The document discusses the backgrounds of Pai Charasika and Ray Tri, co-founders of a company focused on mobile apps and artificial intelligence. It provides an overview of AI terminology, including distinctions between artificial intelligence, machine learning, and neural networks, and explains the differences between supervised and unsupervised learning along with their applications. Additionally, it touches on how neural networks function and the advantage of deep learning with increased hidden layers.

1. Mobile Apps and
Artificial Intelligence
Ray Tri | CTO & Co-Founder
Pai Charasika | CMO & Co-Founder

2. Who are these guys?
Pai Charasika
 Wake Forest
 Business and Communications
 Financial Advisor
 Local Entrepreneur
 Salesman Extraordinaire
Ray Tri
 University of Louisville
 Organizational Leadership and Development
 Business Consultant
 Serial Entrepreneur
 Full Stack Developer

3. What in the world is Sippin?

4. Let’s talk AI
Intro to Artificial Intelligence

5. Terminology
 AI – Broadest term used to address all approaches
 Machine Learning – Using mathematical algorithms to learn and predict.
 Neural Network – Attempts to replicate the functions and structure of the
human brain to learn and predict.
 Deep Learning – A combination of machine learning and neural networking to
draw even more significant insights.
AI, Machine Learning, and Neural Networks
Key Takeaway: All of these approaches are DATA-DRIVEN

6. Early AI
Machine Learning vs. Rules-Based Programming
Google’s AlphaGo IBM’s Deep Blue
Chess:
• 1.40 x 107 legal positions
Go:
• 3.72 x 1079 legal positions
Deep Blue:
• Calculates 2 x 107 positions
per second
• Loop through all possible
plays to pick the best one
• Based on rules written by
programmers
AlphaGo:
• Impossible to loop through all
possible positions and pick
the best one.
• Instead uses a predictive
model to determine its next
move.
• Built its predictive model by
analyzing games of Go played
by experts.

7. Early AI
Machine Learning vs. Rules-Based Programming
Key Takeaway:
Google’s AlphaGo IBM’s Deep Blue
AlphaGo designed its own
predictive model to make
decisions
Programmers wrote the
rules Deep Blue used to
make decisions

8. Types of Learning
Supervised vs. Unsupervised
Data
Data
Data
Supervised Unsupervised
• A human selects
the specific data it
learns from
• No pre-selected data is
provided, but it is
grouped into classes.
ModelInput Output
Data
Data
Data
Analysis
Output

9. Applications of Supervised Learning
What problems can it solve?
Classification
• Classification problems involve real-world situations in which there is a need to
determine an outcome from a set of non-continuous outcomes based on a set of
factors.
Example: Should I golf today?
Predictive
Classification
Model
Pre-selected “Training” Data Input “Live” Data
Outcome: Yes

10. Applications of Supervised Learning
What problems can it solve?
Regression
• Regression problems involve real-world situations in which there is a need to
determine an outcome from a set of continuous outcomes based on a set of
factors.
Example: Probability a patient has a second medical incident.
Predictive
Regression
Model
Pre-selected “Training” Data Input “Live” Data
Second Attack: 38%

11. Applications of Unsupervised Learning
What problems can it solve?
Clustering / Anomalous
• Clustering / Anomalous problems involve finding and identifying data or patterns
that are significant (or abnormal / deviating from the norm)
Example: What do people buy?
Machine
Learning
Analysis
All Available Data
Outcome: If people buy diapers,
they probably buy beer.

12. Neural Networks
What is a Neural Network?
 A Neural Network
attempts to
replicate the
functions and
structure of the
human brain in
programming to
learn and predict. Inputs
Inputs are
weighted
The
“decision”
happens
An output is
sent to the
next neuron

13. Neural Networks
Scale it up!
 A Neural Network
passes inputs to
many neurons, which
pass their outputs to
hidden layers to do
further calculations.
Finally, there is an
output layer which
gives us our end
result.
“Back Propagation” is a means by which a Neural Network modifies its
own weights (learns) based on whether its output was correct or not.
This is a very complex subject that we will not get into today.

14. Neural Networks
Let’s look at one!
 I want to know:
 How well I will do on a test (y)
 This is based on two factors:
 How many hours I slept the night before (x1)
 How many hours I spent studying for the test (x2)
x1
x2
x1w1
11
+
x2w1
21
x1w1
12
+
x2w1
22
x1w1
13
+
x2w1
23
w1
11
w1
12
w1
13
w1
21
w1
22
w1
23
a1w2
11
+
a2w2
12
+
a3w2
13
w2
11
w2
13
w2
12
Let’s call the
output from
this layer “a”
y
1
1 + 𝑒−𝑥
1
1 + 𝑒−𝑥
1
1 + 𝑒−𝑥
1
1 + 𝑒−𝑥

15. Deep Learning Neural Networks
Pump your neural network up!
 A Deep Learning Neural Network has many, many more
hidden layers that are used for computation. This gives it
the added advantage of being able to perform more and
more tasks as more and more layers are added.

16. Conclusion
Let the questions begin