AI and ML Demystified / AI and Machine Learning Demystified by Carol Smith at Midwest UX 2017

AI and ML Demystified /

1. AI and Machine Learning Demystified Carol Smith @carologic Midwest UX 2017, Cincinnati, Ohio October 13, 2017
2. AI is when Machines – Exhibit intelligence – Perceive their environment – Take actions/make decision to maximize chance of success at a goal NAO’s New Job as “Connie” the concierge at Hilton Hotels https://developer.softbankrobotics.com/us-en/showcase/nao-ibm-create-new-hilton-concierge
3. AI and ML Demystified / @carologic / MWUX2017 In the extreme… Google Search for “movies with AI” Copyrights as labeled.
4. “Most people working in AI have a healthy skepticism for the idea of the singularity. We know how hard it is to get even a little intelligence into a machine, let alone enough to achieve recursive self- improvement.” – Toby Walsh http://www.wired.co.uk/article/elon-musk- artificial-intelligence-scaremongering
5. Remember: “We can unplug the machines!” Grady Booch, Scientist, philosopher, IBM’er https://www.ted.com/talks/grady_booch_don_t_fear_superintelligence
6. AI and ML Demystified / @carologic / MWUX2017 Cognitive computers are • Made with algorithms • Knowledgeable ONLY about what taught • Control ONLY what we give them control of • Aware of nuances and can continue to learn more
7. AI and ML Demystified / @carologic / MWUX2017 Cognitive computers (algorithms) can… • Do very boring work for you • Often make better, more consistent decisions than humans • Be efficient, won’t get tired 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
8. AI and ML Demystified / @carologic / MWUX2017 Exhibit intelligence - transfer human concepts and relationships Photo by sunlightfoundation https://www.flickr.com/photos/sunlightfoundation/2385174105
9. AI and ML Demystified / @carologic / MWUX2017 Dependent on Experts • Subject Matter Experts (SME’s) Availability – Lawyers – Machinists – Insurance adjusters – Physicians • Usually not experienced in machine learning – Need close collaboration with those making algorithms
10. AI and ML Demystified / @carologic / MWUX2017 Number Five “Needs Input” Short Circuit (1986 film) - Ally Sheedy and Number Five https://en.wikipedia.org/wiki/Short_Circuit_(1986_film)
11. AI and ML Demystified / @carologic / MWUX2017 Content is annotated by experts Image created by Angela Swindell, Visual Designer, Watson Knowledge Studio
12. AI and ML Demystified / @carologic / MWUX2017 AI is taxonomies and ontologies coming to life (NOT like humans learn) Photo: https://commons.wikimedia.org/wiki/File:Baby_Boy_Oliver.jpg
13. Enormous amount of work.
14. Only as good as data and time spent improving it Biased based on what it taught
15. AI and ML Demystified / @carologic / MWUX2017 Creating an AI requires • Algorithms • Documents • Ground truth (annotation) • Teaching • Iteration • Repeat
16. AI and ML Demystified / @carologic / MWUX2017 Supervised (by a human) Machine Learning Watson Knowledge Studio https://www.ibm.com/us-en/marketplace/supervised-machine-learning
17. AI and ML Demystified / @carologic / MWUX2017 Knowledge and Accuracy • 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
18. AI and ML Demystified / @carologic / MWUX2017 Across industries – priority of accuracy varies Higher Priority 90-99%+ Lower Priority 60-89% accuracy is acceptable
19. AI and ML Demystified / @carologic / MWUX2017 Goal is saving time Machine learning creates more highly trained specialists Not an “all knowing” being
20. AI and ML Demystified / @carologic / MWUX2017 Cancer Burden in Sub-Saharan Africa Risk of getting cancer and Risk of Dying ~same The Cancer Atlas http://canceratlas.cancer.org/the-burden/
21. AI and ML Demystified / @carologic / MWUX2017 What if we could reduce the burden? • Bring taxonomies and ontologies to life • Broaden access to evidence based medicine • More informed treatment decisions
22. AI and ML Demystified / @carologic / MWUX2017 AI actions for success • Example: Healthcare – AI analyzes data (treatment options, similar patients) – Goal: Provide quick, evidence based options – Physician selects treatment for patients based on situation • AI success is helping physician (not replacing)
23. AI and ML Demystified / @carologic / MWUX2017 Examples of AI and Cognitive Computing
24. AI and ML Demystified / @carologic / MWUX2017 Consider for each example • What intelligence does the system need? • What is the AI perceiving in their environment? • What actions are taken to maximize chance of success at goal?
25. AI and ML Demystified / @carologic / MWUX2017 Strategic Games • 1997 Chess, IBM • 2016 Go, Google • Intelligence? • Perception? • Action/Decision? Floor goban, 2007, By Goban1 https://commons.wikimedia.org/wiki/File:FloorGoban.JPG
26. AI and ML Demystified / @carologic / MWUX2017 Understanding human speech • Watson developed for quiz show Jeopardy! • Won against champions in 2011 for $1 million Video: “IBM's Watson Supercomputer Destroys Humans in Jeopardy! Engadget” https://www.youtube.com/watch?v=WFR3lOm_xhE Watson definition: https://en.wikipedia.org/wiki/Watson_(computer)
27. AI and ML Demystified / @carologic / MWUX2017 Decision Making: Self Driving (autonomous) vehicles Junior, a robotic Volkswagen Passat, in a parking lot at Stanford University 24 October 2009, By: Steve Jurvetson https://en.wikipedia.org/wiki/File:Hands-free_Driving.jpg
28. AI and ML Demystified / @carologic / MWUX2017 Image Recognition – Google Photos Carol’s search for “cats” on her Google Photos account.
29. AI and ML Demystified / @carologic / MWUX2017 Sound recognition: Labeling of birdsongs “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 Photo by Gallo71 (Own work) [Public domain], via Wikimedia Commons https://commons.wikimedia.org/wiki/File%3ARbruni.JPG
30. AI and ML Demystified / @carologic / MWUX2017 Analyzing Text: Personality of @carologic (not quite) Personality Insights applied to @Carologic on Twitter IBM Watson Developer Cloud: https://personality-insights-livedemo.mybluemix.net/
31. AI and ML Demystified / @carologic / MWUX2017 Automating Repetitive Work • Automated Radiologist highlights possible issues • Radiologist confirms IBM’s Automated Radiologist Can Read Images and Medical Records, MIT Technology Review https://www.technologyreview.com/s/600706/ibms-automated-radiologist-can-read-images-and-medical-records/
32. AI and ML Demystified / @carologic / MWUX2017 88,000 retina images • Watson knows what a healthy eye looks like • Glaucoma is the second leading cause of blindness worldwide –50% of cases go undetected Seeing is preventing. https://twitter.com/IBMWatson/status/844545761740292096
33. AI and ML Demystified / @carologic / MWUX2017 Chatbots for Easy ordering • Order via text, email, Facebook Messenger or with a Slackbot • Cognitive pieces: –Speech-to-text –Chat –API’s in backend Story: http://www.businesswire.com/news/home/20161025006273/en/Staples%E2%80%99-%E2%80%9CEasy- Button%E2%80%9D-Life-IBM-Watson Photo: Easy Button from Staples: http://www.staples.com/Staples-Easy-Button/product_606396
34. AI and ML Demystified / @carologic / MWUX2017 Chatbots – not really AI, yet • Mapping Q & A –Expected language –Appropriate automated responses –When to escalate to a human Images: https://www.pexels.com/photo/close-up-of-mobile-phone-248512/ https://www.amazon.com/Amazon-Echo-Bluetooth-Speaker-with-WiFi-Alexa/dp/B00X4WHP5E https://www.ibm.com/watson/developercloud/doc/conversation/index.html
35. AI and ML Demystified / @carologic / MWUX2017 Optical character recognition (OCR) • Used to be AI • Now considered routine computing Portable scanner and OCR (video) https://en.wikipedia.org/wiki/File:Portable_scanner_and_OCR_(video).webm
36. AI and ML Demystified / @carologic / MWUX2017 Ethics in Design for AI
37. Humans teach what we feel is important… teach them to share our values. Super knowing - not super doing Grady Booch, Scientist, philosopher, IBM’er https://www.ted.com/talks/grady_booch_don_t_fear_superintelligence
38. AI and ML Demystified / @carologic / MWUX2017 How might we… • build systems that have ethical and moral foundation?’ • that are transparent to users? • teach mercy and justice of law? • extend and advance healthcare? • increase safety in dangerous work? Inspired by Grady Booch, Scientist, philosopher, IBM’er https://www.ted.com/talks/grady_booch_don_t_fear_superintelligence
39. Trust machines just as much as a well-trained human?
40. AI and ML Demystified / @carologic / MWUX2017 Guiding Principles – Ethical AI • Purpose – Aid humans, not replace them – Symbiotic relationship “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/
41. AI and ML Demystified / @carologic / MWUX2017 Transparency • How was AI taught? • What data was used? • Humans remain in control of the system “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/
42. AI and ML Demystified / @carologic / MWUX2017 Skills • Built with people in the industry • Human workers trained how to use tools to their advantage “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/
43. AI and ML Demystified / @carologic / MWUX2017 Regulations • Almost everyone agrees they are necessary • Who will create regulations? • Enforce?
44. “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
45. AI and ML Demystified / @carologic / MWUX2017 The EU General Data Protection Regulation (GDPR) • Framework for transparency rights and safeguards against automated decision-making • Right to contest a completely automated decision if it has legal or other significant effects on them 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. AI and ML Demystified / @carologic / MWUX2017 Regulations take forever • Humans and algorithms aren’t without bias • ML has potential to make less biased decisions • Algorithms trained with biased data pick up and replicate biases, and develop new ones 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
47. AI and ML Demystified / @carologic / MWUX2017 How do we evolve the practice of UX to deal with the new issues these technologies bring and the new information that is created?
48. AI and ML Demystified / @carologic / MWUX2017 Take Responsibility • Create a code of conduct – What do you value? – What lines won’t your AI cross? • Make your AI transparent – How was it made and what does it do? – How do you reduce bias? • Keep humans in control
49. AI and ML Demystified / @carologic / MWUX2017 Don’t fear AI - Explore AI Try the tools Pair with others IBM Watson Developer Tools (free trials): https://console.ng.bluemix.net/catalog/?category=watson
50. AI and ML Demystified / @carologic / MWUX2017 Go forth and create ethical AI’s • Purpose: Intelligence and actions to maximize success • Transparency: Code of Conduct • Skills: How will humans learn to use it?
51. AI and ML Demystified / @carologic / MWUX2017 Contact Carol LinkedIn: https://www.linkedin.com/in/caroljsmith Twitter - @Carologic: https://twitter.com/carologic Slides on Slideshare: http://smatoku.info/carologic
52. AI and ML Demystified / @carologic / MWUX2017 Additional Information and Resources
53. AI and ML Demystified / @carologic / MWUX2017 Watson is a cognitive technology that can think like a human. • Understand • Analyze and interpret all kinds of data • Unstructured text, images, audio and video • Reason • Understand the personality, tone, and emotion of content • Learn • Grow the subject matter expertise in your apps and systems • Interact • Create chat bots that can engage in dialog https://www.ibm.com/watson/
54. AI and ML Demystified / @carologic / MWUX2017 More on Strategic Games Graphic, Science Magazine: http://www.sciencemag.org/news/2016/03/update-why-week-s- man-versus-machine-go-match-doesn-t-matter-and-what-does
55. AI and ML Demystified / @carologic / MWUX2017 The Job Question • Make new economies and opportunities – potentially: –Create jobs –Entire new fields • Some jobs will be lost –What can we do to mitigate this? Jobs that no longer exist The Lector http://www.ranker.com/list/jobs-that-no-longer-exist/coy-jandreau
56. AI and ML Demystified / @carologic / MWUX2017 Tone Analyzer - Watson IBM Watson Developer Cloud, Tone Analyzer https://tone-analyzer-demo.mybluemix.net/
57. AI and ML Demystified / @carologic / MWUX2017 Optimist’s guide to the robot apocalypse - @sarahfkessler “The optimist’s guide to the robot apocalypse” by Sarah Kessler. March 09, 2017. QZ. @sarahfkessler https://qz.com/904285/the-optimists-guide-to-the-robot-apocalypse/
58. AI and ML Demystified / @carologic / MWUX2017 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
59. AI and ML Demystified / @carologic / MWUX2017 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
60. AI and ML Demystified / @carologic / MWUX2017 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/
61. AI and ML Demystified / @carologic / MWUX2017 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
62. AI and ML Demystified / @carologic / MWUX2017 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
63. AI and ML Demystified / @carologic / MWUX2017 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
64. AI and ML Demystified / @carologic / MWUX2017 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
65. AI and ML Demystified / @carologic / MWUX2017 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