Understand concepts around Deep Learning, Machine Learning, Pattern Recognition and more. See AEM scenarios powered with Adobe Sensei. Understand the latest roadmap on AEM and Sensei.

1. #evolve19
AI / MACHINE LEARNING &
ADOBE SENSEI
Brett Butterfield – Adobe Sensei
Giancarlo F. Berner – Kleiber Digital
August 6 - 2019

2. #evolve19 2
SESSION OVERVIEW
• Introduction into AI/Machine Learning
• Live Demo of AI Techniques, powered
by Adobe Sensei

3. #evolve19 3
WHAT IS ARTIFICIAL INTELLIGENCE?
Artificial Intelligence is the concept and process of defining
and building systems that act like humans, think like
humans, learn like humans and decide like humans.
G.F.Berner - 1983
(Today I want to add “and perform tasks like humans”)

4. #evolve19 4
1956 – AI workshop at Dartmouth College (NH) – Beginning of AI research
1956 – 74 – Reasoning Algorithms (games), Natural Language, Robotics
1980 – 87 – Expert Systems, Knowledge Base, Pattern Recognition
1993 – 2011 – Deep Blue, Autonom. Driving, Robotics, Speech Recognition, Search
Engines, Cognitive Systems, Computational Intelligence
2011 – today – Machine/Deep Learning, Big Data, Speech/Image Recognition
1983 – Work on USA – Universal System Analyzer (Digital Analysis System)
1985 – Matrix Matching Concepts (OCR, Image)
1987 – Work on Dr. Hey – Expert System for Pharmacies
1989 – Analysis of Chess Algorithms
A LITTLE HISTORY

5. #evolve19 55
AI
Machine Learning Deep Learning
Expert Systems
Knowledge Base
Pattern Recognition
Image Recognition
Robotics
Speech Recognition
Animation
Simulation
Diagnosis
Analyzers
Games
Predictions

6. #evolve19 6
MACHINE LEARNING - DEFINITION
Machine Learning is the practice and science of building
systems that continuously improve the outcome of a
process, not by modifying the code, but by processing more
and better data.
G.F.Berner - 1992

7. #evolve19 7
Common Styles
• Supervised Learning – A model’s mapping functions are trained by labelling the input
data and knowing the output data.
• Unsupervised Learning – The model only knows the input variables, but not the
corresponding output variables. Used e.g. to structure the data (“similarities”).
New Styles
• Reinforcement Training – “Trial & Error”, decide the best next action (“what gives most
points”).
MACHINE LEARNING STYLES
COMMON AND NEW LEARNING STYLES

8. #evolve19 8
Machine Learning Style
• Classification – Predict the outcome if the output variables are in form of categories,
e.g. input label is “headache”, output is “healthy” or “sick”.
• Regression – Predict the outcome if the output variables are in form real values, e.g.
predict the age of a person.
Popular Algorithms
• Linear Regression (determine output variable of an input and quantify relationship)
• Logistic Regression (transformation function to determine probability)
• Naïve-Bayes (probability that an event will occur based on past events)
• CART (Classification and Regression Trees)
• KNN (K-Nearest Neighbors)
SUPERVISED LEARNING
FORMS AND ALGORITHMS

9. #evolve19 9
Machine Learning Style
• Association – Find probability of co-occurrence of items in a data set. Popular in E-
Commerce, e.g. “Buy a printer, there is 85% probability to buy toner”.
• Clustering – Group samples so that objects in the same cluster are more similar than to
objects of another cluster.
• Dimensionality Reduction – reduce the number of variables of a data set but still
maintaining the relevant information. Feature Extraction and Feature Selection methods
Popular Algorithms
• Apriori (transactional database, popular for commerce)
• K-means (iterative algorithm, groups similar data into clusters)
• Principal Component Analysis (PCA) (Feature Extraction)
UNSUPERVISED LEARNING
FORMS AND ALGORITHMS

10. #evolve19 10
DEMO ADOBE SENSEI
MACHINE LEARNING & AI
• Voice Search
• Image Recognition
• ~ 10 more Live Demos
Brett Butterfield – Director Adobe Sensei

11. #evolve19
THANK YOU!
Brett Butterfield – brettb@adobe.com
Giancarlo Berner – gberner@kleiber-digital.com