AI Tools In social science and management research

AI tools in research

1. Application of Digital Tools and Techniques for Interdisciplinary Research
in Humanities and Social Sciences
DR. JONTIDEURI
A S S I S T A N T P R O F E S S O R
F A C U L T Y O F E N G I N E E R I N G A N D T E C H N O L O G Y
S H A R D A U N I V E R S I T Y , U Z B E K I S T A N
Online International FDP
on
AI Tools in Humanities and Social Sciences Research
Date: 12/13/2024

2. Origin of AI
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John McCarthy
➢In 1956 John McCarthy, Marvin
Minsky, and Alan Newell,
organized a workshop at
Dartmouth College in the USA

3. Learning is ..
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➢.. a process of acquiring new knowledge, skills, and behaviors
➢.. can happen through formal education, informal learning, or through
experience
➢.. a lifelong process
➢Active, Social and Constantly Changing
➢Can be Intentional or Un-intentional
➢Easy or Difficult
➢Permanent or Temporary
➢Rewarding or Frustrating

4. Everyone is Learning!
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•Humans are learning ..
•Learnt hunting for food
•Learnt building for shelter
•Learnt sewing for clothes
•Animals are learning
•Dogs in police investigations
•Plants are learning too..
•Detect and respond to various stimuli in
the environment, such as light, gravity,
temperature, chemical signals etc.
•Remembers associations between stimuli and
outcomes
•Responses can be influenced by past experiences.

5. Now, Machines are Learning!
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6. Machines are Teaching Machines! And will be
Working along with us!
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7. Evolution of Technologies
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Ability of a machine
to imitate intelligent
human behavior
Application of AI that
allows a system to
automatically learn and
improve from experience
Application of ML
that uses complex
algorithms and deep
neural networks to
train a model

8. 01
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9. 01
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10. 01
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11. What is Artificial Intelligence?
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❖Development of computer systems that can:
➢Learn and Think
➢Mimic human intelligence processes, such as
■Learning
■Reasoning
■Problem-solving
■Perception
■Language understanding
❖Make Machines Think and become Smarter (Playing Chess)
❖Learn and Adopt (Driving a car)
❖Help People (Voice Assistants)

12. What is AI ?....
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•Artificial intelligence (AI) is a wide-ranging branch of computer science in which smart /
intelligent machines are built to perform tasks that typically require human intelligence. In
simpler terms, it is making computers think like humans.

13. Machine learning
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14. What is Machine Learning?....
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●Enables systems to learn from data and
make predictions or decisions without
being explicitly programmed to perform
a task.

15. Machine Learning….
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•What is Machine Learning?
•A subfield of Artificial Intelligence (AI) that gives
computers the ability to learn without being
explicitly programmed .
•Machine Learning allows the systems to make
decisions autonomously without any external support.
•These decisions are made when the machine is able to
learn from the data and understand the underlying
patterns that are contained within it.

16. Machine learning
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Machine learning: Example

18. Machine learning: Example
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19. Machine learning: Example
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20. What is Deep learning?
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•Deep learning is a subset of ML that use Artificial Neural Networks to learn from data.

21. Machine learning Life cycle
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22. Machine learning Life cycle
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1. Gathering Data:
This step includes the below tasks:
•Identify various data sources
•Collect data
•Integrate the data obtained from different
sources
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23. Machine learning Life cycle
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2. Data preparation
This step includes the below tasks:
•Data exploration
•Data pre-processing:
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24. Machine learning Life cycle
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3. Data Wrangling
•Cleaning and converting raw data into a useable format
•In real-world applications, collected data may have
various issues, including:
✔Missing Values
✔Duplicate data
✔Invalid data
✔Noise
•So, we use various filtering techniques to clean the data.
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25. Machine learning Life cycle
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4. Data Analysis
This step involves:
•Selection of analytical techniques
•Building models
•Review the result
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26. Machine learning Life cycle
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5. Train Model
•train the model to improve its performance for better outcome of
the problem.
•We use datasets to train the model using various machine learning
algorithms.
•Training a model is required so that it can understand the various
patterns, rules, and, features.
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27. Machine learning Life cycle
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6. Test Model
•Once our machine learning model has been trained on a
given dataset, then we test the model.
•In this step, we check for the accuracy of our model by
providing a test dataset to it.
•Testing the model determines the percentage accuracy of
the model as per the requirement of project or problem.
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28. Machine learning Life cycle
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7. Deployment
•deploy the model in the real-world system.
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29. Types of ML
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30. Types of ML
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•Linear Regression,
•Support Vector
•Machines,
•K-Nearest Neighbors
•K-means Clustering,
•Hierarchical Clustering,
•PCA (Principal Component
Analysis).
•Q-learning,
•Deep Q Networks (DQN),
•Policy Gradient Methods
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31. Types of ML
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32. ML vs. Deep learning?
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33. Applications of Machine Learning
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34. Applications of Machine Learning
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1. Image Recognition:
⮚It is used to identify objects, persons, places, digital images, etc.
⮚The popular use case of image recognition and face detection is Automatic friend tagging suggestion.

35. Applications of Machine Learning
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3. Traffic prediction:
•It predicts the traffic conditions such as whether traffic is cleared, slow-moving, or heavily congested with the
help of two ways:
•Real Time location of the vehicle form Google Map app and sensors
•Average time has taken on past days at the same time.

36. Applications of Machine Learning
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4. Product recommendations:
•Google understands the user interest using various machine learning algorithms and
suggests the product as per customer interest.
•Machine learning is widely used by various e-commerce and entertainment
companies such as Amazon, Netflix, etc., for product recommendation to the user.
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37. Applications of Machine Learning
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5. Email Spam and Malware Filtering:
•Whenever we receive a new email, it is filtered automatically as important, normal,
and spam.
•We always receive an important mail in our inbox with the important symbol and
spam emails in our spam box, and the technology behind this is Machine learning.
•Below are some spam filters used by Gmail:
•Content Filter
•Header filter
•General blacklists filter
•Rules-based filters
•Permission filters
•Some machine learning algorithms such as Multi-Layer Perceptron, Decision tree,
and Naïve Bayes classifier are used for email spam filtering and malware detection.
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38. Applications of Machine Learning
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6. Online Fraud Detection:
•Machine learning is making our online transaction safe and secure by detecting fraud
transaction.
•So to detect this, Feed Forward Neural network helps us by checking whether it is a
genuine transaction or a fraud transaction.
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39. Applications of Machine Learning
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7. Medical Diagnosis:
•In medical science, machine learning is used for diseases diagnoses.
•With this, medical technology is growing very fast and able to build 3D models that
can predict the exact position of lesions in the brain.
•It helps in finding brain tumors and other brain-related diseases easily.
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40. Applications of Machine Learning
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8. Automatic Language Translation:
•Google's GNMT (Google Neural Machine Translation) provide this feature, which is
a Neural Machine Learning that translates the text into our familiar language, and it
called as automatic translation.
•The technology behind the automatic translation is a sequence to sequence learning
algorithm, which is used with image recognition and translates the text from one
language to another language.
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41. Applications of Machine Learning
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9. Self-driving cars:
•Machine learning plays a significant role in self-driving cars.
•Tesla, the most popular car manufacturing company is working on self-driving car.
•It is using unsupervised learning method to train the car models to detect people and
objects while driving.
10. Stock Market trading:
•Machine learning is widely used in stock market trading.
•In the stock market, there is always a risk of up and downs in shares, so for this
machine learning's long short term memory neural network is used for the
prediction of stock market trends.
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42. Top 10 uses of AI
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1.Virtual Assistants
2.Machine translation
3.Recommendation systems
4.Autonomous vehicles
5.Image recognition
6.Healthcare diagnostics
7.Chatbots and Customer
Support
8.Fraud detection
9.Robotics
10.Video games

43. Virtual Assistants
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•AI-powered virtual assistants like
Siri, Google Assistant, and
Amazon Alexa use machine
learning to understand user
requests and provide relevant
information or perform tasks

44. Machine translation
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AI-based translation services
such as Google Translate
use deep learning
•algorithms to translate text
or speech between
languages with impressive
accuracy.

45. Recommendation
Systems
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AI algorithms are used by
platforms like Netflix,
Amazon, and Spotify to analyze user preferences
and behavior, providing personalized
recommendations for movies, products, or music.

46. Autonomous Vehicles
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●Companies like Tesla and
others are developing self-
driving cars that use AI
algorithms to navigate, avoid
obstacles, and make driving
decisions.

47. Image Recognition
●AI-powered image recognition systems, such as
those used by Google Photos or Facebook, can
automatically identify objects, people, and
scenes in images, enabling features like facial
recognition and content-based searching.
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48. Healthcare Diagnostics
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●AI-based systems like IBM Watson and
Google's DeepMind are used to
analyze medical images, identify
patterns, and assist in diagnosing
diseases such as cancer or diabetic
retinopathy.

49. Chatbots and Customer Support
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●AI-powered chatbots are used by companies to
handle customer inquiries, provide support, and
even conduct sales, often with natural language
processing capabilities that enable them to
understand and respond to human language.

50. Fraud Detection
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●AI algorithms are employed
by financial institutions to analyze large volumes
of transaction data, identify patterns, and detect
fraudulent activities.

51. Robotics
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●AI is used in the development of
advanced robots that can perform complex tasks,
such as Boston Dynamics' robots, which can navigate
uneven terrain, carry heavy loads, and even perform
acrobatics.

52. Gaming
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●AI is used to create more immersive and
realistic gaming experiences, with non-player
characters (NPCs) exhibiting human-like
behavior and decision-making abilities.

53. Branches of AI
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55. Prompts in AI
1.Prompt is a set of instructions or guidelines that are given to an AI
model to guide its output.
2.Prompts can be used to control the style, format, and content of the
output, and they can be as simple as a single word or as complex as a
paragraph.
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56. Examples of Prompts
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1.Text prompts: These prompts are used to generate text,
such as poems,code, scripts, musical pieces, emails, letters,
etc.
2.Code prompts:These prompts are used to generate
code,such as Python scripts, Java applications, or SQL queries.

57. Prompts in ChatGPT
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58. Generative AI Tools
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59. Academic Writing with AI Tools
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61. AI Tools for Research
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➔Literature Review (LR)
➔Questionnaire Development
➔Organizing/Constructing
➔Writing Assistance
➔Paraphrasing
➔Data Analysis
➔Plagiarism Checker
➔Citation Generator/Referencing
➔Summarizing
➔Checking Manuscript

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AI Tools - For LR
●Typeset.io(scispace - formally typeset)
●Discovery.researcher.life
●Openreadacademy.com
●Litmaps
●Connected Papers
●ScinapseelicitParis Review(Literature)
●Electric Literature
●Granta
●TLS & Research Buddy

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AI Tools - For LR
●Scispace (Typeset.io) https://typeset.io/
●Discovery.researcher.life
●Openreadacademy.com
●Litmaps
●Connected Papers
●Scinapse: https://www.scinapse.io/
●elicitParis Review(Literature)
●Electric Literature
●Granta
●TLS & Research Buddy

65. AI Tools for Research
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➔Literature Review (LR)
➔Questionnaire Development
➔Organizing/Constructing
➔Writing Assistance
➔Paraphrasing
➔Data Analysis
➔Plagiarism Checker
➔Citation Generator/Referencing
➔Summarizing
➔Checking Manuscript

66. AI Tools for Questionnaire Development
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❖Data Collection and Management
➢SurveyMonkey : https://www.surveymonkey.com/
➢Qualtrics
➢WebHarvy
➢Octoparse
➢NVivo:

67. AI Tools for Research
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➔Literature Review (LR)
➔Questionnaire Development
➔Organizing/Constructing
➔Writing Assistance
➔Paraphrasing
➔Data Analysis
➔Plagiarism Checker
➔Citation Generator/Referencing
➔Summarizing
➔Checking Manuscript

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AI Tools -For Structuring/Organizing/Constructing
●app.jenni.ai: https://jenni.ai/
●researcher app
●Project.wisio.app
●Tome. app

69. AI Tools - For writing assistance / essay writing
●Chat GPT: https://chatgpt.com/
●Poe.com
●Sdk.vercel.ai
●monica.ai
●PopAi
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AI Tools - For paraphrasing
●Quillbot: https://quillbot.com/
●WriteHuman: https://writehuman.ai/
●Rytr.me
●Prowritingaid
●GPT minus 1

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AI Tools - Data Analysis & visualisation
•Napkin.ai: https://www.napkin.ai/
•Polymer
•Julius.ai
•Monkey learn
•QD- chat.openai.com

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AI Tools - For Image Generation
●Bing: https://www.bing.com/images/create?form=FLPGEN
●Haiper AI : https://haiper.ai/

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AI Tools - plagiarism
●originality.ai (AI Content Detector)
●Copyleaks (Plagiarism): https://copyleaks.com/
●Undetectable.ai
●Scibber.com
●Trinka
●Sapling ai
●Quillbot

75. AI Tools - Citation Generator / Referencing
●MyBib: https://www.mybib.com/
●Zbib.org
●Citation machine.net
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76. AI Tools - For Summarizing the paper
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AI Tools - Checking Manuscript
●Penelope.ai
●reviewer.heyscience.ai
•Paper Digest : https://www.paperdigest.org/
•Genei

77. For Conversion
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78. Any Query???
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79. Thank you
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80. What is Referencing?
●Standardize way of acknowledging the sources of
Information and ideas you have used in your
manuscripts.
Why
●We need to specify which ideas are not yourn own
●Giving credit to the original source
●Citations gives direction how to find sources
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