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Demystifying Artificial Intelligence: Solving Difficult Problems at ProductCamp Pittsburgh

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Artificially intelligent systems are becoming part of our everyday lives. This session will answer your questions about artificial intelligence, machine learning, and the ethical conflicts and the implications inherent in these technologies. Topics covered will include: discussions of bias in data; how to focus on the user experience; what is necessary to build a good cognitive computing systems; data needs; levels of accuracy; making safe and secure AI's; and discussions on ethics in AI and our role in leading those conversations. Carol will propose simple models for thinking about these systems and provide time for questions. You will walk away with an awareness of the weaknesses of AI and the knowledge of how these systems work.
Selected by the audience to be presented at ProductCamp Pittsburgh in September 2018

Published in: Design
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Demystifying Artificial Intelligence: Solving Difficult Problems at ProductCamp Pittsburgh

  1. 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.
  2. 2. Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic Help humanity...
  3. 3. Why are AI experiences so challenging?
  4. 4. Complex Unknowable Confusing Mysterious
  5. 5. AI is as imperfect as the humans making it.
  6. 6. Engender Trust
  7. 7. 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?
  8. 8. Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic Data creator: Writer • Creates scripts and stories for hosts/robots • Determines how and when will be presented
  9. 9. 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. 10. Who trained and programmed the system?
  11. 11. 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”
  12. 12. 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
  13. 13. Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic Why is the host acting like that?
  14. 14. Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic Programming • Ford/Arnold as programmer • Westworld does an excellent job of explaining this
  15. 15. Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic Why should we care? • No context - few mental models. • Help understand what is going on.
  16. 16. Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic Reality is… Not a great example!
  17. 17. Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic To engender trust, provide transparency • Data • Training/programming of system • Rationale/bias/logic
  18. 18. What is AI?
  19. 19. 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. 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
  21. 21. 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.
  22. 22. 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)
  23. 23. 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/
  24. 24. Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic Annotating Content Image created by Angela Swindell, Visual Designer, IBM
  25. 25. 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/
  26. 26. 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
  27. 27. 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
  28. 28. 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
  29. 29. 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
  30. 30. 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
  31. 31. 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.
  32. 32. 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.).
  33. 33. 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).
  34. 34. 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.
  35. 35. 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.
  36. 36. 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
  37. 37. Who? What problem?
  38. 38. 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
  39. 39. 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?
  40. 40. 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?
  41. 41. 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?
  42. 42. Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic Anticipated changes with AI system? • We need an AI for that! • Intention? Improvements? • Better or faster? • Scope?
  43. 43. 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
  44. 44. 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
  45. 45. Content
  46. 46. 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
  47. 47. 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/
  48. 48. 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. 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
  50. 50. 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).
  51. 51. 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. 52. AI is only as good as data and time spent improving it. Biased based on what taught.
  53. 53. Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic Data created by those who write • History written by victors • Lawn care specialists
  54. 54. Training
  55. 55. 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?
  56. 56. 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…
  57. 57. 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. 58. Design AI Responsibly
  59. 59. 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. 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/
  61. 61. Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic Intentional Design • Keep people and data safe • When unintended consequences arise, how do we deal with them?
  62. 62. 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?
  63. 63. 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
  64. 64. 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?
  65. 65. 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
  66. 66. 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
  67. 67. 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.
  68. 68. 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. 69. Crazy, Evil? Reduce their power.
  70. 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!
  71. 71. 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
  72. 72. 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.
  73. 73. 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?
  74. 74. 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?
  75. 75. 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?
  76. 76. Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic Confidence in Content • Representing confidence. • How is ‘ABC’ comparable to other entries?
  77. 77. 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. 78. As AI matures - update communication approach
  79. 79. Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic Ethics for AI
  80. 80. Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic If we don’t ask tough questions, who will?
  81. 81. Do we want users to trust AI? How much?
  82. 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
  83. 83. Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic Help humanity...
  84. 84. 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?
  85. 85. 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?
  86. 86. 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/
  87. 87. 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)
  88. 88. Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic Great Problem Solvers… • People and problems • Not “technology first.” • Ethics
  89. 89. 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
  90. 90. Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic Create Ethical AI • Less-biased content • Intentional design • Communicate responsibly about AI
  91. 91. Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic Don’t fear AI - Explore AI Try out tools (see Appendix and footer notes) Pair with others
  92. 92. Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic Discussion Common AI Topics
  93. 93. Trolley Problem Trolley Car 36, Rockford, Illinois https://www.rockfordparkdistrict.org/trolley
  94. 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. 95. My AI is Alive! It came up with something new and we have no idea why or how!
  96. 96. Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic Contact Carol LinkedIn – CarolJSmith Twitter - @Carologic Slideshare – carologic SpeakerRate - CarolJSmith
  97. 97. Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic Additional Information and Resources
  98. 98. 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
  99. 99. 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/
  100. 100. 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/
  101. 101. 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
  102. 102. 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
  103. 103. 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/
  104. 104. Demystifying Artificial Intelligence: Solving Difficult Problems / @carologic 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
  105. 105. 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
  106. 106. 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
  107. 107. 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
  108. 108. 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

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