was discovered by Charnok
• Association
• Vector space

33. Word Embedding

34. Word Embedding for Language Model
• The model runs each word in
the 5-gram through to get a
vector representing it and feed
those i to a other odule
called which tries to predict if
the 5-gra is alid or
roke .

35. Use of Word Embeddings
• Word embeddings exhibit an even
more remarkable property:
analogies between words seem to
be encoded in the difference vectors
between words.
• For example, there seems to be a
constant male-female difference
vector

36. Shared representation of word and image
• The basic idea is that one classifies images by
outputting a vector in a word embedding.
• Images of dogs are apped ear the dog ord
vector.
• Images of horses are apped ear the horse e tor.
• Images of auto o iles ear the auto o ile e tor.
And so on.
• The interesting part is what happens when you
test the model on new classes of images.
• For e a ple, if the odel as t trai ed to lassif ats
– that is, to ap the ear the at e tor – what
happens when we try to classify images of cats?

37. Shared representation of word and image

38. Shared representation of word and image
• It turns out that the network is able to handle these new classes of images
quite reasonably.
• Images of ats are t apped to ra do poi ts i the ord e eddi g
space.
• Instead, the te d to e apped to the ge eral i i it of the dog e tor, a d, i
fa t, lose to the at e tor.
• Similarly, the tru k i ages e d up relati el lose to the tru k e tor, hi h
is ear the related auto o ile e tor.

39. On Human Resources
• Shortage of required manpower at almost all levels
• AI task designer
• What can I do with my data?
• Generating Insights (GI)
• DL solution designer
• Strong background in Algorithms, Coding and Statistics
• Tool users
• Knowledge on how to use off-the-shelf tools to develop applications
• Data annotator/curator
• Courses on
• Business analytics
• Statistics, Machine Learning and Deep Learning
• AI Application development
• Low level training, annotation/curation

40. ISI Centre for AI, ML and Data Analytics
• A Centre for theoretical and application oriented research in AI, ML and Big Data
• Identified areas:
• Computer Vision and Image Analysis
• Speech and Language Technology
• Social Media Analytics
• Sensor data analytics
• Health Care Analytics, Computational Biology and Bioinformatics
• Assistive Technology
• Forecast and Emergency Response (including Finance)
• Cosmology and Astro Physics
• Primary activities:
⁻ Public and privately funded projects
⁻ Training programmes and short-term courses
⁻ Development of Human Resources
⁻ Facilitating start-ups by Institute students and scholars