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UX in the Age of AI: Leading with Design UXPA2018

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How can designers improve trust of cognitive systems? What can we do to make these systems transparent? What information needs to be transparent? The biggest challenges inherent with AI will be discussed, specifically the ethical conflicts and the implications for your work, along with the basics of these concepts so that you can strive for making great AI systems.

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UX in the Age of AI: Leading with Design UXPA2018

  1. 1. UX in the Age of AI: Leading with Design Carol Smith @carologic UXPA 2018, Puerto Rico June 25, 2018 Advanced Technologies Group
  2. 2. UX in the Age of AI: Leading with Design / @carologic Help humanity...
  3. 3. Why does UX work need to change?
  4. 4. Complex Unknowable Confusing Mysterious
  5. 5. UX in the Age of AI: Leading with Design / @carologic AI is Imperfect • Systems made and curated by humans who are: – Flawed. – Inconsistent. – Limited. – Biased. – Constantly changing. – Messy!
  6. 6. Engender Trust
  7. 7. UX in the Age of AI: Leading with Design / @carologic Data • Who made the data? • What is data’s provenance?
  8. 8. UX in the Age of AI: Leading with Design / @carologic Data creator: Writer • Creates scripts and stories for “robots.” • Determines how and when will be presented.
  9. 9. UX in the Age of AI: Leading with Design / @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. UX in the Age of AI: Leading with Design / @carologic System training and maintenance: Scientists • Triage system when there are issues. • Adjust programming and settings. • Review previous stories “Step into analysis”
  12. 12. UX in the Age of AI: Leading with Design / @carologic Why should we care? • What are their weaknesses’? – Not very creative. – Seemingly minimal exposure to the rest of the world.
  13. 13. Why is it doing that?
  14. 14. UX in the Age of AI: Leading with Design / @carologic Reveries (the small gestures hosts present)
  15. 15. UX in the Age of AI: Leading with Design / @carologic Why did they start doing that? • Ford/Arnold as programmer. • Westworld does an excellent job of explaining this.
  16. 16. UX in the Age of AI: Leading with Design / @carologic Why should we care? • No context - few mental models. • Help understand what is going on.
  17. 17. UX in the Age of AI: Leading with Design / @carologic Left reality quickly… Not a great example…
  18. 18. UX in the Age of AI: Leading with Design / @carologic To engender trust, provide transparency • Who made the data? • Who trained/programmed the system? • Why system providing data it is?
  19. 19. Why is UX different?
  20. 20. UX in the Age of AI: Leading with Design / @carologic AI is dynamic • Mature AI takes data and training - applies to new situations. • Attributions to new data may be: – Inaccurate – Weird – Inappropriate – Unintended
  21. 21. What is AI?
  22. 22. 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
  23. 23. UX in the Age of AI: Leading with Design / @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.
  24. 24. UX in the Age of AI: Leading with Design / @carologic Types of AI • Machine Learning – Supervised learning – Unsupervised learning – Reinforced learning • Deep Learning • Pattern Recognition – Natural Language Processing – Image Analysis Artificial Intelligence Demystified by. Rahul December 23, 2016. Analytics Vidhya https://www.analyticsvidhya.com/blog/2016/12/artificial-intelligence-demystified/
  25. 25. UX in the Age of AI: Leading with Design / @carologic Taxonomies and Ontologies Come to Life (NOT like humans learn) Photo: https://commons.wikimedia.org/wiki/File:Baby_Boy_Oliver.jpg
  26. 26. UX in the Age of AI: Leading with Design / @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.
  27. 27. UX in the Age of AI: Leading with Design / @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.).
  28. 28. UX in the Age of AI: Leading with Design / @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).
  29. 29. UX in the Age of AI: Leading with Design / @carologic This looks familiar? • Information Architecture! • Organizing huge amounts of information. • Requires deep understanding of the content.
  30. 30. UX in the Age of AI: Leading with Design / @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.
  31. 31. UX in the Age of AI: Leading with Design / @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.
  32. 32. UX in the Age of AI: Leading with Design / @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
  33. 33. Whose problem are we solving? What is the problem?
  34. 34. UX in the Age of AI: Leading with Design / @carologic Like Any Good UX • Understand problem deeply. • Build right AI system to solve problem. • Different problems require different systems.
  35. 35. UX in the Age of AI: Leading with Design / @carologic Who will use the system and why? • What are their goals? • What problems are they trying to solve? – Is this a problem a computer can solve? – Is AI the right solution? – What is out of scope? • Are they working independently or together? – How might they collaborate?
  36. 36. UX in the Age of AI: Leading with Design / @carologic How trusting are the users of AI? • Work to gain trust? • What might engender trust via the UI? • Add “fun” to show it is working?
  37. 37. UX in the Age of AI: Leading with Design / @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?
  38. 38. UX in the Age of AI: Leading with Design / @carologic Anticipated changes with AI system? • Not just throwing AI in (just because you can...) • What is intention? What kind of improvements? • What might a machine do better or faster? • What is not going to be improved (out of scope)?
  39. 39. UX in the Age of AI: Leading with Design / @carologic What are potential unintended consequences? • Understand user’s fears. • Those fears should be well known. • Figure out how to address fears. • Fears lead to potential unintended consequences. – Preparing for these will protect your users.
  40. 40. UX in the Age of AI: Leading with Design / @carologic AI Management Matches Org Ecosystem • Microcosm of organization – Not independent of organization – Same issues • Need similar support – Need people curating content – Watching for issues, etc. – Who is responsible for managing, training, and oversight?
  41. 41. Content
  42. 42. UX in the Age of AI: Leading with Design / @carologic Data Source • In existence? • Available? • High quantity? • High quality? Photo by sunlightfoundation https://www.flickr.com/photos/sunlightfoundation/2385174105
  43. 43. UX in the Age of AI: Leading with Design / @carologic Curation • Where content sourced from? – What bias does it already have? – How will it be organized? – What are potential unintended consequences? • Who is creating/curating collection? – Respected experts in the 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/
  44. 44. UX in the Age of AI: Leading with Design / @carologic All data is biased - what is bias? • Human condition - we all bring our personal experience and knowledge with us. • Affected by: – Social class, resource availability. – Race, Gender, Sexuality. – Culture, Theology, Tradition. – Other factors we aren’t even aware of.
  45. 45. “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
  46. 46. UX in the Age of AI: Leading with Design / @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).
  47. 47. UX in the Age of AI: Leading with Design / @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.
  48. 48. AI is only as good as data and time spent improving it. Biased based on what taught.
  49. 49. UX in the Age of AI: Leading with Design / @carologic Data created by those who write • History written by victors. • Lawn care specialists.
  50. 50. Training
  51. 51. UX in the Age of AI: Leading with Design / @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?
  52. 52. UX in the Age of AI: Leading with Design / @carologic Accuracy of system • How important is accuracy? • Consider a reverse card sorting exercise Image: Gerry Gaffney. (2000) What is Card Sorting? Usability Techniques Series, Information & Design. http://www.infodesign.com.au/usabilityresources/design/cardsorting.asp
  53. 53. UX in the Age of AI: Leading with Design / @carologic Across Industries – Priority of Accuracy Varies Higher Priority 90-99%+ Lower Priority 60-89% accuracy is acceptable Financial Ecommerce
  54. 54. Design AI Responsibly
  55. 55. UX in the Age of AI: Leading with Design / @carologic UX professionals must push… • Keep people at the center of our work. • User’s goals. • Ethics – For our users. – For humanity.
  56. 56. 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/
  57. 57. UX in the Age of AI: Leading with Design / @carologic Intentional Design • Keep people and data safe. • When unintended consequences arise, how do we deal with them?
  58. 58. UX in the Age of AI: Leading with Design / @carologic Make it your business to keep people safe • Make contingency plans. – Warning signs? – How do we deal with unintentional consequences? – What is the worst potential outcome?
  59. 59. UX in the Age of AI: Leading with Design / @carologic Make a Plan • No need to imagine every single situation • Focus on how you’ll react to worst situations: – What happens when it becomes a Nazi? – What happens when it does XYZ unexpectedly?
  60. 60. UX in the Age of AI: Leading with Design / @carologic What will you do? • What is the method for “turning it off”? – Who turns it off? How? – Who has access? • Who is notified immediately? • 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
  61. 61. UX in the Age of AI: Leading with Design / @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
  62. 62. Grady Booch, Scientist, philosopher, IBM’er https://www.ted.com/talks/grady_booch_don_t_fear_superintelligence UX Responsibility: Ensure that humans can unplug the machines!
  63. 63. Crazy, Evil? Reduce their power.
  64. 64. UX in the Age of AI: Leading with Design / @carologic PAPA Policy
  65. 65. UX in the Age of AI: Leading with Design / @carologic Indicators of ethics • Richard Mason - ethical discussion of security and controls • PAPA Policy – Privacy – Accuracy – Property – Accessibility (access to information, not #a11y) Ethical Issues in IS by Richard Mason’s H/T to Andrea Resmini https://www.gdrc.org/info-design/4-ethics.html
  66. 66. UX in the Age of AI: Leading with Design / @carologic Privacy • 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?
  67. 67. UX in the Age of AI: Leading with Design / @carologic Accuracy • What is good enough? • Set a bar (a goal) • Keep going until attain goal • More accurate, exponential effort
  68. 68. UX in the Age of AI: Leading with Design / @carologic Property • 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?
  69. 69. UX in the Age of AI: Leading with Design / @carologic Accessibility of Data • What do we have a right to access? • Barriers – Literacy and awareness. – Connection to internet – economics and location. – Access to pertinent data.
  70. 70. UX in the Age of AI: Leading with Design / @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 • Physical disabilities • Cognitive, learning, and neurological How People with Disabilities Use the Web: Overview https://www.w3.org/WAI/intro/people-use-web /
  71. 71. UX in the Age of AI: Leading with Design / @carologic Sharing 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. UX in the Age of AI: Leading with Design / @carologic Communication and responsibility • 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. UX in the Age of AI: Leading with Design / @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. UX in the Age of AI: Leading with Design / @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. UX in the Age of AI: Leading with Design / @carologic How do users know when something is wrong? • Show examples. – Bias has changed. – Accuracy has decreased. • Where do these examples live? • How can a user contest something and report it? – When should they expect a response?
  76. 76. UX in the Age of AI: Leading with Design / @carologic Confidence in Content • Ratings? A scale? • How is ‘ABC’ comparable to other entries? – “Top” entry”? – Potential new discovery in the data? – Outside of the AI’s normal purview? – Really just a guess? – No idea at all?
  77. 77. UX in the Age of AI: Leading with Design / @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. UX in the Age of AI: Leading with Design / @carologic UX Ethics for AI
  80. 80. UX in the Age of AI: Leading with Design / @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. UX in the Age of AI: Leading with Design / @carologic Help humanity...
  84. 84. UX in the Age of AI: Leading with Design / @carologic Creating Ethical AI • Less-biased content. • Transparency of data sources and training. • Intentional design: Build in safety. • Build practices around: – PAPA (Privacy, Accuracy, Property, Accessibility)
  85. 85. UX in the Age of AI: Leading with Design / @carologic Create a code of conduct/ethics • What do you value? • What lines won’t your AI cross? – What is too far? – What are you including? • How will you track your progress?
  86. 86. UX in the Age of AI: Leading with Design / @carologic Take Responsibility • Keep humans in control. • Hire people affected by bias – Non-typical schools, non-typical careers, etc. – POC, LGBTQ, women (52% of world). • Conduct auditing – Algorithmic, data, UI, etc. 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. UX in the Age of AI: Leading with Design / @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
  88. 88. UX in the Age of AI: Leading with Design / @carologic Teach others about AI • Demystify – use plain language. • Teach people how to utilize and benefit from the system. • Provide easy way to raise concerns (anonymous if appropriate).
  89. 89. UX in the Age of AI: Leading with Design / @carologic UX professionals must push… • Keep people at the center of our work. – Lead with our user’s goals. – Ease of use, usability, findability, effectiveness, efficiency… • Work to mature organizations approach – Push back on “technology first” ideas. – Lead on ethics - for our users, humanity.
  90. 90. UX in the Age of AI: Leading with Design / @carologic Don’t fear AI - Explore AI Try out tools (see Appendix and footer) Pair with others
  91. 91. UX in the Age of AI: Leading with Design / @carologic Contact Carol LinkedIn – CarolJSmith Twitter - @Carologic Slideshare – carologic SpeakerRate - CarolJSmith
  92. 92. UX in the Age of AI: Leading with Design / @carologic Uber is Hiring! https://www.uber.com/careers/ Advanced Technologies Group
  93. 93. UX in the Age of AI: Leading with Design / @carologic Additional Information and Resources
  94. 94. UX in the Age of AI: Leading with Design / @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
  95. 95. UX in the Age of AI: Leading with Design / @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/
  96. 96. UX in the Age of AI: Leading with Design / @carologic 10 Major Milestones in the History of AI https://www.analyticsvidhya.com/blog/2016/12/artificial-intelligence-demystified/
  97. 97. UX in the Age of AI: Leading with Design / @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
  98. 98. UX in the Age of AI: Leading with Design / @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
  99. 99. UX in the Age of AI: Leading with Design / @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/
  100. 100. UX in the Age of AI: Leading with Design / @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
  101. 101. UX in the Age of AI: Leading with Design / @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
  102. 102. UX in the Age of AI: Leading with Design / @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
  103. 103. UX in the Age of AI: Leading with Design / @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
  104. 104. UX in the Age of AI: Leading with Design / @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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