Introduction to A.I.

1. 10 THINGS
EVERY ENTREPRENEUR
NEEDS TO KNOW ABOUT
ARTIFICIAL INTELLIGENCE

2. Lay a foundation for discussions around A.I.
Hopefully, spark some ideas around how to apply it.
And, build the case for starting now.
TODAY’S GOALS

3. #1: What it is
#2: Why you should care
#3: What powers it
#4: It’s here to stay
#5: What's different now
OUR JOURNEY
#6: How to think about it
#7: How it works
#8: Different approaches
#9: Where it’s used
#10: How to get started

4. #1: WHAT IS A.I.?

5. “The science of making machines do those things that
would be considered intelligent if they were done by
people.”
~ Marvin Minsky, ‘Father of Artificial Intelligence’
A NEBULOUS CONCEPT

6. So let’s dig in a little deeper...

7. #2: WHAT POWERS IT?

8. “Field of study that
gives computers the
ability to learn without
being explicitly
programmed.”
~ Arthur Samuel, 1959
MACHINE LEARNING
analyticsvidhya.com/blog/2015/07/difference-machine-learning-statistical-modeling

9. Training your computer to do stuff,
just like you would train a pet.
IN OTHER WORDS...

10. SIMILAR TO HOW WE LEARN
Data System Output
Model
Question Answer
Emotions
Mindset
Algorithm
The reference data pattern
(decision-making stuff)
Process the computer uses
to ‘learn’ the model
The model is built from
historical data Training data
Life experience
Perspective
Algoritm

11. AT THE END OF THE DAY...
Machine learning is all about pattern recognition.

12. AND...
A.I. is all about applying and combining machine
learning systems in creative and useful ways.

13. BUT STILL A MURKY LANDSCAPE
Artificial Intelligence
Machine Understanding (?)
Pattern recognition
Classification
Prediction
Can only do one thing
Brute-force approach
Autonomous decisions
Universally applicable
Intuition approach
Google DeepMind
Amazon Machine Learning
Natural language processing
Computer vision
Optimization
IBM Watson
Classic learning
Multi-tiered
deep learning
neural networks
Deep learning
neural network
Explicit ProgrammingHandwritten
Machine Learning
logiccomplexity

14. #3: WHY SHOULD I CARE?

15. SOFTWARE IS EATING THE WORLD
Everything is becoming code.
Software automates, simplifies, and
accelerates business.

16. BUT...
Someone needs to write the code/logic.

17. THE TRADITIONAL WAY
Handwritten logic.
If / Then / Else

18. THE PROBLEM
Complex situations require
complex2
software logic.

19. THE SOLUTION
Trained logic using historical data.
Model
tripID hasEggs eggsBought milkBought
1 1 6 1
2 0 0 2
3 1 6 1
4 1 8 1
5 0 0 1
6 1 6 1
7 0 0 3
0011001101011101
010110011101
0
11001001101
Stored as a
mathematical model.
Finds patterns in
the data.

20. WHAT IT LOOKS LIKE
console.aws.amazon.com/machinelearning/home?region=us-east-1#/datasources

21. A.I. IS EATING THE SOFTWARE
All applications are becoming “smart” — with
unprecedented complexity in logic.
Just like software automates, simplifies, and
accelerates business...
Artificial Intelligence automates, simplifies, and
accelerates software.

22. #4: IS IT HERE TO STAY?

23. As Everything is becoming software, it is fueled by...
● Limitless computing
● Limitless storage
● Limitless data (IoT = massive need)
● Deep learning
● Targeted machine learning SaaS (easy access)
WHY NOW?

24. Massive strides in the past couple of years.
Just in the past few months…
● Google open sources natural language processing
platform
● Amazon open sources deep learning platform
● Google announces quantum computing works
● IBM offers access to quantum computer
● Google’s DeepMind beats Go champion
WHAT’S NEW

25. WILL IT STICK THIS TIME?
The Internet gave us big data (greater need).
The cloud gave us massive computing (more horsepower).
And it’s getting much, much bigger…

26. BIG DATAx

27. MASSIVE COMPUTINGx
Google’s New Chip Is a Stepping Stone to Quantum
Computing Supremacy
The search giant plans to reach a milestone in computing
history before the year is out.
~Hartman Nevet
Head of Google’s Quantum AI Lab
via: technologyreview.com
via: researchgate.net

28. A.I. IS ON A PATH TO UBIQUITY
“The most profound technologies are those that
disappear. They weave themselves into the fabric of
everyday life until they are indistinguishable from it.”
~Mark Weiser
Scientific American, 1991

29. IN JUST 4 YEARS
Predicted for 2020...
● 13% of US households own consumer robots 1
(robotics)
● 30% of new cars will have a self-driving mode 2
(auto)
● 70% of mobile users access devices via biometrics 2
(security)
● We interact with 150+ smart devices (IoT) every day 2
(lifestyle)
All are underpinned by A.I.
1
roboticstrends.com/article/13_of_us_households_to_own_consumer_robots_by_2020
2
weforum.org/agenda/2015/02/5-predictions-for-technology-in-2020

30. ADDING FUEL TO THE FIRE
Think global.
tractica.com/newsroom/press-releases/artificial-intelligence-for-enterprise-applications-to-re
ach-11-1-billion-in-market-value-by-2024

31. THE GOLDEN AGE OF AI
We’ve hit the tipping point.
Watching AI get smarter is
like watching a bullet train.
The moment you see it
coming, it’s already blown
past you.

32. #5: WHAT'S DIFFERENT NOW?

33. DRIVEN BY BUSINESS
Since the 1950’s A.I. research has been driven by
academia.
Today, businesses are driving the research and
breakthroughs.
Which has helped flush out what works and what
doesn’t...

34. #6: HOW SHOULD
I THINK ABOUT IT?

35. MICRO A.I.
Just like software development focuses on
microservices, A.I. solutions should be focused on micro
vs. monolithic.
Academia was focused on... Business now focuses on...
General A.I. Narrow A.I.

36. I.A.
Use A.I. to build Intelligent Assistance products.

37. Artificial flowers have the same
relationship to natural flowers as
artificial intelligence has to
natural intelligence.
Useful but not the same.

38. #7: HOW DOES IT WORK?

39. IT’S ALL CLASSIFICATION
via: wjscheirer.com

40. “Features”
Points of differentiation within the data.
How would you teach a
child to recognize the
differences?
● Distance between eyes
● Width of nose
● Shape of cheekbones
● etc.
HOW DOES IT CLASSIFY?

41. “Probability”
Each potential
answer gets a
numeric
probability
calculated for it.
Higher probability
means greater
confidence.
HOW DOES IT MAKE DECISIONS?

42. ● Supervised learning — Labeled training data
● Unsupervised learning — Unlabeled training data
● Transfer learning — Applying aspects across models
● Reinforcement learning — Reward-based training
TRAINING
gym.openai.com

43. #8: WHAT ARE THE DIFFERENT
APPROACHES?

44. REMEMBER...
Data System Output
Model
Question Answer
Emotions
Mindset
Algorithm
The reference data pattern
(decision-making stuff)
Process the computer uses
to ‘learn’ the model
The model is built from
historical data Training data
Life experience
Perspective
Algoritm

45. Who wants to be a
data scientist?
ENDLESS ALGORITHMS
docs.microsoft.com/en-us/azure/machine-learning/machine-learning-algorithm-choice
machinelearningmastery.com/a-tour-of-machine-learning-algorithms

46. No “hidden”
layers
“CLASSIC” LEARNING
playground.tensorflow.org

47. 1-2 “hidden”
layers
“SHALLOW” LEARNING
playground.tensorflow.org

48. >2 “hidden”
layers
“DEEP” LEARNING
playground.tensorflow.org

49. (SIMPLE) NEURAL NETWORK
Each layer performs a
discrete function
≥ 1 input
neurons
≥ 1 output
neurons
≥ 1 hidden layers
Output “fires” if all
weighted inputs sum
to a set “threshold”
Each connection applies a
“weighted” influence on
the receiving neuron
Layers build on each other
(iterative)
Each input can
be a separate
“feature”
Each neuron takes in
multiple inputs
Hidden layers can’t directly
“see” or act on outside world
cs231n.github.io/neural-networks-1

50. HOW MUCH IS A HOUSE WORTH?
Decisions based on combinations.
3 bedrooms
37 years old
1450 ft2
$191,172
Is it “old” or “historic?”
Is it “small” or “open floor plan?”
$32,108 per bedroom
$64,251 per acre
Need a lower weight for “old”
Apply initial
abstractions
Set values
cs231n.github.io/neural-networks-1

51. #9: WHERE IS IT USED?

52. ENDLESS USES
● Classifying DNA sequences
● Economics
● Fraud detection
● Medical diagnosis
● Search engines
● Speech recognition
● Job search
● Spam filtering
● Risk prediction
● Visual product search
● Create art / music
● Industrial design
● Image caption generation
● Facial recognition
● Colorization of b&w images
● Adding sound to silent movies
● Language translation
● Image editing
● Vehicle navigation
● Error detection

53. IN THE WILD
Recommender
(pick from list)
Classifier
(binary)
Visual
recognition
(deep learning)

54. A.I. will drive
all of it.

55. #10: HOW DO I GET STARTED?

56. ● You don’t need a supercomputer
● You don’t need to write a ton of code
● You don’t need to invest massive amounts of time
● You don’t need a data science degree
● You don’t need to be a math whiz
● You don’t need mountains of data
MYTH BUSTING

57. ● Amazon Artificial Intelligence
● Google Cloud Machine Learning
● Microsoft Cognitive Services
● IBM Watson *
● DiffBot
* - PHP library is 3rd-party
SaaS OPTIONS

58. ● TensorFlow *
● Amazon DSSTNE *
● H2O *
● PredictionIO
● Apache Mahout
● Scikit Learn
● Caffe *
OPEN SOURCE OPTIONS
● Microsoft CNTK *
● Torch *
● Theano *
● MXnet *
● Chainer *
● Keras *
● Neon *
* ANN / Deep learning

59. ● archive.ics.uci.edu/ml
● deeplearning.net/datasets
● mldata.org
● grouplens.org/datasets
● cs.toronto.edu/~kriz/cifar.html
● cs.cornell.edu/people/pabo/movie-review-data
● yann.lecun.com/exdb/mnist (handwriting)
● kdnuggets.com/datasets/index.html (long list)
● image-net.org (competition)
OPEN SOURCE DATASETS

60. CLOSING

61. DON’T SWEAT THE MATH
Forget theory, just do it.

62. Intro blog posts:
● Artificial Intelligence 101 (the big picture)
● Machine Learning 101 (what you’ll actually use)
New ‘How to Apply A.I. in Your Business’ blog series:
● Voice-Powered Products w/ Amazon Alexa
● Predictive Social Media w/ IBM Watson(live)
● Image Recognition w/ Google Cloud
● Recommendation Engine w/ Microsoft Azure
GO DEEPER

63. UNLEASH YOUR BUSINESS
EMBRACE EXPONENTIAL
10xnation.com