Designing User Interactions with AI: Servant, Master or Symbiosis. Alan Dix
The AI Summit London, 22nd Sept. 2021.
https://www.alandix.com/academic/talks/AI-Summit-2021-UI-with-AI/
All AI ultimately affects people, in some cases deeply buried, in others interacting directly with users whether physically, such as autonomous vehicles, or virtually, such as recommender systems. In these interactions AI may be a servant, such as Alexa operating on command; or AI may be the master, such as gig-work platforms telling workers what to do. However, potentially the most productive interactions are a symbiosis, human and AI complimenting one another. Designing human-in-the-loop systems changes the requirements of both AI algorithms and user interfaces. This talk will explore some of the design principles and examples in this exciting area.
Key Takeaways:
* Deterministic ground
– helping users know what may or may not adapt
* Appropriate intelligence
– tuning AI to offer human alternatives and fail well
* Epistemic interaction
– choosing user interactions that are informative for ML
user interface, artificial intelligence, design, machine learning, deterministic ground, appropriate intelligence, alien intelligence, Epistemic interaction
Designing User Interactions with AI: Servant, Master or Symbiosis. Alan Dix
The AI Summit London, 22nd Sept. 2021.
https://www.alandix.com/academic/talks/AI-Summit-2021-UI-with-AI/
All AI ultimately affects people, in some cases deeply buried, in others interacting directly with users whether physically, such as autonomous vehicles, or virtually, such as recommender systems. In these interactions AI may be a servant, such as Alexa operating on command; or AI may be the master, such as gig-work platforms telling workers what to do. However, potentially the most productive interactions are a symbiosis, human and AI complimenting one another. Designing human-in-the-loop systems changes the requirements of both AI algorithms and user interfaces. This talk will explore some of the design principles and examples in this exciting area.
Key Takeaways:
* Deterministic ground
– helping users know what may or may not adapt
* Appropriate intelligence
– tuning AI to offer human alternatives and fail well
* Epistemic interaction
– choosing user interactions that are informative for ML
user interface, artificial intelligence, design, machine learning, deterministic ground, appropriate intelligence, alien intelligence, Epistemic interaction
History and future of Human Computer Interaction (HCI) and Interaction DesignAgnieszka Szóstek
This is the first presentation given for the master course at HITLab, Canterbury University, Christchurch, New Zealand. It shows the snippets of the history of the field of human computer interaction that led to its increasing popularity at the present.
additional slides for Chapter 4: Paradigms
from
Dix, Finlay, Abowd and Beale (2004).
Human-Computer Interaction, third edition.
Prentice Hall. ISBN 0-13-239864-8.
http://www.hcibook.com/e3/
Artificial Intelligence and Machine Learning Aditya Singh
Presented By JBIMS Marketting Batch (2017-2020).
Application Artificial Intelligence in MIS(Management Information System). Presented By Trilok Prabhakaran , Aditya Singh , Shashi Yadav, Vaibhav Rokade. Presentation have live cases of two different industry.
Interaction Design in Human Computer Interaction by Vrushali Dhanokar. This PPT is useful to every students who study Human Computer Interaction in detail. Specially for TE Students of Information Technology in Pune University. Thank You.
A 1-hour introductory lecture on multimodal interaction that I gave to bachelor HCI students. Included a section on how to get started in this exciting line of research.
AI and automation is all the rage nowadays - but what’s the history of these technologies, innovations and ideas?
AI and automation is all the rage nowadays - but what’s the history of these technologies, innovations and ideas? This slides will discuss the brief history of the current interesting technologies and their development to society and mankind.
History and future of Human Computer Interaction (HCI) and Interaction DesignAgnieszka Szóstek
This is the first presentation given for the master course at HITLab, Canterbury University, Christchurch, New Zealand. It shows the snippets of the history of the field of human computer interaction that led to its increasing popularity at the present.
additional slides for Chapter 4: Paradigms
from
Dix, Finlay, Abowd and Beale (2004).
Human-Computer Interaction, third edition.
Prentice Hall. ISBN 0-13-239864-8.
http://www.hcibook.com/e3/
Artificial Intelligence and Machine Learning Aditya Singh
Presented By JBIMS Marketting Batch (2017-2020).
Application Artificial Intelligence in MIS(Management Information System). Presented By Trilok Prabhakaran , Aditya Singh , Shashi Yadav, Vaibhav Rokade. Presentation have live cases of two different industry.
Interaction Design in Human Computer Interaction by Vrushali Dhanokar. This PPT is useful to every students who study Human Computer Interaction in detail. Specially for TE Students of Information Technology in Pune University. Thank You.
A 1-hour introductory lecture on multimodal interaction that I gave to bachelor HCI students. Included a section on how to get started in this exciting line of research.
AI and automation is all the rage nowadays - but what’s the history of these technologies, innovations and ideas?
AI and automation is all the rage nowadays - but what’s the history of these technologies, innovations and ideas? This slides will discuss the brief history of the current interesting technologies and their development to society and mankind.
Bootstrapping the Information Architecture (Italian IA Summit)Peter Boersma
When I design, it is in the early stages of an interactive system’s life. There are no widgets to place on screens, or menus to collapse or expand. No wireframes, no screen flows, no accessibility or SEO issues. No search, no controlled vocabulary, no settings screens or personalisation options to design. In short: the project needs to be bootstrapped.
I am involved when a lot of things need to be explored and modelled; the scope and environment of the system, the core concepts that make up its parts, their relationships and their names. So what do we produce in that stage? Mostly so-called concept diagrams.
In this talk, I explain what concept diagrams are, referencing other people’s experiences as well as my own, and how they are useful when a design needs to be bootstrapped. I show how I have used variations of them in recent assignments for KLM and the City of Amsterdam, among others. I will try to convince you that you should create one for each and every situation that needs bootstrapping.
A recap of interesting points and quotes from the May 2024 WSO2CON opensource application development conference. Focuses primarily on keynotes and panel sessions.
In this presentation i talk about the design process for mobile. From knowing your user goals and preferences, to your business needs, and the different factors you need to consider before building an app.
Talk from Renaissance IO 2014 on how to make sure you’re designing your apps for the right audience. Covers Baxley’s “Universal Model of the User Interface” and designer temperaments.
USECON Webinar 2017: Alina's Guests - Floor Drees from sektor5USECON
Everyone working in Artificial Intelligence (AI)/chatbots, has the opportunity to further develop technology which will affect the future of especially finance/payment, transport and health. The main question is how human-like‘ these solutions will need to be (if at all) in order to be adopted. And how will the future of employment look like?
USECON Webinar "Alina's Guests": Chatbots with Floor Drees from sektor5Alina Köhler
Everyone working in Artificial Intelligence (AI)/chatbots, has the opportunity to further develop technology which will affect the future of especially finance/payment, transport and health. The main question is how human-like‘ these solutions will need to be (if at all) in order to be adopted. And how will the future of employment look like?
Concept computing is the next paradigm for Internet and enterprise software. Concept computing is a:
-- Paradigm shift from information-centric to knowledge-driven patterns of computing.
-- Spectrum of knowledge representation, from search to knowing.
-- Synthesis of AI, semantic, model-driven, mobile, and User interface technologies.
-- Solution Architecture where every aspect of computing is semantic and directly model-driven.
-- Development methodology where Every stage of the solution lifecycle becomes semantic, model-driven & super-productive.
-- New domain where value multiplies.
Requirements Engineering for the HumanitiesShawn Day
This workshop explores how requirements engineering can be employed by digital and non-digital humanities scholars (and others) to conceptualise and communicate a research project.
requirementsEngineeringAs the field of digital humanities has evolved, one of the biggest challenges has been getting the marrying technical expertise with humanities scholarly practice to successfully deliver sustainable and sound digital projects. At its core this is a communications exercise. However, to communicate effectively demands an ability to effectively translate, define and find clarity in your own mind.
Introduction presentation for the opening session of InfoCamp Seattle 2008, an un-conference for user-centered design, information architecture, librarianship, and information management. http://infocamp.info
A tutorial session on UXD hacks I gave at O'Reilly Etech in 2004.
Original context here: http://conferences.oreillynet.com/cs/et2004/view/e_sess/4767
"User-Centered Design and participatory product development are established, proven techniques for making interfaces and information understandable. But how is it possible to use them when your knowledge, the technology, and the possible markets are moving so quickly? Is it possible to create alpha-tech that defines a new market and is a joy to use? UI Design for Alien Cowboys is a three-hour tutorial and workshop that proposes that it is."
Leverage IoT to Enhance Security and Improve User ExperienceSmashing Boxes
Security and Access are critical issues when it comes to securing environments, such as coworking and shared office spaces, manufacturing environments, hotels, and even museums. Current access solutions rely on keycards swiped at entry/exit points while static QR codes are scanned at various security checkpoints. These methods aren’t very secure. Keycards are often lost, forgotten, and even shared among users. And QR codes can be copied and scanned from anywhere.
When was the last time you found yourself locked out of your office because you forgot your keycard?
Chances are, you don’t have to think about this too hard. Here at Smashing Boxes, we are certainly no stranger to this. When a phone upgrade all but eliminated the need to carry a wallet for engineer Grady Knight, he found himself without his keycard, locked outside of the Smashing Boxes office. DoorBot, a homegrown IoT innovation, saved the day by alerting team members to answer the door.
Once a SMASHING LABS project, DoorBot is a reprogrammed Amazon Dash Button encased it in a 3D-printed enclosure, adapted to work as an IoT button. It’s a smarter doorbell that isn’t disruptive, sends notifications to specific subscribers, and didn’t have to be hardwired into the electrical system. When implemented at the Smashing Boxes Durham office, where the working area and doors are spread out, this was the perfect solution. That is until the continuous notifications in the DoorBot Slack channel (along with having to get up to answer the door), disrupted employee workflow, and led people to mute channel notifications.
The frequent DoorBot disruption combined with his background using IOT CONNECTED DEVICES TO SOLVE BUSINESS CHALLENGES prompted Grady to explore the following idea: How we can leverage IoT to not only enhance security but deliver an improved user experience?
The solution he came up with: leverage IoT to make a simple modification to commercially available locks. We hope to use this device to enhance or replace traditional monitoring systems with an integrated design system based on the following:
Infrastructure:
OAuth / AWS Lambda / DynamoDB
Mobile: REACT NATIVE
IoT Device: Node / Raspberry Pi / Camera
Learn more here https://smashingboxes.com/blog/leverage-iot-enhance-security-improve-user-experience/
Bourbon on a Budget with IoT - Pinetop Distillery | RIoT NCSmashing Boxes
IoT for production-ready applications in high-risk manufacturing: How Smashing Boxes engineer Grady Knight taps into the power of IoT to optimize the complex craft of distilling high-quality spirits.
In this presentation, Grady Knight discusses how Internet-connected devices (IoT) can be used in small scale manufacturing, specifically in distilling bourbon. He demonstrates the utility of custom, one-off IoT development to enable manufacturing (versus building IoT connected devices for widespread consumption.) This presentation was given at NC RIoT's lunch n learn in June 2019.
We are still in the toddler phase of the “wearable” generation. Much of what we “touch” currently is just noise in the form of data.
Where is it going? We break down some predictions.
Find more ideas at smashingboxes.com/ideas
The Law of Demeter (LoD) is a design guideline mainly used in object-oriented software development.
This presentation was given at a Stacked! Meetup by Michael Elfassy of Smashing Boxes. Learn more about Stacked! at meetup.com/stacked or stackedconf.org.
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
4. STARTER UX ML QUESTIONS
I. Where do I find information about UX for ML?
II. Are there resources like tutorials? Demos? Thought
leaders?
III. How is machine learning going to change the future of UX?
a. Opportunities and setbacks
IV. Revolutions will take hold when tools become highly
mature and more accessible (for designer/dev/user needs)
a. We have not reached this level of expertise yet
5. ML RENAISSANCE
I. “Machine learning is in the midst of a renaissance that will
transform countless industries and provide designers with
a wide assortment of new tools for better engaging with
and understanding users. These technologies will give rise
to new design challenges and require new ways of
thinking about the design of user interfaces and
interactions.” - Patrick Hebron, Machine Learning for Designers
7. LET’S START WITH “WHAT’S UX?”
I. “User Experience (UX) design is the process of creating
products that provide meaningful and relevant
experiences to users. This involves the design of the entire
process of acquiring and integrating the product, including
aspects of branding, design, usability, and function.”
-The Interaction Design Foundation
8. THEN, MACHINE LEARNING?
I. “Machine learning is a field of artificial intelligence that
uses statistical techniques to give computer systems the
ability to “learn” (e.g., progressively improve performance
on a specific task) from data, without being explicitly
programmed.”
-Wikipedia
II. Just in case: Algorithms are a process or set of rules to be followed in calculations or
other problem-solving operations, especially by a computer.
10. USES OF HCI OVER TIME
I. 1980’s - 1990’s: Desktop and Mental Models
II. 1990’s - Early 2000’s: Collaboration and Communication
III. Mid 2000’s - 2010: Self Expression and Social Change
IV. Now - Future: Crafting technologies to the human
experience
11. ML - A DIFFERENT KIND OF LOGIC
I. Fuzzy logic - logic formed on approximations versus exact
a. Need auxiliary knowledge of range of values
i. Car sizes
1. Approximation of range
2. Can meaningfully discuss with other people
II. Computers have “lived a life of experiential deprivation.”
- Patrick Hebron
12. “PRIOR” TO MACHINE LEARNING
I. Systems are used to Boolean logic where every expression
has to ultimately evaluate as either true or false
II. Machine Learning challenges this logic by giving machines
experiential knowledge, which then helps deal with the
fuzzier/human logic
a. “Design challenges the problem of working with
imprecise technology and unpredictable behavior.” - Ibid
13.
14.
15.
16. FACE VALUE
I. Infinite number of combos, genders, vantages,
environments, races, etc…
II. Examples of how we cannot reach perfect recognition with
faces, despite a lifetime of examples
17. 20 QUESTIONS
I. Ways we network with others
a. Not sure what things about you will overlap (job etc)
II. For conversational user interfaces
a. Make sure you have the ability to create common
ground with the users and the things the system can
do to become better acquainted
III. Works as a learning decision tree
19. DESIGN THINKING
“Design thinking is a human-centered approach to
innovation that draws from the designer’s toolkit to integrate
the needs of people, the possibilities of technology, and the
requirements for business success.” - Tim Brown, president and CEO, IDEO
20. UX & DESIGN THINKING
I. Design Thinking is a process or methodology. It is about
applying a specific way of thinking to a situation.
II. Design Thinking methods can be utilized not only by UX
designers, but also developers, product owners and
marketers.
III. Creative insight isn’t limited to just designers
a. Designer’s emphasis on criticism
21. DT & ML STAGES
I. Design Thinking Stages
a. Empathize
i. Who is my user?
ii. Pain points
b. Define
i. Point of View
c. Ideate
i. Brainstorming
d. Prototype
i. Working and ready
e. Test
i. Monitor Use
ii. Effectiveness
I. Machine Learning Stages
a. Analyze
i. Capture key decisions to find out
variables/metrics
b. Synthesize
i. Combo of separate elements to create new
c. Ideate
i. Small sample of data applied to various
analytical models/algorithms for insight
d. Tuning
i. Additional data capture
e. Validate
i. UX and analytic model tuning perspectives
Source: Abhay Pandey - Medium
24. YOU AND ML ON THE DAILY
I. Siri - taught to understand the nuances of our language
II. Facebook - uses algorithms to recognize faces from contact list
III. Google Maps - traffic speed/time and best possible route
IV. Google Search - recommendations based on searches
a. 2012 - Introduced Knowledge Graph - algorithm used to decipher semantic
content of a search query
V. PayPal - ML to fight off fraud. Analyzes tons of consumer data and evaluate risks
VI. Netflix - video recommendation engine
VII. Uber - algorithms to determine arrival times, pick up locations, and UberEATS’
VIII. Lyst - match customer searches with relevant rec. Meta-data tags for visual comp.
IX. Spotify - ML to establish likes and dislikes and provides list of related tracks
25. Phoneme - Any of the perceptually distinct units of sound in a specified language that distinguish one
word from another, for example p, b, d, and t in the English words pad, pat, bad, and bat.
28. CURATION VS ALGORITHMS
I. Apple Music has DJs who custom create playlists rather
than how Spotify or Pandora works
a. Designers need to be able to design BOTH algorithmic
data and curated data - they’re equally important
personas
b. What data do you have?
c. What data do you want?
30. UNDERSTANDING ML LANGUAGE
I. Machines like Siri understanding spoken language is a big
deal because it allows development of more natural
interaction paradigms
II. Diverse vocals/speech patterns
III. Speech-to-text sometimes struggles for humans and
computers alike (Shazam and SoundHound)
31. “GAMING THE ALGORITHM”
I. Pamela Pavilscak, author of Emotionally Intelligent Design, talks about the
strategic evasion of computers knowing too much about us
a. Then we’re disappointed that we get a bunch of info not related to us
i. Private browsing, ads vs ad Google search
b. Algorithms face backwards, not forward
i. “Making predictions on past behaviors doesn’t take into
account what is essentially human. We change a little every
day. We get interested in new things. We change our minds. We
feel a little conflicted. Algorithms have a hard time with that.”
32. OTHER ALGORITHMIC SETBACKS
I. They’re working with the big picture of what you do
a. Topics based on behavior
II. Incomplete information - algorithms keep up with big
purchases but other data points can be inaccurate
III. Resources - history of sites (anticipatory) but doesn’t
understand personalization completely
34. A BEHAVIORAL PROBLEM
I. “We are more than just the sum of our behaviors. We don’t
behave rationally, maybe not even predictable irrationally. A
human presence can improve the algorithmic experience.” -
Pamela Pavilscak
35. ALGORITHMIC EMPATHY
I. Algorithms change the way we explore and understand
empathy with users
II. Pamela Pavilscak’s process to
“Cultivate Algorithmic Empathy”
a. Research practices: Data Role Play, Algorithm Swap,
Data Doubles, Algorithmic Personas, and Shared
Mythologies (broken out on next slides)
36. DATA ROLE PLAY
I. It acknowledges the abstraction of algorithms
a. Good way to start the conversation about this new type
of empathy
b. Would it be awkward to ask a stranger for private
information like email and phone number?
i. Even worse - getting their info without them
knowing it
37. ALGORITHM SWAP
I. We have very private experiences with our devices
II. Might change once we start interacting with voices rather
than hands
a. Right now it feels unnatural to spend time with
someone else’s private self
i. YET, can be meaningful to see that side of the
person you’re designing for
38. ALGORITHMIC PERSONAS
I. Design teams chose a base persona on a combination of:
a. Demographics
b. Interviews
c. Behavioral data that is collected
39. SHARED MYTHOLOGIES
I. When we reach an algorithmic disconnect, people start to
speculate as to WHY it’s no longer working
II. So INSTEAD of letting myth after myth float around
a. Design to reveal the algorithm
i. “See yourself through the ads you encounter” like on
Facebook etc…
41. DESIGNERS AND iOT DEVICES
I. “Allows designers to discover implicit patterns within
numerous facets of a users behavior. These patterns carry
inherent meanings, which can be learned from and acted
upon, even if the user is not expressly aware of having
communicated them. In this sense, these implicit patterns
can be thought of as input modalities that, in practice,
serve a very similar purpose to more tangible input modes.”
- Patrick Hebron
42.
43. DISNEY AND MACHINE LEARNING
I. Eventually Disney wants “a system that has gathered so
much data about a user over such a long period of time
that the anticipatory design creates a completely
different experience for each person, despite the fact
that all the users are in the same physical location.”
-Bryon Houwens
48. DATA SCIENCE
I. Look like familiar UX pattern/flows?
a. Divergence and convergence
II. Predictive modeling is the scientific process within
machine learning that is incredibly important for an
aligned UX methodology and practice
III. We can only do but so much with current material
a. Present opportunities for growth
50. USING DEDUCTIVE & INDUCTIVE
REASONING
I. Helps designers with holistic understanding of actions
II. General rules for observing and collecting data
a. “Deductive - broad theory about rules governing a
system, distill theory into more specific hypotheses,
gather specific observations and test against our own
hypotheses to confirm if original theory was right or
wrong.”- Patrick Hebron
51. USING DEDUCTIVE & INDUCTIVE
REASONING
I. “Inductive reasoning starts with a group of specific
observations that look for patterns in those observations,
formulate tentative hypotheses, and ultimately try to
produce a general theory that encompasses original
observations.” - Patrick Hebron
52. STORYTELLING
I. Storytelling for developers and designers to visualize something together
a. More than just screens that user interacts with, but understand why
things are happening behind the curtains
II. Importance of storytelling in the beginning of product creation
a. The experience impacting the user
i. Storyboards
ii. Prototypes
iii. Strategy Decks
iv. Diagrams
III. Empowerment as a shared vision and language with fewer steps
53. AIRBNB
I. Were creating a model to answer question “what will the booked
price of a listing be on any given day in the future?”
a. Developers were talking alphas and betas - foreign material to
many designers
II. Asked developers to sketch out the idea in a diagram and do a
walk-through
a. THIS is where the connection was made
III. Language barrier broken down and then created a shared
language about the product and moving forward (storytelling)
56. ML UX BEST STEPS - PATRICK H.
1. Design tasks explicitly so that users can catch errors and redirect system
behavior.
2. Fallback mechanisms - circumvent in ML functionality and perform tasks
with explicit logic.
3. Test in as many environments and limited audience release.
4. Use metrics—confidence scores—to assess feature. Realistic expectations.
5. Consider impressive sounding feature if unreliable. Failure vs feature.
6. Make risks obvious. Allow users to decide if benefit outweighs risk.
7. Serious consequences—even death—in system failure. Take extreme
caution to assess risk/liabilities.
58. WHY PROTOTYPE?
I. To understand - “freedom to think through all the different
ways you could solve the problem, discover new problems
that need to be addressed, and help you refine your ideas
with the feedback you receive.”
II. To test and improve - “the main reason to prototype”
III. To communicate - “invest team, stakeholders, or end users”
IV. To advocate - “for the design or direction”
- Kathryn McElroy, Prototyping for Physical and Digital Products
59. ML PROTOTYPING - WEKINATOR
I. ML challenges rapid prototyping because of architectures and training
II. Requisite of code or data sets limits tools (evolving)
III. Currently, there are some existing tools we can use
a. Wekinator - it’s free and open source
i. Allows development of experimental gesture recognition and
interface controllers for microphones, webcams, Kinect etc...
ii. Programming free!
iii. “Walks designer through process where defines particular
gesture by demoing it to machine and then associates gesture
with the desired output action.” -Ibid
61. MATHEMATICA
I. “Tool features a polished UI and does not require deep
understanding of programming, though some basic
familiarity with text-based scripting is helpful
II. Machine learning features applied to wide range of data
types and auto data preprocessing and model selection
features will help users get good results without a great
deal of trial-and-error or deep knowledge of a particular
model’s training parameters.” -Patrick Hebron
62. MATHEMATICA CONTINUED
I. “Provides turnkey support for range of common ML tasks
like image recognition, text classification, and classification
or regression of generic data.
II. Datasets can be loaded through interactive, visual interface
III. Extremely well documented and embeds assistive tools like
feature suggestion and autocompletion directly to its
interface” -Ibid
IV. Not free www.wolfram.com/mathematica
65. WRAPPING IT UP
I. Time to make that prototype and test with people
a. Are they happy? Are they freaked out? Do they trust
you? Do they trust the device(s)?
b. Observe, listen, chronicle.
c. Why? What can you do? What can’t you do?
66. KEEP EXPLORING
I. HCI designers prototyping process post product release
II. UX for Machine Learning is growing at a rapid pace and
keep in mind we’re all figuring it out—experts included
III. Use tried and true methods where it is appropriate
IV. Remember, YOU already use machine learning—think like
another user
V. Errors are opportunities
68. TAKEAWAYS
READ
Patrick Hebron - @patrickhebron - www.patrickhebron.com
Pamela Pavliscak - @paminthelab - Emotionally Intelligent Design
LEARN
Wekinator - http://www.wekinator.org/
Mathematica - http://www.wolfram.com/mathematica/
LET US HELP!
SB Design Sprints - 2-hour to 2-day discovery sprint to help identify your
needs, personas and design schemas.
Prototype Workshop - Come with an idea, leave with a clickable prototype
Dave Shepley - dave.shepley@smashingboxes.com @smashingboxes