Carol Smith presented on AI and machine learning at the Midwest UX 2017 conference in Cincinnati, Ohio. She discussed how AI systems exhibit intelligence by perceiving their environment and taking actions to achieve goals defined by their human programmers. She provided examples of AI applications such as self-driving cars, image recognition in Google Photos, and analyzing medical images to assist radiologists. Smith emphasized that AI systems are only as good as the data and training provided by experts, and that humans remain in control of defining the goals and oversight of AI.
Artificial Intelligence Introduction & Business usecasesVikas Jain
Vikas Jain is a leading keynote speaker on artificial intelligence.
Develop AI Solution mindset to help business leaders & professionals from IT/non-IT Industry can use it to solve complex problems and grow their business.
Quantitative Ethics - Governance and ethics of AI decisionsNikita Lukianets
Presented as a part of the conference "Robots and Artificial Intelligence: The new force awakens" held in Nice, France in March 2018. This presentation provides framework and strategies to approach ethical aspects in the development of the AI of tomorrow.
The main topics discussed:
1) Data is the new electricity
2) Artificial intelligence and the decision making
3) Ethical frameworks for artificial intelligence
AI, Machine Learning & Deep Learning Risk Management & Controls: Beyond Deep Learning and Generative Adversarial Networks: Model Risk Management in AI, Machine Learning & Deep Learning
Business growth principles in the new economy Ashish Bedekar
A presentation I made @ Palava #Smartcity #startup accelerator on 19th April 2018. http://bit.ly/2qz5Xr2
#1 About me:
You may like to check out https://ashishbedekar.fyi.to/ecosystems which gives an overview of my profile, including LinkedIn recommendations
#2: Connect with me http://www.linkedin.com/in/ashishrbedekar | Twitter: @ashishrbedekar
#3 I believe in giving back to the community e.g
- Pro-bono startup advisor @Zone startup- an Indo-Canadian start-up Accelerator http://bit.ly/2o9XNqa
- Pro-bono startup advisor@ Supercharger Fintech accelerator ( KL, HK) http://bit.ly/2ErcM6S
- Pro-bono startup advisor@ NIT Trichy- International biz competition- http://bit.ly/2AQxqYu
-Mentor of change- Govt. of India- Atal innovation mission http://bit.ly/2HMdWrV
-Member of IET- IoT India (The IET is one of the world's largest multi-discipline professional societies of engineers with more than 160,000 members in 127 countries) http://bit.ly/2o9Pue9
-Mentor for startup boot camp-E- Cell- IIT Madras- one of India’s leading engineering college- http://bit.ly/2G6nRMg
Artificial Intelligence Introduction & Business usecasesVikas Jain
Vikas Jain is a leading keynote speaker on artificial intelligence.
Develop AI Solution mindset to help business leaders & professionals from IT/non-IT Industry can use it to solve complex problems and grow their business.
Quantitative Ethics - Governance and ethics of AI decisionsNikita Lukianets
Presented as a part of the conference "Robots and Artificial Intelligence: The new force awakens" held in Nice, France in March 2018. This presentation provides framework and strategies to approach ethical aspects in the development of the AI of tomorrow.
The main topics discussed:
1) Data is the new electricity
2) Artificial intelligence and the decision making
3) Ethical frameworks for artificial intelligence
AI, Machine Learning & Deep Learning Risk Management & Controls: Beyond Deep Learning and Generative Adversarial Networks: Model Risk Management in AI, Machine Learning & Deep Learning
Business growth principles in the new economy Ashish Bedekar
A presentation I made @ Palava #Smartcity #startup accelerator on 19th April 2018. http://bit.ly/2qz5Xr2
#1 About me:
You may like to check out https://ashishbedekar.fyi.to/ecosystems which gives an overview of my profile, including LinkedIn recommendations
#2: Connect with me http://www.linkedin.com/in/ashishrbedekar | Twitter: @ashishrbedekar
#3 I believe in giving back to the community e.g
- Pro-bono startup advisor @Zone startup- an Indo-Canadian start-up Accelerator http://bit.ly/2o9XNqa
- Pro-bono startup advisor@ Supercharger Fintech accelerator ( KL, HK) http://bit.ly/2ErcM6S
- Pro-bono startup advisor@ NIT Trichy- International biz competition- http://bit.ly/2AQxqYu
-Mentor of change- Govt. of India- Atal innovation mission http://bit.ly/2HMdWrV
-Member of IET- IoT India (The IET is one of the world's largest multi-discipline professional societies of engineers with more than 160,000 members in 127 countries) http://bit.ly/2o9Pue9
-Mentor for startup boot camp-E- Cell- IIT Madras- one of India’s leading engineering college- http://bit.ly/2G6nRMg
Novi Sad AI is the first AI community in Serbia with goal of democratizing knowledge of AI. On our first event we talked about Belief networks, Deep learning and many more.
Machine Learning for Non-Technical People - Turing Fest 2019Britney Muller
Machine Learning/AI is becoming more and more accessible and will free you up to work on higher level thinking.
ANYONE can come up with the next big ML/AI application.
What will you solve?
From Biology to Industry. A Blogger’s Journey to Data Science.Shirin Elsinghorst
What does blogging mean for Data Sciences?
What is Big Data today?
How to become a Data Scientist and what type of work results from this transformation?
Australian Legal Education in 2017: Taking Stock for an Uncertain FutureSally Kift
This presentation was made to The Future of Legal Education Workshop hosted by Griffith University's Law Futures Centre on 1 November 2017. It suggests that Australian legal education research over the last decade has positioned us well for an uncertain future. While our Law Schools cannot afford to be complacent, especially given the increasing automation of legal work and the unbundling of legal services, the strong research and evidence base to which Australian legal educators may refer provides a degree of optimism for an uncertain future. Critically, this must be a joint endeavour that engages all branches of the legal profession and the Academy working together. Students and young lawyers in particular have a vital role to play in shaping the future of their professional education. In the absence of an #OLTphoenix, Australian legal education is well-placed to be self-sustaining and self-generating.
The abstract for the session was as follows:
In 2017, Australian legal education finds itself at a crossroads. In common with its disciplinary brethren, it is being impacted by the multitude challenges and volatile policy environment facing the Australian higher education sector more broadly. As for the rest of the Academy also, Law Schools are being squeezed on numerous fronts in their quest to fund pedagogical innovation. In the meantime, law students, who continue to bear a disproportionately high percentage of their degree costs, find themselves entering an extremely competitive job market with reduced employment opportunities. And of potentially even greater import, the disruptive innovation being felt in universities is also now impacting the legal services industry itself, so much so that the halcyon days of Priestley’s dead hand (or light hand, depending on your perspective) finally look to be drawing to a close.
This presentation will review Australian legal education’s pedagogical progress over the last decade through a scholarship lens and ask how is legal education positioned in 2017 for an uncertain future? In the absence of a national body such as the Office for Learning and Teaching (OLT), which was de-funded in mid-2016, is Australian legal education research and scholarship sufficiently mature to be self-sustaining and self-generating? At the risk of being overly optimistic, it will be suggested that, in an era of stackable credentials, the quality of Australian legal education generally ranks amongst the best in the world and is well-positioned to prepare its students to take their place, personally and professionally, as global citizens in complex and dynamic legal and other workplaces.
UX in the Age of AI: Where Does Design Fit In? Fluxible 2017Carol Smith
Cognitive computing and machine learning are not new concepts, but they are new to most UX’ers. Carol Smith addresses questions about artificial intelligence (AI) such as:
- What are these terms and technologies and how do they work?
- How can we take advantage of these powerful systems to help our users?
- Should I be concerned that computers will take over the world soon? Spoiler: It is extremely unlikely.
Once this baseline understanding is established, we’ll look at examples of AI in use and discuss the relevancy of design work in the age of AI. Additionally, we’ll explore the ethical challenges inherent with the use of AI from the user’s perspective, specifically regarding trust and transparency.
This was presented at Fluxible 2017 in Kitchener-Waterloo, Ontario, Canada on 23 Sept 2017.
Designing AI for Humanity at dmi:Design Leadership Conference in BostonCarol Smith
As design leaders we must enable our teams with skills and knowledge to take on the new and exciting opportunities that building powerful AI systems bring. Dynamic systems require transparency regarding data provenance, bias, training methods, and more, to gain user’s trust. Carol will cover these topics and challenge us as design leaders, to represent our fellow humans by provoking conversations regarding critical ethical and safety needs.
Presented at dmi:Design Leadership Conference in Boston in October 2018.
IA in the Age of AI: Embracing Abstraction and Change at IA Summit 2018Carol Smith
This session focuses on the questions we need to ask to create good, ethical experiences for our users.
Information Architects must push to…
- Keep people at the center of our work.
- Lead with our user’s goals.
- Ease of use, usability, findability, effectiveness, efficiency…
We must work to mature organizations approach
- Push back on “technology first” ideas.
- Lead on ethics - for our users, humanity.
UX in the Age of AI: Leading with Design UXPA2018Carol Smith
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.
AI for IA's: Machine Learning Demystified at IA Summit 2017 - IAS17Carol Smith
What is machine learning? Is IA relevant in the age of AI? How can I take advantage of cognitive computing? Learn the basics of these concepts and the implications for your work in this presentation. Carol Smith provides examples of machine learning use and will discuss the challenges inherent in in AI.
Data Science for Beginner by Chetan Khatri and Deptt. of Computer Science, Ka...Chetan Khatri
What is Data Science?
What is Machine Learning, Deep Learning, and AI?
Motivation
Philosophy of Artificial Intelligence (AI)
Role of AI in Daily life
Use cases/Applications
Tools & Technologies
Challenges: Bias, Fake Content, Digital Psychography, Security
Detect Fake Content with “AI”
Learning Path
Career Path
Designing Trustable AI Experiences at World Usability Day in ClevelandCarol Smith
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 distinguish between simply smart systems and AI.
Presented at the World World Usability Day 2018 celebration in Cleveland, Ohio.
Dynamic UXR: Ethical Responsibilities and AI. Carol Smith at Strive in TorontoCarol Smith
Artificially intelligent (AI) technologies are exciting and with them come a lot of new user experience research (UXR) responsibilities. How do we understand and clarify our users need for transparency, control, and access (and more) when the system is constantly changing?
These dynamic systems are already part of our everyday lives and quickly becoming part of our jobs. What are our responsibilities with regard to ethics and protecting users from bias?
Presented at Strive, June 7, 2019 in Toronto, Ontario, Canada. Strive is the 2019 UX Research Conference presented by the UX Research Collective Inc.
Tallk given at #SXSW2019 in the Intelligent Future track as part of the Interactive Festival. We explain 3 frameworks for MachineEthics and how they affect the supervised and unsupervised methods, and the data engineering discipline.
Working with data is a challenge for many organizations. Nonprofits in particular may need to collect and analyze sensitive, incomplete, and/or biased historical data about people. In this talk, Dr. Cori Faklaris of UNC Charlotte provides an overview of current AI capabilities and weaknesses to consider when integrating current AI technologies into the data workflow. The talk is organized around three takeaways: (1) For better or sometimes worse, AI provides you with “infinite interns.” (2) Give people permission & guardrails to learn what works with these “interns” and what doesn’t. (3) Create a roadmap for adding in more AI to assist nonprofit work, along with strategies for bias mitigation.
Novi Sad AI is the first AI community in Serbia with goal of democratizing knowledge of AI. On our first event we talked about Belief networks, Deep learning and many more.
Machine Learning for Non-Technical People - Turing Fest 2019Britney Muller
Machine Learning/AI is becoming more and more accessible and will free you up to work on higher level thinking.
ANYONE can come up with the next big ML/AI application.
What will you solve?
From Biology to Industry. A Blogger’s Journey to Data Science.Shirin Elsinghorst
What does blogging mean for Data Sciences?
What is Big Data today?
How to become a Data Scientist and what type of work results from this transformation?
Australian Legal Education in 2017: Taking Stock for an Uncertain FutureSally Kift
This presentation was made to The Future of Legal Education Workshop hosted by Griffith University's Law Futures Centre on 1 November 2017. It suggests that Australian legal education research over the last decade has positioned us well for an uncertain future. While our Law Schools cannot afford to be complacent, especially given the increasing automation of legal work and the unbundling of legal services, the strong research and evidence base to which Australian legal educators may refer provides a degree of optimism for an uncertain future. Critically, this must be a joint endeavour that engages all branches of the legal profession and the Academy working together. Students and young lawyers in particular have a vital role to play in shaping the future of their professional education. In the absence of an #OLTphoenix, Australian legal education is well-placed to be self-sustaining and self-generating.
The abstract for the session was as follows:
In 2017, Australian legal education finds itself at a crossroads. In common with its disciplinary brethren, it is being impacted by the multitude challenges and volatile policy environment facing the Australian higher education sector more broadly. As for the rest of the Academy also, Law Schools are being squeezed on numerous fronts in their quest to fund pedagogical innovation. In the meantime, law students, who continue to bear a disproportionately high percentage of their degree costs, find themselves entering an extremely competitive job market with reduced employment opportunities. And of potentially even greater import, the disruptive innovation being felt in universities is also now impacting the legal services industry itself, so much so that the halcyon days of Priestley’s dead hand (or light hand, depending on your perspective) finally look to be drawing to a close.
This presentation will review Australian legal education’s pedagogical progress over the last decade through a scholarship lens and ask how is legal education positioned in 2017 for an uncertain future? In the absence of a national body such as the Office for Learning and Teaching (OLT), which was de-funded in mid-2016, is Australian legal education research and scholarship sufficiently mature to be self-sustaining and self-generating? At the risk of being overly optimistic, it will be suggested that, in an era of stackable credentials, the quality of Australian legal education generally ranks amongst the best in the world and is well-positioned to prepare its students to take their place, personally and professionally, as global citizens in complex and dynamic legal and other workplaces.
UX in the Age of AI: Where Does Design Fit In? Fluxible 2017Carol Smith
Cognitive computing and machine learning are not new concepts, but they are new to most UX’ers. Carol Smith addresses questions about artificial intelligence (AI) such as:
- What are these terms and technologies and how do they work?
- How can we take advantage of these powerful systems to help our users?
- Should I be concerned that computers will take over the world soon? Spoiler: It is extremely unlikely.
Once this baseline understanding is established, we’ll look at examples of AI in use and discuss the relevancy of design work in the age of AI. Additionally, we’ll explore the ethical challenges inherent with the use of AI from the user’s perspective, specifically regarding trust and transparency.
This was presented at Fluxible 2017 in Kitchener-Waterloo, Ontario, Canada on 23 Sept 2017.
Designing AI for Humanity at dmi:Design Leadership Conference in BostonCarol Smith
As design leaders we must enable our teams with skills and knowledge to take on the new and exciting opportunities that building powerful AI systems bring. Dynamic systems require transparency regarding data provenance, bias, training methods, and more, to gain user’s trust. Carol will cover these topics and challenge us as design leaders, to represent our fellow humans by provoking conversations regarding critical ethical and safety needs.
Presented at dmi:Design Leadership Conference in Boston in October 2018.
IA in the Age of AI: Embracing Abstraction and Change at IA Summit 2018Carol Smith
This session focuses on the questions we need to ask to create good, ethical experiences for our users.
Information Architects must push to…
- Keep people at the center of our work.
- Lead with our user’s goals.
- Ease of use, usability, findability, effectiveness, efficiency…
We must work to mature organizations approach
- Push back on “technology first” ideas.
- Lead on ethics - for our users, humanity.
UX in the Age of AI: Leading with Design UXPA2018Carol Smith
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.
AI for IA's: Machine Learning Demystified at IA Summit 2017 - IAS17Carol Smith
What is machine learning? Is IA relevant in the age of AI? How can I take advantage of cognitive computing? Learn the basics of these concepts and the implications for your work in this presentation. Carol Smith provides examples of machine learning use and will discuss the challenges inherent in in AI.
Data Science for Beginner by Chetan Khatri and Deptt. of Computer Science, Ka...Chetan Khatri
What is Data Science?
What is Machine Learning, Deep Learning, and AI?
Motivation
Philosophy of Artificial Intelligence (AI)
Role of AI in Daily life
Use cases/Applications
Tools & Technologies
Challenges: Bias, Fake Content, Digital Psychography, Security
Detect Fake Content with “AI”
Learning Path
Career Path
Designing Trustable AI Experiences at World Usability Day in ClevelandCarol Smith
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 distinguish between simply smart systems and AI.
Presented at the World World Usability Day 2018 celebration in Cleveland, Ohio.
Dynamic UXR: Ethical Responsibilities and AI. Carol Smith at Strive in TorontoCarol Smith
Artificially intelligent (AI) technologies are exciting and with them come a lot of new user experience research (UXR) responsibilities. How do we understand and clarify our users need for transparency, control, and access (and more) when the system is constantly changing?
These dynamic systems are already part of our everyday lives and quickly becoming part of our jobs. What are our responsibilities with regard to ethics and protecting users from bias?
Presented at Strive, June 7, 2019 in Toronto, Ontario, Canada. Strive is the 2019 UX Research Conference presented by the UX Research Collective Inc.
Tallk given at #SXSW2019 in the Intelligent Future track as part of the Interactive Festival. We explain 3 frameworks for MachineEthics and how they affect the supervised and unsupervised methods, and the data engineering discipline.
Working with data is a challenge for many organizations. Nonprofits in particular may need to collect and analyze sensitive, incomplete, and/or biased historical data about people. In this talk, Dr. Cori Faklaris of UNC Charlotte provides an overview of current AI capabilities and weaknesses to consider when integrating current AI technologies into the data workflow. The talk is organized around three takeaways: (1) For better or sometimes worse, AI provides you with “infinite interns.” (2) Give people permission & guardrails to learn what works with these “interns” and what doesn’t. (3) Create a roadmap for adding in more AI to assist nonprofit work, along with strategies for bias mitigation.
On March 26, 2015 Steve Omohundro gave a talk in the IBM Research 2015 Distinguished Speaker Series at the Accelerated Discovery Lab, IBM Research, Almaden.
Google, IBM, Microsoft, Apple, Facebook, Baidu, Foxconn, and others have recently made multi-billion dollar investments in artificial intelligence and robotics. Some of these investments are aimed at increasing productivity and enhancing coordination and cooperation. Others are aimed at creating strategic gains in competitive interactions. This is creating “arms races” in high-frequency trading, cyber warfare, drone warfare, stealth technology, surveillance systems, and missile warfare. Recently, Stephen Hawking, Elon Musk, and others have issued strong cautionary statements about the safety of intelligent technologies. We describe the potentially antisocial “rational drives” of self-preservation, resource acquisition, replication, and self-improvement that uncontrolled autonomous systems naturally exhibit. We describe the “Safe-AI Scaffolding Strategy” for developing these systems with a high confidence of safety based on the insight that even superintelligences are constrained by the laws of physics, mathematical proof, and cryptographic complexity. “Smart contracts” are a promising decentralized cryptographic technology used in Ethereum and other second-generation cryptocurrencies. They can express economic, legal, and political rules and will be a key component in governing autonomous technologies. If we are able to meet the challenges, AI and robotics have the potential to dramatically improve every aspect of human life.
UX for Artificial Intelligence / UXcamp Europe '17 / Berlin / Jan KorsankeJan Korsanke
/ My talk from the UXcamp Europe in Berlin. Please enjoy and feel free and don't hesitate to contact me if you have questions or want to talk about UX and AI
What is artificial intelligence, how do we create collaboration and what’s gonna happen in the future?
This tutorial offers a step-by-step guide on how to effectively use Pinterest. It covers the basics such as account creation and navigation, as well as advanced techniques including creating eye-catching pins and optimizing your profile. The tutorial also explores collaboration and networking on the platform. With visual illustrations and clear instructions, this tutorial will equip you with the skills to navigate Pinterest confidently and achieve your goals.
The cherry: beauty, softness, its heart-shaped plastic has inspired artists since Antiquity. Cherries and strawberries were considered the fruits of paradise and thus represented the souls of men.
Hadj Ounis's most notable work is his sculpture titled "Metamorphosis." This piece showcases Ounis's mastery of form and texture, as he seamlessly combines metal and wood to create a dynamic and visually striking composition. The juxtaposition of the two materials creates a sense of tension and harmony, inviting viewers to contemplate the relationship between nature and industry.
Brushstrokes of Inspiration: Four Major Influences in Victor Gilbert’s Artist...KendraJohnson54
Throughout his career, Victor Gilbert was influenced heavily by various factors, the most notable being his upbringing and the artistic movements of his time. A rich tapestry of inspirations appears in Gilbert’s work, ranging from their own experiences to the art movements of that period.
Heart Touching Romantic Love Shayari In English with ImagesShort Good Quotes
Explore our beautiful collection of Romantic Love Shayari in English to express your love. These heartfelt shayaris are perfect for sharing with your loved one. Get the best words to show your love and care.
Fashionista Chic Couture Maze & Coloring Adventures is a coloring and activity book filled with many maze games and coloring activities designed to delight and engage young fashion enthusiasts. Each page offers a unique blend of fashion-themed mazes and stylish illustrations to color, inspiring creativity and problem-solving skills in children.
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
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
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: https://www.slideshare.net/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