when the cloud decides: designing for predictive machine learning for the iot (o'reilly design 2016)

+WHEN THE CLOUD DECIDES: DESIGNING FOR

PREDICTIVE MACHINE LEARNING FOR THE IOT

2015 PARC

Mike Kuniavsky

OReilly Design Conference

January 21, 2016

Good afternoon and thanks for having me here. In this talk I want to look at the

design challenges of systems that anticipate users needs and then act on them.

That means that it sits at the intersection of the internet of things, user experience

design and machine learning, which to me is new territory for designers who may

have dealt with one of those disciplines before, but rarely all three at once.

The talk is divided into several parts: it starts with an overview of how I think

Internet of Things devices are primarily components of services, rather than being

self-contained experiences, how predictive behavior enables key components of

those services, and then I finish by trying to to identify user experience issues

around predictive behavior and suggestions for patterns to ameliorate those issues.

A couple of caveats:

- I focus almost exclusively on the consumer internet of things. Although predictive

behavior is an important part of the Industrial Internet of Things for things like

preventitive maintenance or energy savings in building environments, I feel its

REALLY key to the consumer IoT because of its potential ability to cut through the

information and data fog we live in.

- I want to point out that few if any of the issues I raise are new. Though the term

internet of things is hot right now, the ideas have been discussed in research

circles for decades. Search for ubiquitous computing, ambient intelligence, and

pervasive computing and itll help you keep from reinventing the wheel.

- Finally, most of my slides dont have words on them, so Ill make the complete

deck with a transcript available as soon Im done.

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+OReilly Design 2016

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Let me begin by telling you a bit about my background. Im a user

experience designer. I was one of the first professional Web

designers. This is the navigation for a hot sauce shopping site I

designed in the spring of 1994.

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Ive also worked on the user experience design of a lot of

consumer electronics products from companies youve probably

heard of.

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I wrote a couple of books based on my experience as a designer.

One is a cookbook of user research methods, and the second

describes what I think are some of the core concerns when

designing networked computational devices.

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I also started a couple of companies. The first, Adaptive Path, was

primarily focused on the web, and with the second one, ThingM, I

got deep into developing hardware.

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Today I work for PARC, the famous research lab that invented the

personal computer and laser printer, as a principal in its Innovation

Services group. We help companies reduce the risk of adopting

novel technologies using a mix of ethnographic research, user

experience design and innovation strategy. We do everything from

developing novel products for our clients to coaching teams in how

they can be much more strategic and effective with their innovation

efforts.

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-

R. G. Shoup, 1971

PARC

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PARC also started thinking about what we call the IoT long before

most other companies.

It was at PARC in 1971 that Dick Shoup, and early PARC

researcher, wrote that eventually processors would be as

common, and as invisible as electric motors. This clearly

outlines the destiny of connected computer: that eventually it will

become as boring and as common as electric motors are today.

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Mark Weiser, 1996

PARC

20 years later, also at PARC, Mark Weiser coined the term

ubiquitous computing to describe a future when the number of

computers surpassed the number of people, in everyday usage.

In this chart from 20 years ago, he predicted that would happen

around 2005. He didnt live to see that crossover, but he was

basically rightthe iPhone launched in 2007and we now live

in the world he envisioned.

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But it didnt appear all at once. Weve only started the transition to

the ubiquitous computing world, and as such, were seeing a lot

of bad ideas about what the Internet of Things is and it isnt.

There are so many bad ideas in fact that there are entire

Tumblrs dedicated to mocking stupid IoT ideas. One is about

dumb smart things and the other is just about smart

refrigerators.

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+ CONNECTING STUFF TOTHE INTERNET IS EASY

AND POINTLESS

OReilly Design 2016

Simply connecting existing stuff to the internet does not produce

customer value

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Simple connectivity helps when youre trying to maximize the

efficiency of a fixed process, but thats not a problem that most

people have. Weve been able to simply connect various

devices to a computer since a Tandy Color Computers could

lights off and on over X10 in 1983. The problem is that that

wasnt very useful then, and its not very useful now. If you

replace the Tandy with an iPhone and the lamp with a washing

machine

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or an egg carton, you still have the same problem, and its a user

experience problem.

The UX problem is that end users have to connect all the dots to

coordinate between a wide variety of devices, and to interpret

the meaning of all of these sensors to create personal value. For

many simply connected products there is so little efficiency to be

had relative to the cognitive load that its just not worth it. Whats

worse, the extra cognitive load is exactly opposite to what the

product promises, and customers feel intensely disappointed,

perhaps even betrayed, when they realize how little they get out

of such a product That makes most such products effectively

WORSE than useless. That promise gap is what distinguishes

an optional and marginal gadget from a tool.

This strategy worked very poorly for Quirky.

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+ IOT SERVICE AVATARS +MACHINE LEARNING =

PREDICTIVE BEHAVIOR

OReilly Design 2016

How do you create a tool that reduces

cognitive load instead of creating it, that

exchanges peoples precious time for

significant value? One approach is to couple

cloud-based services with predictive machine

learning models to anticipate what behaviors

will maximize the chances of a desirable

outcome in a given situation. Designing the UX

for this kind of solution is what Ill talk about

today, but first let me unpack these two

concepts a bit.

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When I talk about services, Im talking about thinking of hardware

devices as avatars of cloud-services, which makes them very

different than traditional consumer electronics. Historically, a

company made an electronic product, say a turntable, they found

people to sell it for them, they advertised it and people bought it.

That was traditionally the end of the companys relationship with

the consumer until that person bought another thing, and all of the

value of the relationship was in the device. With the IoT, the sale of

the device is just the beginning of the relationship and holds almost

no value for either the customer or the manufacturer.

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SERVICE

AVATARS

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Value now shifts to services and the devices, software applications

and websites used to access itits avatarsbecome

secondary. A camera becomes a really good appliance for

taking photos for Instagram, while a TV becomes a nice

Instagram display that you dont have to log into every time, and

a phone becomes a convenient way to check your friends

pictures on the road.

Hardware becomes simultaneously more specialized and devalued

as users see through each device to the service it represents.

The avatars exist to get better value out of the service.

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Amazon really gets this. Heres a telling older ad from Amazon for

the Kindle. Its saying Look, use whatever device you want. We

dont care, as long you stay loyal to our service. You can buy our

specialized devices, but you dont have to.

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When Fire was released 3 years ago, Jeff Bezos even called it a

service.

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Amazon Dash is a service thats enabled by dedicated devices, or

by a Dash button whose only purpose is to be an avatar for a

macaroni and cheese service.

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Most large-scale IoT products are service avatars. They use

specialized sensors and actuators to support a service, but have

little valueor dont work at allwithout the supporting service.

Smart Things, which was acquired by Samsung last year, clearly

states its service offering right up front on their site. The first

thing they say about their product line is not what the

functionality is, but what effect their service will achieve for their

customers. Their hardware products functionality, how they will

technically satisfy the service promise, is almost an afterthought.

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Compare that to X10, their spiritual predecessor thats been in the

business for more than 20 years. All that X10 tells is you is what

the devices are, not what the service will accomplish for you. I

dont even know if there IS a service. Why should I care that

they have modules? I shouldnt, and I dont.

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+ PREDICTIVE BEHAVIOR,ENABLED BY MACHINE

LEARNING

OReilly Design 2016

I think the real consumer value connected services offer is their ability to make

sense of the world on peoples behalf, to reduce cognitive load by enablingpeople to interact with devices at a higher level than simple telemetry, at

the level of intentions and goals, rather than data and control. Humans are

not built to collect and make sense of huge amounts of data across manydevices, or to articulate our needs as systems of mutually interdependent

components. Computers are great at it.

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They can make statistical models from many data sources across

space and time and then try to maximize the probability of a

desired outcome. A model learned from thousands of samples

across many people and long periods of time can compensate for

much wider variety of situations in a more nuanced way than an

individual will ever be able to. Because people and their machines

act pretty consistently, these systems can essentially predict the

future, which is how Waze knows that youre probably driving

home when you get in the car after work without you ever telling it

your home address and schedule.

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Such prediction and response is at the heart of the value

proposition many of the most compelling IoT products offering,

starting with the Nest. The Nest says that it knows you. How

does it know you? It predicts what youre going to want based

on your past behavior.

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Amazons Echo speaker says its continually learning. How is that?

Predictive machine learning.

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The Birdi smart smoke alarm says it will learn over time, which is

again the same thing.

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Jaguar, learningAND intelligent.

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The Edyn plant watering system adapts to every change. What is

that adaptation? Predictive analytics.

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Canary, a home security service.

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Heres foobot, an air quality service.

[I also like how one of its implicit service promises is to identify

when your kids are smoking pot.]

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+ ISSUES WITH THE UX OFPREDICTIVE BEHAVIOR

OReilly Design 2016

Predictive behavior creates a seductive world of espresso

machines that start brewing as youre thinking its a good time

for coffee; office lights that dim when its sunny and mac andcheese that never runs out. The problem is that although the

value proposition is of a better user experience, its unspecific in

the details. All the previous machine learning systems that

existed were used in areas such as predictive maintenance and

finance were made by and for specialists. Now that these

systems are for general consumers, we have some significantUX questions. Exactly how will our experience of the world, our

ability to use all the collected data, become more efficient and

more pleasurable?

Were still early in our understanding of predictive UX, so right nowthe problems are worse than solutions and I want to start by

articulating the issues Ive observed in our work.

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+ ISSUE 1: EXPECTATIONS OFAUTONOMOUS BEHAVIOR

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Weve never had mechanical things that make significant decisions

on their own, the first major issue is around expectations. As

devices adapt their behavior, how will they communicate thattheyre doing so? Do we treat them like animals? Do we stick a

sign on them that says adapting, like the light on a video

camera says recording? Should my chair vibrate when

adjusting to my posture? How will users, or just passers-by,

know which things adapt? I could end up sitting uncomfortable

for a long time, waiting for my chair to change, before realizing itdoesnt adapt on its own. How should smart devices set the

expectation that they may behave differently in what appears to

be identical circumstances?

How do we know HOW intelligent these devices are? Peoplealready often project more smarts on devices than those devices

actually have, so a couple of accurate predictions may imply a

much better model than actually exists. How do we know were

not just homesteading the uncanny valley here?

Chair by Raffaello D'Andrea, Matt Donovan and Max Dean.

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+ ISSUE 2: UNCERTAINTY

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The irony in predictive systems is that theyre pretty unpredictable, at least at first.

When machine learning systems are new, theyre often inaccurate, which is not

what we expect from our digital devices. 60%-70% accuracy is typical for a first

pass, but even 90% accuracy isnt enough for a predictive system to feel right,

since if its making decisions all the time, its going to be making mistakes all the

time, too. Its fine if your house is a couple of degrees cooler than youd like, but

what if your wheelchair refuses to go to a drinking fountain next to a door because

its been trained on doors and it cant tell thats not what you mean in this one

instance? For all the times a system gets it right, its on the mistakes that we judge

it and a couple such instances can shatter peoples confidence. Anxiety is a kind of

cognitive load, and a little doubt about whether a supposedly smart system is going

to do the right thing is enough to turn a UX thats right most of the time into one

thats more trouble than its worth. When that happens, youve more than likely lost

your customer.

Unfortunately, sooner than we think, such inaccurate predictive behavior isnt going to

be an isolated incident. Soon were going to have 100 connected devices

simultaneously acting on predictions about us. If each is 99% accurate, then one is

always wrong. So the problem is: How can you design a user experience to make a

device still functional, still valuable, still fun, even when its spewing junk behavior?

How can you design for uncertainty?

Photo CC BY 2.0 photo 2011 Pop Culture Geek taken by Doug Kline:

https://www.flickr.com/photos/popculturegeek/6300931073/

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+ ISSUE 3: CONTROL

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The last issue comes as a result of the previous two: control. How

can we maintain some level of control over these devices, when

their behavior is by definition statistical and unpredictable?

On the one hand you can mangle your devices predictive behavior

by giving it too much data. When I visited Nest once they told me

that none of the Nests in their office worked well because theyre

constantly fiddling with them. In machine learning this is called

overtraining. The other hand, if I have no direct way to control it

other than through my own behavior, how do I adjust it? Amazon

and Netflixs recommendation systems, which are machine

learning systems for predicting what you may like, give you some

context about why they recommended something, but what do I do

when my only interface is a garden hose?

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From: Sheridan and Verplank, 1978

At the dawn of computing cyberneticists like Norbert Weiner

philosophized about the increasingly complex relationship

between people and computers, and how it was fundamentally

different than with other kinds of machines. Developers working

in supervisory control of manufacturing machines and robotics

have had to deal with these questions pragmatically for about 30

years, but I think this is now a problem that is going to be a

general UX problem for everyone going forward.

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+ SHARING CONTROL ANDRESPONSIBILITY WITH ALGORITHMS

OReilly Design 2016

AutomationLevel

1 The computer offers no assistance: humans must make all decision andactions

2 The computer offers a complete set of decision/action alternatives

3 Narrows the selection down to a few

4 Suggests one alternative

5 Executes that suggestion if the human approves

6 Allows the human a restricted time to veto before automatic execution

7 Executes automatically, then necessarily informs humans

8 Informs the human only if asked

9 Informs the human only if it, the computer, decides to

10 The computer decides everything and acts autonomously, ignoring thehuman

From: Parasuraman, Sheridan, Wickens, 2000

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Heres a more modern framework from the industrial automation

world that defines a spectrum of responsibility between people

and computers. It ranges from humans doing all the work to

computers doing all the work completely autonomously. Of

course the goal is to get a system to level 9 or 10. Thats the

maximum reduction in cognitive load. However, for a system to

qualify for that, it has to be very stable, its effects need to be

highly predictable and, equally importantly, it needs to be

adequately embedded in society that its OK for a computer to

take on that level of responsibility. At the airport we trust the

monorail computers to work without human intervention, but we

dont trust the plane computers to do that, even though-as I

understand itplanes can basically fly themselves these days.

I think predictive Internet of Things services fall between 5 and 7

on this scale right now. The problem is that this is the exact

range where youre increasing cognitive load, so the result of the

automation had better be worth this new extra effort.

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+ FIVE PATTERNS FORADDRESSING PREDICTIVE

UX ISSUES

OReilly Design 2016

Here are 5 UX design patterns Ive observed in developing

predictive IoT systems.

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+ PATTERN 0: HAVE A USER STORYFOR EVERY STAGE OF PREDICTION

Acquire

Extract

Classify

Model

Train

Behave

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My first pattern isnt really a pattern, but a general approach. To design these

systems you need to have a user model for every stage of the machine

learning and prediction process. There needs to be a story to tell about each

step, even if its a step that seems like it would invisible to customers.

Starting with acquisition: how will you incentivize people o add data to the

system at all? Why should I upload my cars dashcam video to your traffic

prediction system EVERY DAY? Next, how will you communicate youre

extracting features? I like the way that Google speech to text shows you

partial phrases as youre speaking into it, and how it corrects itself. That

small bit of feedback tells people its pulling information out and it trains

users how to meet the algorithm halfway. How do machine-generated

classifications compare to peoples organization of the same phenomena?

How is a context model presented to end users and developers? How will

you get people to train it and tell you when the model is wrong? Does the

final behavior actually match their expectation?

Machine learning algorithms used to be strictly behind-the-scenes, but in the

IoT they are actors in our lives, so as designers its our responsibility to

understand the situations where the algorithms and the devices they control

interact with peoples lives, especially since theres a deep symbiotic

relationship between the data that comprises the models, the behavior those

models induce and the people who are the intended beneficiaries.

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+ PATTERN 1: BUILD AND MAINTAINTRUST

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VS

Because these systems are working on peoples behalf, at the core

of the user experience is that the user has to trust them to act

autonomously. If you dont trust a machine learning system,

youll be worried about it. If you worry, thats cognitive load.

Every unit of cognitive load has to be balanced by end-user

value.

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+ COMPONENTS OF TRUST INPREDICTIVE UX

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Benevolence Integrity Balance

Humor Control

From: Yoo and Gretzel, 2011

Kantor, et al, 2011

What makes for a trustworthy experience? Computers are social actors and predictive

systems are especially so, the qualities of a trustworthy digital assistant are

essentially the same as those of a trustworthy human apprentice.

Here are some factors recommender system researchers identified as building blocks

of trust when interacting with a system making decisions on your behalf. Why do

you trust a Slack chatbot more than one from, say, AT&T? Is it because its funny,

and if its funny then it cant be evil, right? For me every one of these bricks

represents an interesting cluster of UX, branding and messaging challenges that

are unique to each device.

Benevolence, the recommender systems caring about the user and acting in the

users interest

Integrity, adherence to a set of principles (e.g. honesty) that the user finds acceptable

Balance. several studies have demonstrated that communicators can enhance their

trustworthiness when they provide both sides of the argument - the pros and the

cons - rather than arguing only in their own favor

Humor. A number of studies found positive effects of humor on communicator

trustworthiness judgments but rarely on judgments of expertise

Familiarity. products that were familiar to users were helpful in establishing users trust

in recommender systems

Control. users showed more positive affective reactions to recommender systems

when they had increased control

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+ PATTERN 2: SET BEHAVIOREXPECTATIONS

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Our current expectation for digital systems is that theyll behave consistently

and the reasons for their behavior will be clear. Neither of these is true for theuser experience of predictive systems, which dont necessarily behave

identically in what appear to be similar circumstances, whose behavior

changes over time, and where the reasons for the behavior may not beobvious. If we undermine peoples confidence in a system by violating their

expectations, theyre likely to be disappointed and stop using it.

The first thing a predictive UX needs to do is to set peoples expectationsappropriately. It needs to explain the nature of the device, to describe it is

trying to predict, that its trying to adapt, that its going to sometimes be wrong,to explain how its learning, and how long itll take before it crosses over from

creating more trouble than benefit.

Recommender systems, such as Google Now, describe why a certain kind of

content was selected, and that sets the expectation that in the future thesystem will recommend other things based on other kinds of content youverequested. Nests FAQ explains that you shouldnt expect your thermostat to

make a model of when youre home or not until its been operating for a week

or so.

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From: Arnall, 2006

About ten years ago Timo Arnall and his students tried to address a

similar set of questions around interactions with RFID-enabled

devices by creating an iconography system that communicated

to potential users that these devices had functionality that was

invisible from the outside. Perhaps we need something like this

for behavior created by predictive analytics?

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+ PATTERN 3: EXPLAIN SEQUENCESWHEN A STATE HAS CHANGED OR IS

GOING TO CHANGE

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Predictive behavior is all about time, about sequences of activities.

Many predictive UX issues around expectations and uncertainty

have time as their basis: what were you expecting to happenand why. If it didnt happen, why? If something else happened,

or it happened at an unexpected time, why did that happen?

Knowing that a device has acted on your behalf, and that its going

to actand HOW its going to actin the future is important to

giving people a model of how its working, to set theirexpectations, to reduce the uncertainty. Nest, for example, has a

calendar of its expected behavior, and it shows when its acting

on your behalf to change the temperature, and when you can

expect that temperature will be reached.

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+ EXPLANATION COMPONENTS

What just happened?

Why did it happen?

What is going to happen next?

When will it happen?

What can I do?

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Explanations should not be technical, and they should not

overwhelm people with information, but a user should be able to

find out these things if they want. This should be prioritized

relatively low in the interfacethe Nests schedule is pretty

hiddenbut it should be clear and available for someone to

unpack what just happened.

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+ PATTERN 4: CREATE CLEAR WAYSFOR PEOPLE TO ADJUST THE

PREDICTIVE BEHAVIOR

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That last element in explanations is especially important, because it points to

the difficulty of adjusting the behavior of a machine learning system once

that system has built a model, and how important it is to give people

affordances for adjusting that model.

Remember the Wall Street Journal story from about 15 years ago about a guy

(Basil Iwanyk) who thought that his TiVo thought he was gay because of

the shows it recommended for him? Thats funny, but the second part isfrustrating: he didnt want it to think he was gay, so he tried to correct it theonly way he could think of, by clicking thumbs-up on what he thought were

the least gay things he could imagine, war movies. The TiVo then decidedhe was a Nazi (which he also wasnt). You have to give people a clear way

to teach the system and tell it when its model is wrong. Statistical systems,by definition, dont have simple rules that can be changed. There arentobvious handles to turn or dials to adjust, because everything is

probabilistic. If the model is made from data collected by several devices,which device should I interact with to get it to change its behavior? GoogleNow asks whether I want more information from a site I visited, Amazon

shows a explanation of why it gave me a suggestion. Mapping this to the

consumer IoT means way more explanation than were currently getting,which is either that a thing has happened, or it hasnt.

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+ PATTERN 5: SUPPORT PEOPLE,DONT REPLACE THEM

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Finally, dont automate. These systems shouldnt try to replace

people, but to support them, to augment and extent their

capabilities, not to replace them.

One of my favorite current examples of this kind of system is from

Meshfire, which is a social media management tool that has a

machine learning assistant. Its machine learning assistant,

called Ember, doesnt try to replace the social media manager.

Instead it manages the media managers todo list. It adds things

that it thinks are going to be interesting, deletes old things, and

reprioritizes the managers list based on what it thinks is

important. I think this is a good model for how such systems can

add value to a persons experience without creating a situation

where random, unexplained behaviors confuse people, frustrate

them and make them feel powerless. Ember is an augmentation

to the social media manager, it helps that person focus on

whats important so that they can be smarter about their

decisions. It doesnt try to be smarter than they are. How can

our devices HELP us, rather than trying to replace us?

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+ ANTIPATTERN: DONT MAKE PEOPLEDO ALL THE TRAINING, THEY WONT

OReilly Design 2016

1:11 1:26 1:36

1:47 1:59 2:25

2:38 2:57

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Finally, an antipattern: making people do all of the training, asking

them to identify whether a behavior is appropriate or not, should

be done selectively and infrequently. Yes, it will really help your

supervised models accuracy to have people identify the correct

positives from the false positives, but unless youre paying these

people, its incredibly annoying to have customers do it all the

time. Last Friday one consumer IoT product with a machine

learning system Im playing with asked me to classify its output

at 1:11PM, then again at 1:26, and again at 1:47 and again and

again. I think it was on roughly ten-minute sensing cycle, and at

every cycle it tried to make a decision, and asked me to verify it.

Im sure its still doing it, but I turned off all notifications from it,

and now Im considering turning it off entirely. People will

sometimes willingly act as sensors and actuators for your

system, but because they are not machines, they will not do it all

the time and youre just going to have to find a better way to

train your model.

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+ ITS NOT ABOUT THETHINGS

OReilly Design 2016

Finally, for me the IoT is not about the things, but the experience

created by the services for which the things are avatars.

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The wheel is an extension of the foot. The book is an extension of the eye. Clothing is an extension of the skin. Electric circuitry is an extension of the

central nervous system. Marshall McLuhan,

The Medium is the Massage, 1967

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Ultimately we are using these tools to extend our capabilities, to

use the digital world as an extension of our minds. To do that well

we have to respect that as interesting and powerful as these

technologies are, they are still in their infancy, and our job as

entrepreneurs, developers and designers will be to create systems,

services, that help people, rather than adding extra work in the

name of simplistic automation. What we want to create is a

symbiotic relationship where we, and our predictive systems, work

together to create a world that provides the most value, for the

least cost, for the most people, for the longest time.

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+ Thank you!

Mike [email protected]@mikekuniavsky

OReilly Design 2016

Thank you.

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when the cloud decides: designing for predictive machine learning for the iot (o'reilly design 2016)

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