1.
Demystifying Artificial Intelligence:
Solving Difficult Problems
Carol Smith @carologic
ProductCamp Pittsburgh @PGHPCAMP
September 22, 2018
This work is licensed under a Creative
Commons Attribution-NonCommercial
4.0 International License except where
noted otherwise.

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Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
Help humanity...

3.
Why are AI
experiences
so challenging?

4.
Complex
Unknowable
Confusing
Mysterious

5.
AI is as imperfect as the
humans making it.

6.
Engender Trust

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Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
Making AI in Westworld
• Who made the data
– Host backstories and scripts
– Environmental design
• What is data’s provenance?

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Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
Data creator: Writer
• Creates scripts and
stories for hosts/robots
• Determines how and
when will be presented

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Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
Why should we care?
• What are his bias’?
– Straight, white male
– Reused scripts due to deadlines and a lack of creativity
– What else?
• How did this affect the experience?
• Does it matter?

10.
Who trained
and programmed
the system?

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Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
System training and maintenance: Scientists
• Triage system when
there are issues
• Adjust programming
and settings
• Review previous
stories “Step into
analysis”

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Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
Why should we care?
• What are their weaknesses’?
– Not very creative
– Seemingly minimal exposure to the rest of the world

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Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
Why is the host acting like that?

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Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
Programming
• Ford/Arnold as
programmer
• Westworld does
an excellent
job of explaining this

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Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
Why should we care?
• No context - few mental models.
• Help understand what is going on.

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Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
Reality is… Not a great example!

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Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
To engender trust, provide transparency
• Data
• Training/programming of system
• Rationale/bias/logic

18.
What is AI?

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Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
AI is dynamic
• Mature AI takes data and training
- applies to new situations.
• Attributions to new data may be:
– Inaccurate
– Weird
– Inappropriate
– Unintended

20.
AI is present when computers/machines
– Exhibit intelligence
– Perceive their environment
– Take actions/make decision
to maximize chance of success at a goal
Our Road to Self-Driving Vehicles | Uber ATG
https://youtu.be/27OuOCeZmwI

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Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
AI/Cognitive computers are
• Made with algorithms.
• Limited domain knowledge – only what you teach.
• Control ONLY what we give them control of.
• Aware of nuances and can continue to learn.

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Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
Number Five “Needs Input”
Short Circuit (1986 film)
Ally Sheedy and Number Five (Tim Blaney)
https://en.wikipedia.org/wiki/Short_Circuit_(1986_film)

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Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
Types of AI – Machine Learning
• Supervised learning
– Input data and target variable
– Need specialist to do training
– Most common
Artificial Intelligence Demystified by. Rahul December 23, 2016. Analytics Vidhya
https://www.analyticsvidhya.com/blog/2016/12/artificial-intelligence-demystified/

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Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
Annotating Content
Image created by Angela Swindell, Visual Designer, IBM

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Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
Types of AI – Machine Learning
• Unsupervised learning
– Input data – machine defines patterns
• Reinforced learning
– Games – creator specifies rules and reward, machine learns
Artificial Intelligence Demystified by. Rahul December 23, 2016. Analytics Vidhya
https://www.analyticsvidhya.com/blog/2016/12/artificial-intelligence-demystified/

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Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
Machine Training Creation Methods
Creation Method Duration Knowledge / Accuracy
Supervised
Programmed with knowledge transfer
Months - years Best
Supervised
Entry by content specialist
Weeks - months High potential
Unsupervised Days - weeks Core knowledge only
Reinforced learning Varies Highly accurate

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Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
Supervised Machine Learning - GUI
Watson Knowledge Studio, Supervised Machine Learning:
https://www.ibm.com/us-en/marketplace/supervised-machine-learning

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Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
Pattern recognition
• Natural Language
Processing
• Image Analysis
• 88,000 retina images
– Recognize healthy eye
– Glaucoma #2 cause of
blindness worldwide
– 50% of cases undetected
IBM Watson https://twitter.com/IBMWatson/status/844545761740292096

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Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
Deep Learning
• Best at classifying objects
based on features
• Can be applied
to other types of AI
• Lots of AI systems working
together
Toward ethical, transparent and fair AI/ML: a critical reading list, by Eirini Malliaraki, Feb 19 via
tweet from @robmccargow https://medium.com/@eirinimalliaraki/toward-ethical-transparent-and-
fair-ai-ml-a-critical-reading-list-d950e70a70ea

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Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
Taxonomies and Ontologies Come to Life
(NOT like humans learn)
Photo: https://commons.wikimedia.org/wiki/File:Baby_Boy_Oliver.jpg

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Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
Humans Teach and Monitor AI
• Water – add new information and teach (continuous)
• Thin – pluck poor performing models, bad patterns
• Prune – as AI matures, continuous monitoring,
adding and removing functionality.
• Cull – remove/stop broken/biased models.

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Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
Use Case: Lawn care treatment selection
• Users: Lawn technicians and sales people.
• Goal: More quickly and effectively customize solutions
for customers and minimize costs
(time, effort, chemical amount, cost to customer, etc.).

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Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
Hire a Consulting Firm
• Understand users, goals.
• Review existing data
– Knowledge about lawn care products.
– Great data from a few technicians.
– Create ground truth and teach AI (few weeks).

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Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
Can’t Just Turn AI systems on
• Subject matter experts (SME’s) knowledge needed
– Lawyers
– Machinists
– Insurance adjusters
– Physicians
• Working with experts in AI systems.

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Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
Lawn Care – Data and Training
• Data:
– Patterns across customers
– Effectiveness
of treatments.
– Extent of problems,
pests, etc.
• Training for:
– Types of grass.
– Conditions (sun
exposure, etc.).
– Customer attitude
about lawn care.

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Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
Ready for Use
• Experts begin reviewing results
– Too many recommendations for heavy chemical use.
– Need to replace models that aren’t working.
• But what went wrong

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Who?
What problem?

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Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
Use best practices
• Understand problem deeply
• Select AI system
– Different problems require different systems
• Build right AI system, in ethical way to solve problem

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Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
Who will use the system and why?
• What are their goals?
• What problems are they trying to solve?
• Team/independent work?

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Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
How trusting are the users of AI?
• Work to gain trust?
• What might engender trust via the UI?

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Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
What questions most likely asked about data?
• What…
– Comes next?
– Outliers?
– Changed? How can I tell what changed?
– New? Unexpected?
– Validates assumptions?
– Increased/decreased frequency?

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Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
Anticipated changes with AI system?
• We need an AI for that!
• Intention? Improvements?
• Better or faster?
• Scope?

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Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
What are potential unintended consequences?
• Understand user’s fears
• Become familiar
• Learn how to address
• Fears lead to potential unintended consequences
– Preparing for these will protect your users

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Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
AI Management Matches Org Ecosystem
• Microcosm of organization
– Not independent of organization - same issues
• Need similar support
– Curating content
– Watching for issues, etc.
– Managing, training, and oversight

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Content

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Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
Data Source
• In existence?
• Available?
• High quantity?
• High quality?
Photo by sunlightfoundation
https://www.flickr.com/photos/sunlightfoundation/2385174105

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Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
Curation
• Where content sourced from?
– Bias?
– Organization?
– Potential unintended consequences?
• Who is creating/curating collection?
– Respected experts in industry
– Diverse, socio-economic, cross cultural, international team
“3 guiding principles for ethical AI, from IBM CEO Ginni Rometty”
by Alison DeNisco. January 17, 2017, Tech Republic http://www.techrepublic.com/article/3-
guiding-principles-for-ethical-ai-from-ibm-ceo-ginni-rometty/

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Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
All data is biased - what is bias?
• All bring personal experience and knowledge
• Affected by:
– Social class, resource availability
– Race, Gender, Sexuality
– Culture, Theology, Tradition
– Other factors we aren’t even aware of

49.
“We often have
no way of knowing
when and why people
are biased.”
- Sandra Wachter
Q&A: Should artificial intelligence be legally required to explain itself?
By Matthew Hutson, May. 31, 2017. Interview with Sandra Wachter, data ethics researcher at Univ. of Oxford and Alan Turing Institute.
http://www.sciencemag.org/news/2017/05/qa-should-artificial-intelligence-be-legally-required-explain-itself

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Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
Lawn Care - what happened?
• Understand users, goals.
• Review existing data
– Knowledge about lawn care products.
– Great data from a few technicians.
– Create ground truth and teach AI (few weeks).

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Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
Data Source Problematic
• Few technicians who are
great at documenting.
– Prefer using chemicals
to treat lawns.
– Limited data biased
towards chemical use.
• Most technicians, take
horrible notes.
• Prefer “all natural”
treatments.
Neither are wrong.
Limited data created a bias.

52.
AI is only as good as data
and time spent improving it.
Biased based on what taught.

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Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
Data created by those who write
• History written by victors
• Lawn care specialists

54.
Training

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Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
Which experts will train the system?
• How are they vetted?
• How frequently will they be available?
• How will quality be maintained?
• Where will they work?
• What process will they use?
• When something goes wrong, how do you respond?

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Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
How important is accuracy?
• How accurate must the system be?
• Consider your colleagues
• You are creating another colleague…

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Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
Across Industries – Priority of Accuracy Varies
Higher Priority
90-99%+
Lower Priority
60-89% accuracy is acceptable
Financial
Ecommerce

58.
Design AI
Responsibly

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Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
Keep people at the center of our work
• Great solutions are made when they solve a problem
for people
• User’s goals
• Ethics

60.
Intentional
Design
http://www.flickr.com/photos/rockyvi/6451635085/sizes/m/in/photolist-aQ7jkF/
Some rights reserved by Rocky VI - http://www.flickr.com/photos/rockyvi/
License: http://creativecommons.org/licenses/by-nc-nd/2.0/

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Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
Intentional Design
• Keep people and data safe
• When unintended consequences arise,
how do we deal with them?

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Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
Make it your business to keep people safe
• Warning signs?
• How do we deal with unintentional consequences?
• What is the worst potential outcome?

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Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
Make a Plan
• No need to imagine
every single situation
• Who is notified
immediately?
• What is the method for
“turning it off”?
• Unintended consequences
of turning it off?
Google’s new tensor processing units:
https://www.nytimes.com/2018/02/12/technology/google-artificial-intelligence-chips.html

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Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
What will you do?
• Focus on how you’ll react to worst situations:
• What happens when it becomes a Nazi?
• What happens when it does XYZ unexpectedly?

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Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
Back doors and brakes
• A way to get into the system and shut it down
• Secured from inside and outside
• “If it’s not usable, it’s not secure.”
– Jared Spool, IAS17
Unintuitive and Insecure: Fixing the Failures of Authentication,
Jared Spool, IA Summit 2017

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Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
Privacy and Ownership
• Who owns the data?
– User owns what and when?
– Organization owns what and when?
• What must a user reveal?
– Life expectancy of data?
– Transitional phases?
Ethical Issues in IS by Richard Mason’s
H/T to Andrea Resmini
https://www.gdrc.org/info-design/4-ethics.html

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Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
Access to Data
• What do we have a right to access?
• Barriers
– Literacy and awareness.
– Connection to internet – economics and location.
– Access to pertinent data.

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Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
Who gets to use our tools?
• “When we do not design for people with disabilities
we are being ableist”
– Anne Gibson @perpendicularme #ias18 Roundtable on Ethics
How People with Disabilities Use the Web: Overview https://www.w3.org/WAI/intro/people-use-web /

69.
Crazy, Evil?
Reduce their power.

70.
Grady Booch, Scientist, philosopher, IBM’er https://www.ted.com/talks/grady_booch_don_t_fear_superintelligence
Our Responsibility:
Ensure that humans
can unplug
the machines!

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Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
Communicating
About
The System
Strong Bad Email #45 – Techno - Strong Bad makes a techno song.
https://youtu.be/JwZwkk7q25I Homestarrunnerdotcom Published on Mar 31, 2009

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Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
Communicate Responsibly
• How is communication about the AI handled?
– How do you report issues?
• To whom?
– Everyone needs to be bought in.
– Not everyone can fix it.

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Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
Potential Bias
• Show awareness; acknowledge issues.
– What are potential signs of building bias?
– Over communicate about potential bias.
• Acknowledge potential for bad decisions
based on data.
– Who is responsible?

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Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
Data and Training Transparency
• What data was it based on?
– Sources referenced?
– Access to overall collection?
• Who trained AI?
– Experience? Background? Vetting?
– Who will update it? How often?

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Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
How do users know when something is wrong?
• Show examples of changes.
• Where do these examples live?
• How can a user contest something and report it?

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Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
Confidence in Content
• Representing confidence.
• How is ‘ABC’ comparable to other entries?

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Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
Displaying Information
• AI generated content
– separate from other content?
– marked as AI generated?
– more clearly referenced?
– does it matter?

78.
As AI matures -
update
communication
approach

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Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
Ethics for AI

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Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
If we don’t ask tough
questions, who will?

81.
Do we want users
to trust AI?
How much?

82.
Humans teach what we feel is important… teach them to share our values.
Grady Booch, Scientist, philosopher, IBM’er https://www.ted.com/talks/grady_booch_don_t_fear_superintelligence

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Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
Help humanity...

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Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
Create a code of conduct/ethics
• What do you value?
• What lines won’t your AI cross?
• How will you track your progress?

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Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
To engender trust, provide transparency
• Who made the data?
• Who has access to users’ data/useage?
• Who trained/programmed the system?
• Why system providing data it is?

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Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
Take Responsibility
• Keep humans in control.
• Hire/work with people affected by bias
– Non-typical schools, non-typical careers, etc.
– POC, LGBTQ+, women
• Conduct auditing
How to Keep Your AI from Turning into a Racist Monster by Megan
Garciahttps://www.wired.com/2017/02/keep-ai-turning-racist-monster/

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Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
Teach others about AI
• Demystify in plain language
• Teach others
• Provide easy way to raise concerns
(anonymous if appropriate)

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Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
Great Problem Solvers…
• People and problems
• Not “technology first.”
• Ethics

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Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
Learn about making ethical, transparent and fair AI
Toward ethical, transparent and fair AI/ML: a critical reading list, by Eirini Malliaraki, Feb 19 via tweet from
@robmccargow https://medium.com/@eirinimalliaraki/toward-ethical-transparent-and-fair-ai-ml-a-critical-
reading-list-d950e70a70ea

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Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
Create Ethical AI
• Less-biased content
• Intentional design
• Communicate responsibly about AI

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Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
Don’t fear AI - Explore AI
Try out tools (see Appendix and footer notes)
Pair with others

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Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
Discussion
Common AI Topics

93.
Trolley Problem
Trolley Car 36, Rockford, Illinois https://www.rockfordparkdistrict.org/trolley

94.
Does the Trolley Problem Have a Problem? What if your answer to an absurd hypothetical question had no bearing on how you behaved in real life?
By Daniel Engber. Slate.com. June 18, 2018. Image of anxious hypothetical trolley car lever operator by Lisa Larson-Walker
https://slate.com/technology/2018/06/psychologys-trolley-problem-might-have-a-problem.html

95.
My AI is Alive!
It came up with something new
and we have no idea why or how!

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Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
Contact Carol
LinkedIn – CarolJSmith
Twitter - @Carologic
Slideshare – carologic
SpeakerRate - CarolJSmith

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Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
Additional Information
and Resources

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Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
AI Tools
• A list of artificial intelligence tools you can use today — for businesses, by Liam
Hanel, July 11, 2017 on Lyr.AI
https://lyr.ai/a-list-of-artificial-intelligence-tools-you-can-use-today%E2%80%8A-
%E2%80%8Afor-businesses/ and https://medium.com/imlyra/a-list-of-artificial-
intelligence-tools-you-can-use-today-for-personal-use-1-3-7f1b60b6c94f
• Best AI and machine learning tools for developers, By Christina Mercer, Sep 26,
2017 in Techworld from IDG https://www.techworld.com/picture-gallery/apps-
wearables/best-ai-machine-learning-tools-for-developers-3657996/
• 15 Top Open Source Artificial Intelligence Tools by Cynthia Harvey, September
12, 2016 on Datamation https://www.datamation.com/open-source/slideshows/15-
top-open-source-artificial-intelligence-tools.html
• IBM Watson Developer Tools (free trials):
https://console.ng.bluemix.net/catalog/?category=watson

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Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
Want to Know More?
• The Rise Of Artificial Intelligence As A Service In The Public
Cloud
Rise Of Artificial Intelligence As A Service In The Public Cloud by Janakiram MSV , Forbes Article:
https://www.forbes.com/sites/janakirammsv/2018/02/22/the-rise-of-artificial-intelligence-as-a-service-in-the-public-cloud/#11aa85a8198e
Courses at http://www.fast.ai/

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Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
10 Major Milestones in the History of AI
https://www.analyticsvidhya.com/blog/2016/12/artificial-intelligence-demystified/

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Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
Additional Resources
• “How IBM is Competing with Google in AI.” The Information. https://www.theinformation.com/how-ibm-is-
competing-with-google-in-ai?eu=2zIDMNYNjDp7KqL4YqAXXA
• “The business case for augmented intelligence” https://medium.com/cognitivebusiness/the-business-case-for-
augmented-intelligence-36afa64cd675
• “Comparison of machine learning methods applied to birdsong element classification” by David Nicholson.
Proceedings of the 15th Python in Science Conference (SCIPY 2016).
http://conference.scipy.org/proceedings/scipy2016/pdfs/david_nicholson.pdf
• “Staples’ “Easy Button” Comes to Life with IBM Watson” in Business Wire, October 25, 2016.
http://www.businesswire.com/news/home/20161025006273/en/Staples%E2%80%99-%E2%80%9CEasy-
Button%E2%80%9D-Life-IBM-Watson
• “How Staples Is Making Its Easy Button Even Easier With A.I.” by Chris Cancialosi, Forbes.
https://www.forbes.com/sites/chriscancialosi/2016/12/13/how-staples-is-making-its-easy-button-even-easier-
with-a-i/#4ae66e8359ef
• “Inside Intel: The Race for Faster Machine Learning”
http://www.intel.com/content/www/us/en/analytics/machine-learning/the-race-for-faster-machine-learning.html

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Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
More Resources
• “Update: Why this week’s man-versus-machine Go match doesn’t matter (and what does)” by Dana
Mackenzie. Science Magazine. Mar. 15, 2016 http://www.sciencemag.org/news/2016/03/update-why-week-s-
man-versus-machine-go-match-doesn-t-matter-and-what-does
• “For IBM’s CTO for Watson, not a lot of value in replicating the human mind in a computer.” by Frederic
Lardinois (@fredericl), TechCrunch, Posted Feb 27, 2017. https://techcrunch.com/2017/02/27/for-ibms-cto-
for-watson-not-a-lot-of-value-in-replicating-the-human-mind-in-a-computer/
• “Google and IBM: We Want Artificial Intelligence to Help You, Not Replace You” Most Powerful Women by
Michelle Toh. Mar 02, 2017. Fortune. http://fortune.com/2017/03/02/google-ibm-artificial-intelligence/
• “Facebook scales back AI flagship after chatbots hit 70% f-AI-lure rate - 'The limitations of automation‘” by
Andrew Orlowski. Feb 22, 2017. The Register https://www.theregister.co.uk/2017/02/22/facebook_ai_fail/
• “Microsoft is deleting its AI chatbot's incredibly racist tweets” by Rob Price. Mar. 24, 2016. Business Insider
UK. http://www.businessinsider.com/microsoft-deletes-racist-genocidal-tweets-from-ai-chatbot-tay-2016-3
Special Thanks: Soundtrack to 'Run Lola Run', 1998 German thriller film written and directed by Tom Tykwer,
and starring Franka Potente as Lola and Moritz Bleibtreu as Manni. Soundtrack by Tykwer, Johnny Klimek, and
Reinhold Heil

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Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
Even More Resources
• “IBM’s Automated Radiologist Can Read Images and Medical Records” by Tom Simonite, February 4, 2016.
Intelligent Machines, MIT Technology Review. https://www.technologyreview.com/s/600706/ibms-automated-
radiologist-can-read-images-and-medical-records/
• “The IBM, Salesforce AI Mash-Up Could Be a Stroke of Genius” by Adam Lashinsky, Mar 07, 2017. Fortune.
http://fortune.com/2017/03/07/data-sheet-ibm-salesforce/
• "Google can now tell you're not a robot with just one click" by Andy Greenberg. Dec. 3, 2014. Security: Wired.
https://www.wired.com/2014/12/google-one-click-recaptcha/
• “Essentials of Machine Learning Algorithms (with Python and R Codes)” by Sunil Ray, August 10, 2015.
Analytics Vidhya. https://www.analyticsvidhya.com/blog/2015/08/common-machine-learning-algorithms/
• IBM on Machine Learning https://www.ibm.com/analytics/us/en/technology/machine-learning/
• “At Davos, IBM CEO Ginni Rometty Downplays Fears of a Robot Takeover” by Claire Zillman, Jan 18, 2017.
Fortune. http://fortune.com/2017/01/18/ibm-ceo-ginni-rometty-ai-davos/
• “Google and IBM: We Want Artificial Intelligence to Help You, Not Replace You” by Michelle Toh. Mar 02,
2017. Fortune. http://fortune.com/2017/03/02/google-ibm-artificial-intelligence/

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Yes, even more resources
• Video: “IBM Watson Knowledge Studio: Teach Watson about your unstructured data”
https://www.youtube.com/watch?v=caIdJjtvX1s&t=6s
• “The optimist’s guide to the robot apocalypse” by Sarah Kessler, @sarahfkessler. March 09, 2017. QZ.
https://qz.com/904285/the-optimists-guide-to-the-robot-apocalypse/
• “AI Influencers 2017: Top 30 people in AI you should follow on Twitter" by Trips Reddy @tripsy, Senior
Content Manager, IBM Watson . February 10, 2017 https://www.ibm.com/blogs/watson/2017/02/ai-
influencers-2017-top-25-people-ai-follow-twitter/
• “3 guiding principles for ethical AI, from IBM CEO Ginni Rometty” by Alison DeNisco. January 17, 2017, Tech
Republic http://www.techrepublic.com/article/3-guiding-principles-for-ethical-ai-from-ibm-ceo-ginni-rometty/
• "Transparency and Trust in the Cognitive Era" January 17, 2017 Written by: IBM THINK Blog
https://www.ibm.com/blogs/think/2017/01/ibm-cognitive-principles/
• "Ethics and Artificial Intelligence: The Moral Compass of a Machine“ by Kris Hammond, April 13, 2016.
Recode. http://www.recode.net/2016/4/13/11644890/ethics-and-artificial-intelligence-the-moral-compass-of-a-
machine

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Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
Last bit: I promise
• "The importance of human innovation in A.I. ethics" by John C. Havens. Oct. 03, 2015
http://mashable.com/2015/10/03/ethics-artificial-intelligence/#yljsShvAFsqy
• "Me, Myself and AI" Fjordnet Limited 2017 - Accenture Digital.
https://trends.fjordnet.com/trends/me-myself-ai
• "Testing AI concepts in user research" By Chris Butler, Mar 2, 2017. https://uxdesign.cc/testing-ai-
concepts-in-user-research-b742a9a92e55#.58jtc7nzo
• "CMU prof says computers that can 'see' soon will permeate our lives“ by Aaron Aupperlee. March
16, 2017. http://triblive.com/news/adminpage/12080408-74/cmu-prof-says-computers-that-can-
see-soon-will-permeate-our-lives
• “The business case for augmented intelligence” by Nancy Pearson, VP Marketing, IBM Cognitive.
https://medium.com/cognitivebusiness/the-business-case-for-augmented-intelligence-
36afa64cd675#.qqzvunakw

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Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
Definition: Artificial Intelligence
• Artificial intelligence (AI) is intelligence exhibited by machines.
• In computer science, an ideal "intelligent" machine is a flexible rational agent that
perceives its environment and takes actions that maximize its chance of success
at some goal.[1] Colloquially, the term "artificial intelligence" is applied when a
machine mimics "cognitive" functions that humans associate with other human
minds, such as "learning" and "problem solving".[2]
• Capabilities currently classified as AI include successfully understanding human
speech,[4] competing at a high level in strategic game systems (such as Chess
and Go[5]), self-driving cars, and interpreting complex data.
Wikipedia: https://en.wikipedia.org/wiki/Artificial_intelligence#cite_note-Intelligent_agents-1

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Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
Definition: The Singularity
• If research into Strong AI produced sufficiently intelligent software, it might be able to reprogram
and improve itself. The improved software would be even better at improving itself, leading to
recursive self-improvement.[245] The new intelligence could thus increase exponentially and
dramatically surpass humans. Science fiction writer Vernor Vinge named this scenario
"singularity".[246] Technological singularity is when accelerating progress in technologies will
cause a runaway effect wherein artificial intelligence will exceed human intellectual capacity and
control, thus radically changing or even ending civilization. Because the capabilities of such an
intelligence may be impossible to comprehend, the technological singularity is an occurrence
beyond which events are unpredictable or even unfathomable.[246]
• Ray Kurzweil has used Moore's law (which describes the relentless exponential improvement in
digital technology) to calculate that desktop computers will have the same processing power as
human brains by the year 2029, and predicts that the singularity will occur in 2045.[246]
Wikipedia: https://en.wikipedia.org/wiki/Artificial_intelligence#cite_note-Intelligent_agents-1

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Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic
Definition: Machine Learning
• Ability for system to take basic knowledge (does not mean simple or non-complex)
and apply that knowledge to new data
• Raises ability to discover new information. Find unknowns in data.
• https://en.wikipedia.org/wiki/Machine_learning
More Definitions:
• Algorithm: a process or set of rules to be followed in calculations or other problem-
solving operations, especially by a computer.
https://en.wikipedia.org/wiki/Algorithm
• Natural Language Processing (NLP):
https://en.wikipedia.org/wiki/Natural_language_processing