Discover the transformative role of Artificial Intelligence in shaping content creation and social media engagement within library environments. This presentation explores how AI-driven tools are revolutionizing the way libraries share information, curate content, and connect with their communities. Explore practical applications of AI in generating personalized content, automating social media interactions, and enhancing user engagement, all while maintaining the unique character of library services.
Key Points:
Tailoring library content through AI: Customized reading recommendations, curated lists, and more.
Amplifying outreach with AI-powered social media strategies, boosting community interaction.
Ethical considerations in AI-generated content for libraries: Striking the right balance.
Real-world examples of successful AI-driven library campaigns that foster engagement.
Collaborative possibilities: How libraries can work with AI to enhance user experiences.
Future prospects: Navigating the evolving landscape of AI and its integration in library services.
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AI
Discover the transformative role of Artificial Intelligence in shaping content creation and
social media engagement within library environments. This presentation explores how AI-
driven tools are revolutionizing the way libraries share information, curate content, and
connect with their communities. Explore practical applications of AI in generating
personalized content, automating social media interactions, and enhancing user engagement,
all while maintaining the unique character of library services.
Key Points:
• Tailoring library content through AI: Customized reading recommendations, curated lists,
and more.
• Amplifying outreach with AI-powered social media strategies, boosting community
interaction.
• Ethical considerations in AI-generated content for libraries: Striking the right balance.
• Real-world examples of successful AI-driven library campaigns that foster engagement.
• Collaborative possibilities: How libraries can work with AI to enhance user experiences.
• Future prospects: Navigating the evolving landscape of AI and its integration in library
services.
Today we are exploring…
Welcome
3. What is Artificial Intelligence
the theory and development of computer systems able to perform tasks
that normally require human intelligence, such as visual perception,
speech recognition, decision-making, and translation between languages.
4. What is Machine Learning
Machine Learning is a subset of Artificial Intelligence that uses algorithms
and statistical models to allow a computer system to make decisions
around a specific task without explicit instructions; relying on patterns and
inference
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Source:
https://meaningfulimpacthub.com/
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When talking about AI…
People will have one of two
reactions:
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What makes up an intelligent system?
AI Components
Logic and Rules Based
Computer makes decisions
based on a decision tree,
logic rules, or a predefined
process with a calculated
result.
Pattern Based
(Machine Learning)
Computer learns overtime
by using data and algorithms
to detect patterns.
Deep Learning
Deep Learning is a subset of
Machine Learning that
enables the computer to
make decisions on its own.
Neural Networks
A neural network allows an
AI to make its own
conclusions, where a simple
pattern-only based AI must
rely solely on data. A neural
network allows deep
learning to function.
8. Pattern Based Intelligence -> currently exists with self driving cars, language translations,
movie recommendations etc.
Strong Artificial Intelligence -> (doesn’t yet exist)
• computers think at a level that meets or passes people (abstract thinking)
Artificial Intelligence Exists
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Flash Light Examples
Understanding AI
If an ML algorithm makes an
inaccurate prediction, then the
engineer needs to correct. In DL,
the algorithms can determine on
their own if a prediction is accurate
or not.
Deep Learning
Allow machines to make to
their own accurate decisions
without intervention from
engineer
Neural Networks
If detects {dark} turn on {light}
Logic Rules
it’s performing a function with the
data given and gets progressively
better at that function
Machine Learning
Eventually, the system can
turn on the light with other
queues such as “I can’t see”
DL “Code”
Flashlight will turn on
automatically as it learns other
words for “dark” picking up on
phrases that contains the word
ML “Code”:
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create an algorithm that is able to teach itself without any external help
Pattern Recognition
Deep Learning
Uses more complicated mathematical models to define
pictures content and speech
Self Learning
The advance machine learning
system makes decisions by
analyzing its own data and
making patterns
Learning on Examples
This method is used when a
machine learns through
examples. For instance, Google’s
automatic spam filtering learns
as users report spam.
Learning on Experience
The system learns from positive and negative
experiences.
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From Patterns to Automation
AI Models
The idea is that an algorithm will sift through the data, learn from it, and apply it to make a decision. This can be seen in any
recommendation type service. Machine Learning takes it a step farther by automating tasks; helping data security firms identify potential
threats or finance looking for favorable deals.
AI’s can be Transactional in which a question is asked and an answer is given, like a virtual assistant. AI’s can also be Automated in which
routine tasks such automatically taking trash out on garbage day.
13. Healthcare
Used in healthcare to identify and notice predictable
trends – such as having a machine look at charts to
recognize tumors sooner with more accuracy – or eyes to
determine stage of glaucoma
14.
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Good Read:
https://www.businessinsider.com/healthcare-artificial-intelligence-pitfalls-2019-3
16. Smart Homes
See how a home can alert when it sees a person versus an
animal or know that its going to rain tomorrow so no
need to water the grass today
17.
18.
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Inspiring AI’s
AI: AlphaGo
AlphaGo is the first AI to beat a
human in arguably the most
difficult game to master. AlphaGo
now teaches moves to trainees.
AI: ROSS
ROSS is an AI tool to make legal
research easier and faster
20.
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Run-away AI’s
Tay (Thinking About You)
Released on March 23 2016 via Twitter, Tay (as TayTweets
on Twitter) was designed to mimic the interactions of 19
year old girl through learned conversations on Twitter.
Users began tweeting pollitcally incorrect phrases to Tay,
and thus, Tay responded and answered with the learned
inappropriate behavior – as it was it was not taught what
the difference between Good Language and Bad Language
was.
Microsoft Artificial Chatter Bot
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Run-away AI’s
Inspirobot.me
I am an artificial intelligence dedicated to generating
unlimited amounts of unique inspirational quotes for
endless enrichment of pointless human existence.
-- From their website
Happy Accidents
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Logic and Rules Based
Challenges for AI
Training
Similar to having good data, an AI
might need to learn the correct
response for the correct situation or
identify dangers or inappropriate
interactions
Precision
The idea of garbage data in
garbage data out. If you
flood an AI with bad data and
don’t set the proper syntax or
thresholds you will get
incoherent results
Context
AI’s can struggle with understanding context. For
example, asking Siri ”call me an ambulance” may yield
“OK, from now on, I will call you Ambulance”
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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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Activity
https://affinelayer.com/pixsrv/
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Bringing It All Together
Impact On Staff
At some point, AI can very well
replace some functions and
roles of what we do. IE:
Receptionist/Simple Q & A
Improving AI
If building an AI solution, you
will want to be accurate with
the data you provide
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Making Your Job Better
Shift of Focus
AI can replace many of our tasks that we do to make them:
* More Efficient =
* More Accurate = As long as the data provided to the
AI is “good”, then the function will be more accurate.
*Tedious Work may be “outsourced” to the AI
Making The Work Day More Meaningful
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AI Struggles
Shift of Focus
AI may not have emotional intelligence or show
empathy…(yet).
AI makes a decision based on facts and may
not account for emotional variables.
To assume AI can’t replace a job role because it
doesn’t have emotional intelligence would be a
failure point to any organization.
Empathy / Emotional Intelligence
53. AI Assistants for Content
AI Content Tools:
· https://rytr.me/
o Create stories, blogs, tweets from a few
key words generated by AI
· https://app.wordhero.co/home
o Create text for a variety of different
topics
· https://app.peppertype.ai/home
o Create text for a variety of different
topics
Video Creation
· https://pictory.ai/
o Create curated stock videos based on
your text
Audio Creation
· https://app.fliki.ai/
o Text to Speech with a variety of voice
overs
Marketing Creation:
https://app.steve.ai
● Create animations based on your text
https://reachout.ai/ or https://bhuman.ai
● Create marketing campaigns from a single
video
54. More AI Assistants
Art Creation:
· https://supermachine.art/
o Create AI-Generated Art
https://aimages.ai/
o Create AI vector art (MagicStock)
https://www.airbrush.ai/
o Create art and enhancements
https://stockimg.ai/
o Create Book Covers and Logos
Course Creation:
· https://minicoursegenerator.com/
o Create a course made by AI
https://links.evolveproject.org/aitools
56. Other Challenges
• AI in Education
• AI Generative Inbreeding
• Content Pulls – what source(s) are they using
• Using AI to write all your blog posts
57. More Resources
• Guides for getting started:
• https://links.evolveproject.org/ai_resources
• Tech Gadgets Guides:
• https://links.evolveproject.org/ai_gadgets