AI will augment our humanity—it will give each of us “superpowers” to meet challenges of all kinds—including some of our biggest ones.. All around the world, the Artificial Intelligence and Research team is leading the way, working to fulfill our mission—to empower every person and every organization on the planet to achieve more—with AI.

1. Artificial
Intelligence
Why it matters
Ashish Jaiman
@ashishjaiman
linkedin.com/in/ashishjaiman

2. Human
Innovation

3. What is AI

5. Demo
https://translator.microsoft.com/
https://azure.microsoft.com/en-us/services/cognitive-services/
https://azure.microsoft.com/en-us/services/cognitive-services/speech-to-text/
https://azure.microsoft.com/en-us/services/cognitive-services/directory/vision/

6. Why AI
Matters

7. Video

8. Cloud

9. What is learning
Machine learning is a way to try to make machines intelligent by allowing computers to learn from
examples about the world around us or about some specific aspect of it.
Deep learning is an approach to machine learning, particular among all the machine learning methods in
that it is inspired by some of the things we know about the brain. It’s trying to make computers learn
multiple levels of abstraction and representation, which is presumably what makes these systems so
successful
Reinforcement learning is a type of machine learning where the learner doesn’t get to know what a human
would do in this context. The learner only gets to see if the actions were good or bad after a long set of
actions. A lot of the recent progress in this area is in things like playing games, but reinforcement learning
probably is going to be very important for things like self-driving cars.
Transfer Learning (Inductive transfer), is a research problem in machine learning that focuses on storing
knowledge gained while solving one problem and applying it to a different but related problem.

11. Machine Learning Is the crux
Machine
learning
algorithm
Model
Application
Data
Contains
patterns
Finds
patterns
Recognizes
patterns
Provides new data to
see if it matches
known patterns

12. Which means, two types of learning
Training data
The prepared data used to
create a model
Creating a model is called
training a model
Supervised learning
The value you want to
predict is in training data
The data is labeled
You use classification to
get to prediction
Unsupervised learning
The value you want to predict is
not in the training data
The data is unlabeled
You are trying to find groupings
in your data
The most common
approach

13. How do you do “learning” in your own mind
when you want to pick a flight to Seattle?
Flight How close is arrival to
when I need to get
there (hours)
How much
does it cost
I ‘m in their
frequent flyer
program
I’ll take it
AC360 4 $288 N N
LV666 1 $650 Y N
AJ555 2 $289 Y Y
AH444 5 $299 Y N
I chose AJ555 What was my thought process?

14. My mental model
Cost
Arrival Time
Frequent Flyer
YESNO
NO
NO
>$500 Yes
>4 hrs<=4 hrs
No Yep
Model: Decision Tree
Splitting Attributes
Flight How close to
when I need to
get there (hours)
How
much
does it
cost
I ‘m in their
frequent
flyer
program
I’ll take it
AC360 4 $288 N N
LV666 1 $650 Y N
AJ555 2 $289 Y Y
AH444 5 $299 Y N

15. What Happens in ML? The same!
Flight How close to
when I need to get
there (hours)
How
much
does it
cost
I ‘m in their
frequent
flyer
program
I’ll take it
AC360 4 $288 N ?
LV666 1 $650 Y ?
AJ555 2 $289 Y ?
AH444 5 $299 Y ?
Flight How close to
when I need to
get there (hours)
How
much
does
it cost
I ‘m in their
frequent flyer
program
I’ll take it
AA245 4 $300 N N
ALA132 1 $538 Y N
SW585 2 $129 Y Y
ELA44 5 $659 Y N
BS222 6 $125 N N
… … … … …
Learning Set (training)
Testing Set (training)
Learn
Model
Learning Algorithm
Apply
Model
My
Fabulous
Models

16. Deploy
chosen
model
Chosen
Model
Apply
learning
algorithm
to data
Candidate
Model
The Machine Learning Process
Prepared
Data
Apply pre-
processing
to data
Iterate to find the
best model
Data
Preprocessing
Modules
Iterate until data
is ready
Preprocessing
Modules
Machine
Learning
Algorithms
Applications
The goal:
Smarter
applications
Raw
Data
Raw
Data
Choose
data

17. switch
case
case
case
// Need to list every dog type :(
animaltype = "dog”;
break
// Managing different poses, ouch
// What about dogs that I missed?
// How about other animals?
// This is tedious
default
break

18. Styles of Machine Learning Algorithms Examples
Decision tree Neural network Bayesian K-means
P(A) P(B|A)
P(B)
P(A|B) =
Deep learning
uses this

20. The Microsoft AI platform
Services
Infrastructure
Tools

24. AI must maximize efficiencies without destroying the dignity of people1
AI must guard against bias2
AI needs accountability so humans can undo unintended harm3
AI must be transparent4
AI must be designed for intelligent privacy5
AI must be designed to assist humanity6

25. Thank You
AI for ALL
https://learn.microsoft.com/activity/S1443002/launch#/
AI School
https://aischool.microsoft.com/learning-paths