AI tools in Scholarly Research and Publishing

AI tools in Scholarly
Research and Publishing
Brian Pichman

Agenda
• The evolution of AI
• AI in research methodologies
• AI in academic publishing
• Exploring AI tools and technologies
• Building an AI environment
• Navigating challenges and ethical
considerations
• Future trends and opportunities.

A Few Disclaimers
Before we get started

Evolve Project | Brian Pichman
4
The Generic Disclaimers about AI:
AI is…
Challenging Credibility
Remember when ..
• Wikipedia outlawed
• Can’t use internet as a
source
• Online Education isn’t real
Inherently Neutral
Technology can be used for
good things, or it can be used
for bad things
Puzzling Opportunities
AI can create jobs – invention
of cars didn’t eliminate jobs
Constantly Changing
New rules, new techniques,
and constantly impacting

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Examples

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“AI Winters”
https://www.historyofdatascience.com/ai-winter-the-highs-and-lows-of-artificial-intelligence/

Many Flavors Of AI
Its important to explore how we can use AI across various types

8
The Many Uses of AI
AI Can Be Used Almost
Everywhere
There are a variety of tools that
leverage AI that you can use today.
The first part of our day will talk
about the mechanics of these AI
uses and the second part of the day
will be exploring some of our
favorite tools.

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Generative AI
Generative AI
Uses trained data to create any sort
of media – text, audio, or visual
content.
The Many Uses of AI

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Exploring AI
How Does Generative AI Work?
• Training Data: Generative AI models are trained on a dataset of desired
content.
• Generator Network: Creates new data from random input.
• Iterative Learning: Continuous model adjustments during training to
improve data generation.
• Sampling: Utilizes the trained model to generate new data samples
(improperly done gives you AI “hallucinations”
• Most AI models that generate context are trained on things found on the
World Wide Web
Training Generative AI

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Books written by an AI
https://link.springer.com/book/10.1007/978-3-030-16800-1

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Generative AI
Lensa App

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Generative AI
Professional Headshots

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Generative AI
What is a Deep Fake?
Using Ai to Manipulate an Image or Video
DeepFakes use Ai to generate or manipulate content
Commonly used to create realistic but fabricated videos
Involves digesting content to learn how to mimic the patterns of
the target
Once trained can edit existing or generate new content
Ethical concerns over usage (misinformation, identity theft,
privacy)
Deepswap.ai & faceswap.dev - great tools for practice

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Generative AI
Can AI Detect AI?
It was easy to tell what was AI generated and
what wasn’t – through word choice or if that
text resembles anything in existence. Early
AI detection methods worked with high
accuracy.
Early 2022
2024
Now, there has been more content ever
created generated by AI … thus when an AI
leverages that created content as part of its
“brain” it won’t know what’s AI generated or
not. Called “Generative Inbreeding”

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Data Perspective in 2019

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Some More Estimates

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Operational AI
Operational AI
Is the use of AI to make day to day
tasks easier – whether that is AI that
joins meetings and takes notes or
acts as a coach. Operational AI
also satisfies departmental needs
ranging from coding to marketing.
The Many Uses of AI

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How is AI being used across organizational units?
Operational AI
Edit images and remove
content a lot quicker.
Finalize videos.
Editing
Programming
Management
Social Media
Security
Get help writing or debugging code.
Leverage AI to impower your day-
to-day operations
Automatically create content, find
related content, or grow your
social reach.
AI can be used to secure or even
breach infrastructures

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Operational AI
Image Editing Made Easy
https://booltool.boolv.tech/

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Conversational AI
Conversational AI
Type of Ai that allows you to communicate
just like speaking to someone
else. ChatGPT is a great example.
Leverages many types components to
function:
o Natural Language Processing (NLP)
o Natural Language Understanding (NLU)
o Natural Language Generation (NLG)
o Speech Recognition
o Dialog Management
This type of Ai is commonly used in
Chatbots, Virtual Assistants, Interactive
Voice Response (IVR) Systems.
The Many Uses of AI

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• Leverages opensource Ai architecture leveraging the GPT framework.
• One of the best implementations of Ai that generates human like responses.
• Complete developed to handle natural language understanding and
generation.
• Requires training on content to be able to generate responses.
• If trained on bad content or no content cannot deliver quality responses.
“Wise” Conversations
Conversational AI
ChatGPT

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There are a lot of service providers that exist where you can upload
your own unstructured data and train it off your knowledge base,
PDFs, PowerPoints whatever it may be. Then you can ask questions
about the data pulled.
AI Can Collect Data
Training AI Off Your Data

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AI Powering Chat Tools

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Operational AI
AI Personalities
You can ”teach” an AI to be kind, be sarcastic, or even
funny.
You can train it on your own data or data that exists
already.
AI Can Take On Many Forms of Personalities

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…or for companionship…

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One Example Tool – Google Dialogflow
Training Models
Teach the AI to respond and train on
responses that you feel aren’t
correct

https://www.madrona.com/foundation-models/

31
Metaverse
Digital Twins
This is the understanding of building
a Virtual (or Augmented) Reality
environment for communication or
trainings.
The Many Uses of AI

32
Imagine a digital twin of a librarian or a tutor that understands the
student’s classes, what they are struggling with, and build a
relationship with this partner to help them through anything they may
be struggling with
Unique Learning Experiences
Metaverse
AI as a Personalized Learning Assistant

33
Metaverse enables immersive virtual reality classrooms, providing a
dynamic and interactive learning environment ->
• Nursing Labs,
• Diversity Awareness Training
• History
Field Trips without the Travel
Metaverse
Virtual Reality Classrooms in the Metaverse

34
Analytical AI
Managing Data Better
Use AI to digest information faster
than a human can with more
accurate calculations and new
observations
The Many Uses of AI

35
And How We Use It
Other Forms of AI
Optical Character Recognition
Think of how a picture of your license plate
allows a machine to translate that to text and
run a query to determine who violated a toll.
Also see this in scanners that can take an
image and convert this to text.
Consider how you can take a photo of another
language and have it translate to yours
Advance User Preferences
This is the concept of an AI providing solutions
based on historic user’s preferences and
comparing it to similar users.
Compare how Amazon or Netflix makes
recommendations based on your purchases or
views – or even how Amazon guesses when
you might run out of a specific product.
Sensory Data Analysis
Your wearables that detect heart rate for
instance can determine without user
intervention if you are working out and even
what kind of work out such as jogging or
bicycling.

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• When editing or using filters in photos (do X to eyes and Y to
ears)
• Identification of license plates from an image in a toll violation
• Facebook’s ability to identify and recommend faces in photos
• iPhone users can have their phone categorize people by facial
patterns – in which you then define their name
• Google’s Image Recognition
Examples
How we see AI In Everyday Life
Image Recognition
Think of how we can use facial imaging
to determine moods

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AI Cognitive Intelligence

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Have AI Powered Insights
Using AI and Data
• Pull in all data sources to find trends and
predictive analytics.
• AI can also identify objects, do language
translations, and discuss what it sees in a
piece of data, document, or image.
Data Is Important

39
Have AI Powered Insights
Using AI and Data
• Microsoft’s built in AI tool is remarkable –
discuss what you are wanting to accomplish
and it can gather the solutions through a
conversation and access to the data in a
spreadsheet.
Office 365 CoPilot

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What’s Next?
Further Exploring
AI has also expanded its abilities to
understand empathy, robotics, and
more. How can we embrace AI and
help others be ready for the future?
The Many Components of AI

Steering The
Ship of AI
• Historical Context
• From basic algorithms to advanced neural networks.
• The rise of big data in research.
• Key Milestones
• The development of machine learning and deep
learning models.
• Breakthroughs in natural language processing (NLP)
and computer vision.
• The Shift to AI-Enhanced Research
• Increased efficiency and accuracy in data analysis.
• The growing importance of interdisciplinary AI
research.

AI In Research
•Transforming Data Collection
•Automated data scraping and aggregation.
•Use of sensors and IoT devices in data
collection.
•Revolutionizing Data Analysis
•Machine learning models for pattern
recognition.
•AI for predictive analytics and modeling.
•Case Studies
•AI in genomic sequencing and analysis.
•AI applications in social science research.

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https://www.genengnews.com/insights/ai-transforms-genomic-data-into-actionable-insights/
https://medicalxpress.com/news/2021-11-ai-genomic-surveillance-combine-health.html
AI in Research

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AI in Social Studies
https://www.nature.com/articles/s41467-019-14108-y

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AI for Social Good … powered by research
https://www.mckinsey.com/featured-insights/artificial-intelligence/applying-artificial-intelligence-for-social-good

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Real Product Examples From Researched Data

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Xander – AR Glasses with Text

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Starkey’s Genesis AI Hearing Aid

AI in Academic Publishing
• Automated Processes
• AI-driven peer review and editorial decision-
making.
• Automation of formatting and style checks.
• Plagiarism Detection
• Advanced AI tools for detecting plagiarism and
ensuring originality **
• Enhancing Accessibility
• AI-powered tools for improving the readability
and accessibility of academic texts.
• Translation services to bridge language barriers
in research dissemination.

https://www.linkedin.com/pulse/letter-schools-regarding-ai-detectors-dan-bowen-tqqgc/

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PublishDrive

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Storipress

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Something for you
Future Directions in AI for Research and
Publishing
Emerging Technologies
•Natural Language Generation (NLG): Tools like GPT-3 are
revolutionizing content creation, enabling the automated
generation of research summaries, reports, and even entire
articles.
•Semantic Analysis and Knowledge Graphs: Enhancing
search capabilities, making it easier to discover and connect
related research across disciplines.
•AI in Peer Review: Automating the initial screening of
manuscripts, checking for plagiarism, and ensuring
submissions adhere to guidelines.
Impact on the Landscape
•Customized Content Delivery: AI will
enable more personalized research feeds,
recommending articles and data sets based
on individual research interests and past
behavior.
•Enhanced Data Analysis: Tools like
TensorFlow and PyTorch will become more
user-friendly, allowing researchers to perform
complex data analyses without deep
programming knowledge.
•Accelerated Discovery: AI-driven
hypothesis generation will speed up the
research process, identifying potential areas
of study by analyzing vast datasets.

56
Example:

Skill Development
for AI in Academia
• Data Literacy: Understanding data collection, management, and analysis
fundamentals.
• Machine Learning Basics: Familiarity with concepts like supervised and
unsupervised learning, neural networks, and model evaluation.
• Programming Languages: Proficiency in Python or R, given their
prominence in AI research and data science.
• DataCamp: Provides interactive Python and R courses tailored to data
science and AI.

Funding and
Grants for AI in
Academia
Opportunities:
• Government Grants: Agencies like the National Science
Foundation (NSF) and the European Research Council (ERC)
offer grants for AI research projects.
• Private Foundations: Organizations such as the Allen Institute
for AI and the Sloan Foundation provide funding for projects
at the intersection of AI and research.
• Corporate Sponsorship: Tech companies like Google, IBM, and
Microsoft offer grants and resources for AI research and
development.
Implementing AI Solutions:
• Infrastructure Development: Funding can support the
acquisition of necessary computing resources, like GPUs or
cloud computing services.
• Training and Workshops: Grants can be allocated for staff and
student training in AI technologies and data science skills.
• Research Projects: Specific AI projects, such as developing AI
tools for data analysis or enhancing library search systems,
can be funded to improve research and publishing processes.

Tools and
Resources:
• TensorFlow & PyTorch: For machine learning and deep learning
projects.
• Jupyter Notebooks: For sharing live code, equations,
visualizations, and narrative text.
• Hugging Face's Transformers: For state-of-the-art natural
language processing.
• SciKit-Learn: For data mining and data analysis.
• AllenAI's Semantic Scholar: For AI-powered research tool for
literature review.
• Google Scholar & Microsoft Academic: For comprehensive
literature search.
• Zotero & Mendeley: For reference management and sharing
research.

Applying AI
Building Your Own Environments

66
Conversational AI
Conversational AI
What are some tools to build your
own Conversational AI?
You can leverage OpenAI API
The Many Uses of AI

67
Training Data

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Trained Data with Personalities
1.Temperature:
1.This controls the randomness of the model's responses.
2.Lower temperature (e.g., 0.0) means more predictable and
conservative responses.
3.Higher temperature (e.g., 1.0) makes responses more creative and less
predictable.
2.Frequency Penalty:
1.This reduces the model's likelihood of repeating the same line of
thought or content.
2.A higher frequency penalty discourages repetition.
3.A value of 0 means no penalty for repetition.
3.Presence Penalty:
1.This affects the likelihood of introducing new concepts or topics in the
response.
2.A higher presence penalty encourages the model to talk about new
things.
3.A value of 0 means no emphasis on new content.
Providing Context: You can include specific instructions or context in your
prompt. For example, "Act like a friendly and knowledgeable assistant" sets
a tone and character for the model's responses.

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https://www.perplexity.ai/

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Afforai

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Google Colab

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AWS SageMaker

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Data Scrubbing

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Cryfe

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Interviewer AI

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Metaverse
Digital Twins
This is the understanding of building
a Virtual (or Augmented) Reality
environment for communication or
trainings.
Real World Uses

77
Ovation VR
Learn Public Speaking
Ovation was developed to help you
build your skills and confidence by
providing real-time training tools and
feedback as you speak to a realistic,
simulated audience.

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EqualReality

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…how about assistive technology?
https://support.apple.com/en-us/104993
https://dubverse.ai/

Challenges and
Ethical
Considerations
•Data Privacy
•Ensuring the confidentiality and
integrity of research data.
•Bias in AI
•Identifying and mitigating bias in AI
models and datasets.
•Ethical Research Practices
•Responsible use of AI in research.
•Ethical guidelines for AI research.

Copyright Concerns
• Copyright and AI in Research
•Understanding copyright laws in the context of AI-generated content.
•Copyright challenges with using AI to analyze or generate text and
images.
• balancing innovation with respect for original works
• Mitigating Copyright Risks
•Best practices for using copyrighted materials in AI research
• Asking for permission
•Tools and approaches for copyright compliance and fair use.
• AI and Intellectual Property
•Navigating the complexities of IP rights for AI-generated outputs.
•Recent cases and precedents in AI and copyright law.

Legal Highlights
• AI-Generated Art and Copyright: A U.S. court ruled that AI-generated art cannot
receive copyrights under U.S. law. The court's decision emphasized that only works
with human authors are eligible for copyright protection. This ruling was a significant
development, affirming the Copyright Office's rejection of an application filed on
behalf of an AI system called DABUS. The office and the court have maintained that
human authorship is a fundamental requirement for copyright eligibility
• AI-Assisted Works: Despite the ruling that purely AI-generated works cannot be
copyrighted, the U.S. Copyright Office has clarified that AI-assisted works may still
qualify for copyright protection, provided there is significant human creativity
involved. This means that while the AI itself cannot be considered the author,
creative modifications or arrangements of AI-generated content by humans can be
copyrighted. This distinction underlines the importance of human involvement and
ultimate creative control in the creation process.
• EU's Approach to AI and Copyright: The European Union is also navigating the
complexities of AI and copyright law. An early agreement within the EU proposes
that companies using generative AI tools, such as ChatGPT, must disclose any
copyrighted material used to develop their systems. This is part of the EU's broader
initiative to regulate AI technologies, ensuring transparency and accountability,
especially concerning high-risk AI tools. The EU's proposed rules aim to balance the
need for innovation with the protection of citizens' rights and copyright laws

Protecting Data
•Libraries may not be directly
involved in the collection of
research data, but they play a
crucial role in the stewardship and
dissemination of research and
should advocate for and support
ethical practices in data
management

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Data Privacy and Bias
•Protecting research data is like safeguarding a
treasure. We use advanced methods like
encryption to keep it secure, just like a chest with a
strong lock.
•We're also careful about how we share and use
this data. We follow strict rules (like GDPR and
HIPAA) to make sure we're handling it responsibly,
so the information doesn't end up in the wrong
hands.
•To prevent AI from being biased, we diversify the
data it learns from—much like how a well-rounded
diet is better for health
•Regularly checking and fine-tuning our AI models
(like proofreading a critical essay) helps us correct
any biases and ensures the AI's decisions are fair
and reliable.

Data Acquisition
and Integrity
• Sourcing Data Ethically
• Guidelines for ethically sourcing and
using data in AI models.
• Considerations for public vs. private
data usage.
• Data Integrity and Quality
• Ensuring the accuracy and reliability of
data sets.
• Techniques to cleanse and validate
data before AI processing.

AI Hallucinations
and Misinformation
• Understanding AI Hallucinations
• What are AI hallucinations, and why do they occur?
• Examples of hallucinations in AI models and their impact on research
integrity.
• Combating AI-Generated Misinformation
• Strategies to detect and mitigate the spread of AI-generated
misinformation.
• Implementing checks and balances in AI systems to prevent
misinformation.

88
Researching, Talking, Adopting, and Learning
What's Next?
Ai can enhance many of our day to day task to make them:
• Update course guidelines and policies to recognize Ai and if its
approved or not.
• When Ai is approved – highlight what tools are approved
• Research tools that will enhance the Ai experience
• Should we use as an academic aid or productivity assistant?
• Be "ok" with change
• Understand not all Ai is created equal
• Ai changes fast – What works today may not work tomorrow.
Thought Provoking Ideas to guide better understanding

89
The Challenges
Ai can enhance many of our day to day task to make
them:
• Is using Ai bad or just bad right now?
• Is Ai just the next generation of using the Internet as a
source?
• It's been around in various iterations for years, why all
the noise right now?
• What data source is the Ai using?
• Security of Data – Who owns it?
• Ai Generative Inbreeding
• Ai standards (Responsible Ai / Ethics Ai)
• Note: Ai is as good as the data it's provided.
Ai will make you rethink

90
Suggested Standards for AI Data Use
1.Proactive Assessment of Data Protections: Researchers should
assess the type of protections needed for sharing scientific data from
participants, including considering restricted access when necessary, even
if data are technically de-identified.
2.Robust Consent Processes: It's essential to develop consent processes
that provide clarity about the future sharing and use of scientific data,
including any limitations on future use and general data management
practices.
3.Limitations to Data Sharing: There may be valid reasons to limit data
sharing under certain policies, especially if sharing could compromise
participant privacy or safety, or if limitations are stated in informed consent
documents.
4.Review of Conditions for Data Sharing: Institutions should review and
communicate appropriate limitations on the future use of data and consider
potential risks when sharing data with repositories or other users.
5.Privacy Considerations for All Scientific Data: Data used in research
deserves privacy considerations, regardless of whether it comes from non-
research settings with different privacy standards.
6.Commitment to Responsible Data Sharing: The research community
should remain vigilant against potential misuse of data and enforce terms of
data use agreements.

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While Researching
•Minimize Data: Collect no more personal data than
necessary for conducting research.
•Pseudonymize and Anonymize Data: Convert data to
anonymized or pseudonymized form as soon as possible,
and securely delete fully identifiable data.
•Secure Data Storage: Keep personal data secure and
in compliance with data protection law.
•Retention and Destruction of Data: Retain data
subject to periodic review and securely destroy it when no
longer needed.

92
Consent Forms?
•De-identification of Data: Where consent for
identifiable data sharing isn't given, data should be
shared in a de-identified format.
•Use of Data Sharing and Use Agreements: Utilize
agreements to establish clear conditions for data sharing,
particularly for controlled-access data.
•Understanding Legal Protections Against Misuse:
Familiarize with and communicate legal protections, such
as Certificates of Confidentiality, which prohibit disclosure
of protected information for non-research purposes.
Remember, if you collected survey data that is fed into an
AI, what does that do?
How should consent look like for data?

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Feel free to reach out!
Questions / Contacts
o Brian Pichman
o Bpichman@evolveproject.org
https://www.linkedin.com/in/bpichman/

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