Demystifying AI: Solving Problems

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.

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

Why are AI
experiences
so challenging?

Complex
Unknowable
Confusing
Mysterious

AI is as imperfect as the
humans making it.

Making AI in Westworld
• Who made the data
– Host backstories and scripts
– Environmental design
• What is data’s provenance?

Data creator: Writer
• Creates scripts and
stories for hosts/robots
• Determines how and
when will be presented

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?

Who trained
and programmed
the system?

System training and maintenance: Scientists
• Triage system when
there are issues
• Adjust programming
and settings
• Review previous
stories “Step into
analysis”

Why should we care?
• What are their weaknesses’?
– Not very creative
– Seemingly minimal exposure to the rest of the world

Why is the host acting like that?

Programming
• Ford/Arnold as
programmer
• Westworld does
an excellent
job of explaining this

Why should we care?
• No context - few mental models.
• Help understand what is going on.

Reality is… Not a great example!

To engender trust, provide transparency
• Data
• Training/programming of system
• Rationale/bias/logic

AI is dynamic
• Mature AI takes data and training
- applies to new situations.
• Attributions to new data may be:
– Inaccurate
– Weird
– Inappropriate
– Unintended

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

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.

Number Five “Needs Input”
Short Circuit (1986 film)
Ally Sheedy and Number Five (Tim Blaney)
https://en.wikipedia.org/wiki/Short_Circuit_(1986_film)

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/

Annotating Content
Image created by Angela Swindell, Visual Designer, IBM

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

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

Supervised Machine Learning - GUI
Watson Knowledge Studio, Supervised Machine Learning:
https://www.ibm.com/us-en/marketplace/supervised-machine-learning

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

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

Taxonomies and Ontologies Come to Life
(NOT like humans learn)
Photo: https://commons.wikimedia.org/wiki/File:Baby_Boy_Oliver.jpg

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.

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.).

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).

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.

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.

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

Use best practices
• Understand problem deeply
• Select AI system
– Different problems require different systems
• Build right AI system, in ethical way to solve problem

Who will use the system and why?
• What are their goals?
• What problems are they trying to solve?
• Team/independent work?

How trusting are the users of AI?
• Work to gain trust?
• What might engender trust via the UI?

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?

Anticipated changes with AI system?
• We need an AI for that!
• Intention? Improvements?
• Better or faster?
• Scope?

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

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

Data Source
• In existence?
• Available?
• High quantity?
• High quality?
Photo by sunlightfoundation
https://www.flickr.com/photos/sunlightfoundation/2385174105

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/

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

“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

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).

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.

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

Data created by those who write
• History written by victors
• Lawn care specialists

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?

How important is accuracy?
• How accurate must the system be?
• Consider your colleagues
• You are creating another colleague…

Across Industries – Priority of Accuracy Varies
Higher Priority
90-99%+
Lower Priority
60-89% accuracy is acceptable
Financial
Ecommerce

Keep people at the center of our work
• Great solutions are made when they solve a problem
for people
• User’s goals
• Ethics

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/

Intentional Design
• Keep people and data safe
• When unintended consequences arise,
how do we deal with them?

Make it your business to keep people safe
• Warning signs?
• How do we deal with unintentional consequences?
• What is the worst potential outcome?

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

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?

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

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

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.

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 /

Crazy, Evil?
Reduce their power.

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!

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

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.

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?

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?

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?

Confidence in Content
• Representing confidence.
• How is ‘ABC’ comparable to other entries?

Displaying Information
• AI generated content
– separate from other content?
– marked as AI generated?
– more clearly referenced?
– does it matter?

As AI matures -
update
communication
approach

Ethics for AI

If we don’t ask tough
questions, who will?

Do we want users
to trust AI?
How much?

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

Create a code of conduct/ethics
• What do you value?
• What lines won’t your AI cross?
• How will you track your progress?

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?

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/

Teach others about AI
• Demystify in plain language
• Teach others
• Provide easy way to raise concerns
(anonymous if appropriate)

Great Problem Solvers…
• People and problems
• Not “technology first.”
• Ethics

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

Create Ethical AI
• Less-biased content
• Intentional design
• Communicate responsibly about AI

Don’t fear AI - Explore AI
Try out tools (see Appendix and footer notes)
Pair with others

Discussion
Common AI Topics

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

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

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

Contact Carol
LinkedIn – CarolJSmith
Twitter - @Carologic
Slideshare – carologic
SpeakerRate - CarolJSmith

Additional Information
and Resources

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

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/

10 Major Milestones in the History of AI

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

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

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/

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

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

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

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

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

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