SlideShare una empresa de Scribd logo
1 de 40
Building
smart
products
with ML
There will be
163 Zettabytes of
data by 2025.
IDC
And this data can
be used to help
save users time or
steps when using
your products.
Reduce Errors
5x faster
10% of mobile
replies
How do we build smart products?
Step 1: Pick the right problem
Step 2: Ask is AI the right way to solve….
Suggestions are
contextual
Suggestions are
contextual
Misspelled words can
already exist in dictionary
Suggestions are
contextual
Misspelled words can
already exist in dictionary
Suggestions are
constantly evolving
Grammar is much more subjective than
spelling
Professional linguists agree on grammar
suggestions 75% of the time (vs.
~100% for spelling)
Incorrect grammar Correct grammar
Our approach to grammar correction
Step 3: Build for user trust….
High Model Accuracy
Low Model Accuracy
High
User Intent
Low
User Intent
High Model Accuracy
Low Model Accuracy
High
User Intent
Low
User Intent
user asked
for it
(e.g. clicked a
button)
predict with
high
confidence
predict with
low
confidence
flip a coin
High Model Accuracy
Low Model Accuracy
High
User Intent
Low
User Intent
always
right
flip a coin
usually
right
sometimes
right
User Experience for Intelligent Features
Don’t build these.
High Model Accuracy
Low Model Accuracy
High
User Intent
Low
User Intent
High
confidence
answers we
know you
want.
Strong UX
You want
answers, but
we aren’t sure.
Fail gracefully
We’re pretty
sure you want
this, though
you haven’t
asked.
In-line, but
easy to
ignore
We’re not sure what you want,
but we think this might be useful.
Visible, but off to the side
User Experience for Intelligent Features
Don’t build these.
Focus Effort Here
High Model Accuracy
Low Model Accuracy
High
User Intent
Low
User Intent
User Experience for Intelligent Features
High Model Accuracy
Low Model Accuracy
High
User Intent
Low
User Intent
?
?
Step 4: Responsible AI practices.
1. Design your model using concrete goals for fairness and
inclusion
1. Design your model using concrete goals for fairness and
inclusion
2. Use representative datasets to train and test your model
1. Design your model using concrete goals for fairness and
inclusion
2. Use representative datasets to train and test your model
3. Check the system for unfair biases
1. Design your model using concrete goals for fairness and
inclusion
2. Use representative datasets to train and test your model
3. Check the system for unfair biases
4. Analyze performance
Step 5: Prototype
Step 6: Gather initial user feedback
Step 7: Scale user feedback
Step 8: Launch!!
Step 9: Start again
Thank you!
(especially Hilary!)

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