The slides cover introduction to the basic concepts of Artificial Intelligence and its real world applications.

1. Artificial Intelligence
Anand J Kulkarni
PhD, MS, BEng, DME
Research Professor & Associate Director
Institute of Artificial Intelligence
Dr Vishwanath Karad MIT World Peace University
Pune 411 038, MH, Bharat (India)
Email: anand.j.kulkarni@mitwpu.edu.in; kulk0003@ntu.edu.sg
Ph: 91 70301 29900
Dr Anand J Kulkarni 1

2. Speaker Information
Post Doctorate Research Fellow
(Cross Border Supply Chain Management Project)
PhD (in Nature Inspired Optimization)
MASc (in Artificial Intelligence)
BEng (in Mechanical Eng)
Diploma (in Mechanical Eng)
Dr Anand J Kulkarni 2

3. Speaker Information
AI optimization methodologies/Theories Pioneered
1. Probability Collectives
2. Cohort Intelligence
3. Expectation Algorithm
4. Socio Evolution & Learning Optimization Algorithm
5. Ideology Algorithm
6. LAB Algorithm
7. Golf Sport Inspired Search Algorithm
Journal Articles: 36
Book Chapters: 25
Conference Papers: 30
News Paper Articles: 35
3
Dr Anand J Kulkarni

4. Speaker Information
Editorial Activities
Lead Editor:
Engineering Optimization: Methods and Applications
Book Series Editors: Kulkarni, A.J., Gandomi, A.H., Mirjalili, S.A., Lagaros, N.D., Liao, T.W.
Advances in Metaheuristics
Book Series Editors: Kulkarni, A.J. & Siarry, P.
Associate Editor
​Engineering Applications of Artificial Intelligence (Elsevier)
Systems and Soft Computing (Elsevier)
4

5. Speaker Information
Advances in Metaheuristics
Book Series Editors:
Kulkarni, A.J. & Siarry, P.
Editorial Activities
Engineering Optimization: Methods and Applications
Book Series Editors:
Kulkarni, A.J., Gandomi, A.H., Mirjalili, S.A., Lagaros, N.D., Liao,
T.W.
5

6. Dr Anand J Kulkarni 6

7. Data Science-AI-ML-Statistics
Dr Anand J Kulkarni 7

8. What is Intelligence
• Every living creature’s intelligence is characterized by
• Perception
• Reasoning
• Inference
• Learn
• Imitate/Mimic/Follow
• Creative
• Adapt
Dr Anand J Kulkarni 8

9. Intelligence: Perception
Dr Anand J Kulkarni 9

10. Intelligence: Perception
Dr Anand J Kulkarni 10

11. Intelligence: Reasoning
Dr Anand J Kulkarni 11

12. Dr Anand J Kulkarni 12

13. Intelligence: Inference
• Making inferences is like being a detective; you may look at all the
details and put them together to draw a meaningful conclusion.
Dr Anand J Kulkarni 13

14. Intelligence: Mimic/Copy/Follow/Imitate
Dr Anand J Kulkarni 14

15. Intelligence: Adapt and Evolve
Dr Anand J Kulkarni 15

16. Intelligence: Adapt and Evolve
Dr Anand J Kulkarni 16

17. Dr Anand J Kulkarni 17

18. Darwin’s Theory of Adaptation and Evolution
1809-1882
Dr Anand J Kulkarni 18

19. History: Natural Intelligence into Artificial
Intelligence (AI)
• Dr Alan Turing (1950): Computing Machinery and Intelligence
• Very High computer prices (in several million rupees)
• Computer with no Memory
• Concept of (humanlike) Thinking Machines initialized
• Scope limited to Robots and Automation
Dr Anand J Kulkarni 19

20. History: Natural Intelligence into Artificial
Intelligence (AI)
• Dr John McCarthy (1956): Dartmouth Summer Project on AI
• Invited 11 renowned mathematicians, professors and economists
Dr Marvin Minsky
Dr Julian Bigelow
Dr D.M. Mackay
Dr Ray Solomonoff
Dr John Holland
Dr John McCarthy
Dr Claude Shannon
Dr Nathanial Rochester
Dr Oliver Selfridge
Dr Allen Newell
Dr Herbert Simon
Dr Anand J Kulkarni 20

21. Fifth Generation Project
Dr Anand J Kulkarni 21

22. Intelligence Everywhere
Dr Anand J Kulkarni 22

23. Anand J Kulkarni, PhD 23
1952 1957 1962 1967 1971
1977 1980 1984 1989 1991
1996 1998 1999 2004 2009
2014 2019
Ideology Algorithm
21-12-2023

24. Artificial Intelligence Everywhere
Dr Anand J Kulkarni 24

25. • Machine Learning for recognition
- analyzing animal photos, behavior video, pugmarks
- Cogito Tech Drones
• Marc Travers (Avian Conservation) using Conservation Metrics
• LoRaWAN
Dr Anand J Kulkarni 25
Rainforest Connection, Sensing Clues, Open Ears, Audio Moth
for animal, bullet firing acoustics classification
Uof Southern California for Wild Life Conservation in Cambodia

26. Dr Anand J Kulkarni 26
Durham police and Judiciary
System for Parole decision

27. Dr Anand J Kulkarni 27
2017 – Turnover on Smart Homes: INR 150 Billion
2024 – Expected > INR 1000 Billion

28. Dr Anand J Kulkarni 28
Everyday waste > 200 billion tons
Zen Robotics, Finland
Average human: 30-40 objects
Robots: 150-160
AMP Robotics, USA
classifying certain
brand packings
Bin-E, Poland
Wastebox, Austria

29. Dr Anand J Kulkarni 29
Population today: 7 Billion
2050: 9 Billion
Mordor Intelligence, UK:
AI Investment in Food Business
2020: INR 20 Billion
2026: INR 2100 Billion
TOMRA, Norway
Winnow Solutions, UK
Kitchen Cameras, Shanghai
Emaar Hospitality, Dubai
Wastage reduced by 70%

30. Dr Anand J Kulkarni 30
WARDSAUTO Newsletter
Today: 1.2 Billion Vehicles
2035: 2 Billion Vehicles
Potential Gridlock

31. Dr Anand J Kulkarni 31
Artificial Intelligence Everywhere

32. AI in/for Education: Connecting the Dots
Dr Anand J Kulkarni 32

33. AI Driven Cafeteria Approach
• Student’s Choice/Interest at the centre
Dr Anand J Kulkarni 33
Local & Global
Universities &
Institutes
Industries
University/Institute Courses
Dynamics and vibration
Engineering design
Heat and mass transfer
Fluid mechanics
Systems, measurements, and controls
Solid mechanics
…
Thermodynamics
Industry Courses
Power Train maintenance
Switch Gear Design
…
Automobile Engineering

34. Dr Anand J Kulkarni 34

35. Why AI?
Dr Anand J Kulkarni 35
• Healthcare
• The problem arises in minimizing congestion in the recovery unit
• Planning horizon of n days with cyclic scheduling
• Keep the maximum number of patients as low as possible to
reduce the requirement of beds, nurses and other variable costs
40 30 22 13
36 29 23 7
32 30 22 7
36 32 23 17
𝑴 𝑻 𝑾 𝑹
𝑫𝟏
𝑫𝟐
𝑫𝟑
𝑫𝟒
𝐼𝑘 = 144 121 90 44
𝑍 𝐶∗𝜋1
= 105
22 13 40 30
29 23 7 36
7 32 30 22
36 32 23 17
𝑴 𝑻 𝑾 𝑹
𝑫𝟏
𝑫𝟐
𝑫𝟑
𝑫𝟒
𝐼𝑘 = 94 100 100 105
𝜋∗1 = 3, 4, 2, 1
Beds, Nurses, Variable
Costs, etc.

36. Dr Anand J Kulkarni 36
Problem Size Average
% gap
LP vs CPLEX
CPLEX:
Average CPU
Time
(Sec)
CI Method
Average
% gap
LP vs CI
Average
% gap
CPLEX vs CI
Standard
Deviation
(SD)
CPU Time
(Sec)
Function
Evaluations
(FE)
5 x 5 4.3165 0.27 4.5496 0 0 0.038084 1620
6 x 6 3.3520 0.29 3.4857 0 0 0.082466 3169
7 x 7 2.4426 0.41 2.5067 0 0 0.141646 5070
8 x 8 1.7478 0.73 1.7815 0 0.659745 0.206037 6988
9 x 9 1.3584 4.10 1.3778 0 1.920292 0.301112 9136
10 x 10 0.9259 13.80 0.9473 0.0120 3.265155 0.359756 10750
11 x 11 0.8597 98.31 0.9739 0.1057 2.961605 0.443356 12190
12 x 12 0.7083 260.59 0.9296 0.2146 3.045473 0.537049 13601
13 x 13 0.5632 1072.93 0.8675 0.2993 2.695255 0.663312 15477
15 x 15 -- -- 0.8040 -- 2.832436 0.831481 17827
20 x 20 -- -- 0.6492 -- 2.727449 1.659961 26085
25 x 25 -- -- 0.5431 -- 2.636055 3.138706 35435
30 x 30 -- -- 0.4502 -- 2.668305 5.005378 43715
35 x 35 -- -- 0.4031 -- 2.586950 7.842689 51145
40 x 40 -- -- 0.3417 -- 2.696257 11.972532 61610
45 x 45 -- -- 0.3130 -- 2.468574 17.019936 71835
50 x 50 -- -- 0.2784 -- 2.545943 23.980858 81207

37. Generative AI
• Self Supervised learning
• LLM: Trained on Heavy data (Google, Meta, etc.)
• Image Based
• New Content Generation
• Images
• Audio/Music
• Video
• Text
• Speeches
Dr Anand J Kulkarni 37

38. Dr Anand J Kulkarni 38
https://www.nytimes.com/2022/09/02/technology/ai-artificial-intelligence-artists.html
Jason M. Allen via Midjourney
Jason Allen’s A.I.-generated work, “Théâtre D’opéra Spatial,” took first place in the digital
category at the Colorado State Fair 2022

39. OpenAI: DALL E
• Creates original, realistic images and art from a text description by
combining concepts, attributes and styles.
• DALLE E employs Two Techniques
• CLIP: Learns relationship between images and associated text and makes the
computer learn the concepts in images and hence can generate new images
of the same concepts
Dr Anand J Kulkarni 39

40. • CLIP
• Diffusion
An Astronaut
Riding a Horse
Added Gaussian
Noise
Noise
Cleared
Dr Anand J Kulkarni 40

41. Dr Anand J Kulkarni 41

42. Dr Anand J Kulkarni 42

43. The transformation is determined by the captions “a Victorian
House”, which describes the architecture of the house, and “a
Modern House”, which describes how the architecture of the
house should be changed.
Dr Anand J Kulkarni 43

44. Dr Anand J Kulkarni 44

45. GAN
Dr Anand J Kulkarni 45

46. chAIr
Dr Anand J Kulkarni 46
https://steffen-weiss.design/the-chair-project-generating-a-classic
GAN was used with a dataset of iconic 20th-
century chairs with the goal to `generate a classic`.
The resulting model was used to generate new
chairs as visual prompts for a human designer
who turned these images into feasible chairs
through manual sketching and rendering.
chAIr project explores co-creativity between
humans (Philipp Schmitt, Steffen Weiss) and AI
(ANN), taking the chair — the
archetype/illustrative model of a designed
object — as an example.

47. Fights Counterfeiting
Dr Anand J Kulkarni 47

48. Competency
Dr Anand J Kulkarni 48
https://openai.com/research/gpt-4

49. Competency
Dr Anand J Kulkarni 49
https://openai.com/research/gpt-4

50. Training & Assistance
• Q&A by the E-Trainer or Expert Trainer
Dr Anand J Kulkarni 50

51. Training & Assistance
Dr Anand J Kulkarni 51
𝑚𝑎𝑥𝑖𝑚𝑖𝑧𝑒 𝑓 𝑥 = 𝑥2
, −2 ≤ 𝑥 ≤ 2

52. Generating Reports/Manuscripts
• Trained on the data till 2021 (*)
• Cannot pull the references (*)
• Could be erroneous (*)
• Generate Literature Review
• (Text length improved from 3000 to 25000 words)
• Over dependency on AI generating Emails, Reports, Manuscripts, etc.
Dr Anand J Kulkarni 52

53. Generating Class Work
• Generate assignments
• Generate assignment Solutions
• Generate computer codes
• Must stay at least few steps ahead in understanding and acquiring
the knowledge.
Dr Anand J Kulkarni 53

54. Challenges
• Deep Fake
• Polarization of the communities
54
21-12-2023 Anand J Kulkarni, PhD

55. Challenges and Solutions
• Explainable AI
• Ethical AI
• Teaching & Leaning
Dr Anand J Kulkarni 55

56. AI Contributing: Go Green and Sustainability
• According to UN Alliance for Sustainable Fashion Apparel manufacturing accounts for
• 8% of the world’s greenhouse gas emissions
• 9% of annual microplastics in the oceans
• Restocking returns is often not financially feasible and most end up in a landfill
• Traditional approach: Deciding what customers want, then mass producing that item, is a game of
chance: Bet wrong, and a manufacturer generally ends up with a lot of unsold clothing.
• AI can help on several fronts
• Starting with trends forecasting
• Several firms use AI and ML to analyze images on social media, taking note of prints, shapes and color to help
their manufacturer clients figure out what’s going to sail and what’s going to sink
• AI helps the brands figure out pricing strategies and steer clear of trends that are on their way out
• Fewer returns
• Reduced waste
• Increasing industry’s sustainability
Dr Anand J Kulkarni 56

57. Thank you
Dr Anand J Kulkarni 57
Anand J Kulkarni PhD, MS, BEng, DME
Research Professor & Associate Director
Institute of Artificial Intelligence
AI 502 Avanti, MIT World Peace University
124 Paud Road, Kothrud, Pune 411038, MH, Bharat (India)
Email(s):
anandmasc@gmail.com; anand.j.kulkarni@mitwpu.edu.in; kulk0003@ntu.edu.sg
URL: sites.google.com/site/oatresearch/anand-jayant-kulkarni