Machine learning for designers masterclass
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Presentation at BBVA Open Summit 2017 in Madrid. Co-authored by Spring Studio and BBVA Data Analytics
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Machine learning for designers masterclass
- 1. #MachineLearningForDesigners Machine Learning for Designers Masterclass Presented by
- 2. #MachineLearningForDesigners A Practical Definition of ML Become buzzword compliant Interaction Intelligence Start your engines The (Near) Future of Design Futureproof your UX skills Part I Part II Part III
- 3. #MachineLearningForDesigners Part I A Practical Definition of Machine Learning What it is (and isn’t) How it works Part I | A Practical Definition of Machine Learning
- 4. #MachineLearningForDesigners Artificial Intelligence Any showing of “intelligence” made by a computer could be called AI. "AI is whatever hasn't been done yet." – Douglas Hofstadter The scope of AI is disputed: as machines become increasingly capable the definition of "intelligence" changes, a phenomenon known as the AI effect. Part I | A Practical Definition of Machine Learning
- 5. #MachineLearningForDesigners Computer adversarySearch Weather forecasting Autonomous carsSupply chain logistics Spam detection Intelligence can be... Part I | A Practical Definition of Machine Learning
- 6. #MachineLearningForDesigners Artificial Intelligence Machine Learning Speech Recognition Natural Language Understanding Computer Vision Generative Algorithms Artificial Intelligence & Machine Learning Part I | A Practical Definition of Machine Learning
- 7. #MachineLearningForDesigners It’s a cat if it has whiskers and it is furry What is a cat? Part I | A Practical Definition of Machine Learning
- 8. #MachineLearningForDesigners It’s a cat if it has whiskers and it is furry and it is small What is a cat? version 2 Part I | A Practical Definition of Machine Learning
- 9. #MachineLearningForDesigners It’s a cat if it has whiskers and it is furry and it is small and doesn’t climb trees What is a cat? version 3 Part I | A Practical Definition of Machine Learning
- 10. #MachineLearningForDesigners Is this a cat? Rules to detect a cat: 1. It has whiskers 2. It is furry 3. It is small Traditional programming... Machine learning Let me guess how i can distinguish a cat :) Machine Learning Part I | A Practical Definition of Machine Learning
- 11. #MachineLearningForDesigners Part II | Intelligent Interactions Part II Intelligent Interactions Making sense of data Designing for the machine New fundamentals Transparency and control
- 12. #MachineLearningForDesigners UnstructuredStructured ● It fits better in a table ● Closed-option variables. No free-text data ● It’s “less human” ● Easier for machines ● Organic, no predefined format ● Freeform text fields (like reviews) ● It’s “more human” ● Harder for machines Structured vs unstructured data Part II | Intelligent Interactions: Making sense of data
- 13. #MachineLearningForDesigners Machines “prefer” structured data. It’s easier to use and you can create better experiences with it: Unstructured vs structured data: problem Part II | Intelligent Interactions: Making sense of data Typically text, images and videos.
- 14. #MachineLearningForDesigners Natural Language Understanding (language) Computer vision (images, videos) Speech recognition (speakings) ... Unstructured vs structured data: solution Part II | Intelligent Interactions: Making sense of data Work with unstructured data
- 15. #MachineLearningForDesigners Feedback Loops User “Oh! You don’t like this song” Recommend different style song Recommend similar song Listen to song Like song Dislike song Skip song Recommend similar song Listen to similar song Machine Recommend different song Part II | Intelligent Interactions: Design for the machine
- 16. #MachineLearningForDesigners What I say I like vs What I actually watch Implicit vs Explicit Feedback Part II | Intelligent Interactions: Design for the machine
- 17. #MachineLearningForDesigners Clever way to train the machine Part II | Intelligent Interactions: Design for the machine
- 18. #MachineLearningForDesigners Clever way to train the machine Part II | Intelligent Interactions: Design for the machine
- 19. #MachineLearningForDesigners • What data does my algorithm need to do its job? • How do users provide feedback? • Why would users want to give it feedback? • What failures might I anticipate? New Considerations
- 20. #MachineLearningForDesigners Classification Regression Scoring Recommendation Algorithms:Touch interactions: Tap Pinch Swipe right New fundamentals of design Part II | Intelligent Interactions: New fundamentals of design
- 21. #MachineLearningForDesigners Simple Bank and BBVA’s PFMClassification Part II | Intelligent Interactions: New fundamentals of design
- 22. #MachineLearningForDesigners Amount Date Location Deposit Expense etc.. Category Data Algorithm(s) Output Feedback Loop Classification engine User correction Transaction classification Part II | Intelligent Interactions: New fundamentals of design
- 23. #MachineLearningForDesigners Regression (Prediction)BBVA’s Valora Part II | Intelligent Interactions: New fundamentals of design
- 24. #MachineLearningForDesigners Bedrooms Bathrooms Sq ft / Sq m Location Parking Amenities Last sale price Comparable sales etc... Estimated price (range) Data Algorithm(s) Output Feedback Loop Regression engine Actual sales price User corrections House price forecasting Part II | Intelligent Interactions: New fundamentals of design
- 25. #MachineLearningForDesigners RecommendationsAmazon Part II | Intelligent Interactions: New fundamentals of design
- 26. #MachineLearningForDesigners Past book purchases Other purchases Comparison purchases etc.. Book suggestions Data Algorithm(s) Output Feedback Loop Recommendation engine Actual Conversion Wishlist Dislikes Shopping recommendations Part II | Intelligent Interactions: New fundamentals of design
- 27. #MachineLearningForDesigners Classification Regression Scoring Recommendation Combine them for mastery Part II | Intelligent Interactions: New fundamentals of design
- 28. #MachineLearningForDesigners Anticipatory designiPhone Part II | Intelligent Interactions: New fundamentals of design
- 29. #MachineLearningForDesigners Tips and adviceairbnb tips for guests and hosts Part II | Intelligent Interactions: New fundamentals of design
- 32. #MachineLearningForDesigners Tips and adviceairbnb tips for guests and hosts Part II | Intelligent Interactions: New fundamentals of design
- 33. #MachineLearningForDesigners • What intelligence can I add to the experience? • Can I personalize the experience? • What needs might I anticipate for my user? • What data do I need (or can I use)? • What can we glean from unstructured data? New Considerations
- 34. #MachineLearningForDesigners Making ML visible (or not) Part II | Intelligent Interactions: Impact to the Practice of Design Zillow
- 35. #MachineLearningForDesigners Visibility: User data controls Part II | Intelligent Interactions: New challenges Amazon
- 36. #MachineLearningForDesigners Visibility: User privacy controls Part II | Intelligent Interactions: New challenges Spotify
- 37. #MachineLearningForDesigners Visibility: Expectation management Part II | Intelligent Interactions: Impact to the Practice of Design
- 38. #MachineLearningForDesigners Visibility: Expectation management. Curious cases Part II | Intelligent Interactions: Impact to the Practice of Design
- 39. #MachineLearningForDesigners Visibility: Good practices Part II | Intelligent Interactions: Impact to the Practice of Design
- 40. #MachineLearningForDesigners • Where might there be need for transparency? • Where might there be issues of trust? • Does my design raise concern over privacy? • Is there need for user privacy controls? • Should I communicate uncertainty (degree of confidence) in my advice? New Considerations
- 41. #MachineLearningForDesigners Part III | The (near) future of design Part III The (near) future of design New channels New tools New lingo
- 42. #MachineLearningForDesigners To a Designer a recommendation is... To a Data Scientist a recommendation is... Advice or suggestions given to users A system of ordering things according to each user preferences Part III | The (near) future of design: Engines of Intelligent Experiences We need a common language
- 43. #MachineLearningForDesigners Voice + screen Voice only Text and/or voice New channels. New skills.Conversational UX
- 44. #MachineLearningForDesigners Automatic personas [ref] Part III | The (near) future of design: Impact to the Practice of Design Augmenting the designer
- 45. #MachineLearningForDesigners Generative Design By Autodesk Dreamcatcher Part III | The (near) future of design: Impact to the Practice of Design Augmenting the designer
- 46. #MachineLearningForDesigners • Befriend a data scientist • Educate and evangelize • Keep the conversation going BBVA Data Analytics Blog BBVA G+ Communities: Machine Learning Technologies Join the community: jonah.burlingame@springstudio.com @mind_arc alejandro.vidal@bbvadata.com @doblepensador #MachineLearningForDesigners
- 47. #MachineLearningForDesigners Machine Learning for Humans by BBVA Data & Analytics Machine Learning for Designers by Patrick Hebron Experience Design in the Machine Learning Era by Fabien Girardin When User Experience Designers Partner with Data Scientists by Fabien Girardin and Neal Lathia The Step-By-Step PM Guide to Building Machine Learning Based Products by Yael Gavish Specially: #5: Machine Learning is Very Much a UX Problem Human-Centered Machine Learning By Josh Lovejoy and Jess Holbrook Applications Of Machine Learning For Designers by Lassi Liikkanen Data Jujitsu by DJ Patil Power to the People: How One Unknown Group of Researchers Holds the Key to Using AI to Solve Real Human Problems by Greg Borenstein The ethics of good design: A principle for the connected age by Aaron Weyenberg Are notifications a dark pattern? by Andrew Wilshere A Strategists guide to artificial intelligence by Anand Rao Further reading ;)
Editor's Notes
- [JONAH] Right level of technical depth so you realize ML is not magic but not so technical that you feel stupid for not understanding. Want real feedback. Paper evaluation. Introduce Alex Vidal Mata
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- [JONAH] Douglas Hofstadter :"AI is whatever hasn't been done yet.
- [JONAH] There are a lot of intelligent behaviours across all the industries. Sometimes we’re so used to them that we don’t take attention to them and we think they are not intelligent. Note that intelligence definition is very tight with each problem to solve. We’ll talk more about this later.
- [JONAH] AI is the ‘umbrella term’ for a larger group of technologies. Machine Learning is but one science within AI. Similar to how UX is an umbrella term for many disciplines. Today we are going to focus on ML specifically.
- [ALEX] ML is the answer of AI to one critical question… What is a cat? Imagine that you want create a product like isthisacat.com when any user could upload a picture of his pet and know if his pet is a cat or not. For that amazing Series A funded startup you need one of these: A huge workforce to process all the pictures and manage your huge web traffic An AI algorithm that classify cats The latter sounds easier and cheaper, let’s try it. We have to program the algorithm in the next slide Our first minimum viable algorithm is this one. We’ll classify the image as a cat if it has whisker and it is furry. Perfect. Let’s try it with a few examples. Aw, so cute. But… ups. We didn’t take into account lions. Ok, not problem. Next iteration’ll be better.
- [ALEX] Oops...
- [ALEX] As you can see, even with this seemingly simple example this method of programming with rules has limitations - it simply doesn’t scale.
- [ALEX] ML is the answer of AI to one critical question… What is a cat? Imagine that you want create a product like isthisacat.com when any user could upload a picture of his pet and know if his pet is a cat or not. For that amazing Series A funded startup you need one of these: A huge workforce to process all the pictures and manage your huge web traffic An AI algorithm that classify cats The latter sounds easier and cheaper, let’s try it. We have to program the algorithm in the next slide
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- ALEX Talk about the first years of Material Design Guidelines.
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- (COment here this change allow us in banking idutry to server better our 90% of our clients with personalized advisory)
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- And here? Maybe we can think about a continiuum between two extreme cases (work in progress)
- You can add in your interface options to “forget” things about your users. For example here Amazon allow me to remove from my browsing history any items that I don’t want them to keep
- And here? Maybe we can think about a continiuum between two extreme cases (work in progress)
- And here? Maybe we can think about a continiuum between two extreme cases (work in progress)
- And here? Maybe we can think about a continiuum between two extreme cases (work in progress)
- [JONAH]
- ALEX I’m going to share with you something that happens three times in the exact same way in three different projects. Do you remember the airbnb’s insight example? It looks like For a DS a recommendation system is a very specific kind of algorithm that tryes to rank for each user the most prefered items (books/movies/songs…) for him. If look the Airbnb’s example they’re recommending you to change your price becuase probably your price is more than they expected. Try to look for the differences. Left side a machine tries to arrange all the books that amazon has in a way that the books you’d love will be on the first positions
- As I told before touchable devices changed everything in the practice of design. But now with ML we’ve new opportunities