A Deep Look into AI and its Impact on Programmatic Advertising Learn about why AI is important for the advertising industry, and the tremendous impacts it has on marketing and consumers. o What is AI? There are a lot of misconceptions regarding what it is and a lot of deception around it as well. We'll clear that up and separate the pretenders from the contenders. o When to use AI? We'll discuss the ideal use cases for investing in AI technologies and specific examples in marketing. o A Case Study in Advertising. Yang will take you step by step through how StackAdapt solved a particular problem -- starting with a basic algorithm to a full-fledged AI powered system. o What the future looks like. How close are we really to killer robots? In order for AI to evolve further, there are challenges that need to be solved.

1. Yang Han
CHIEF TECHNOLOGY OFFICER,
STACKADAPT
TORONTO, ON ~ MAY 17 – 18, 2018 | DIGIMARCONCANADA.CA
#DigiMarConCanada
AI & Programmatic
Advertising Master Class
MASTERCLASS

2. Part 1. What is AI and how it works
Part 2. AI and Business
Part 3. Case Study in Advertising
Part 4. AI in the Future
Agenda

3. Part 1
What is AI

6. • Adaptable
• Automated
• Consistent
• Solves the problem
(better than a human)
What is AI

7. A lot of data (Clean data)
What Goes into AI?

8. How it works

10. Examples of AI

11. Neural Networks
● Part-of-speech Tagging
● Tokenization
● Named Entity Recognition
● Intent Extraction
Recurrent Neural Networks
● Machine Translation
● Question Answering System
● Image Captioning
Recursive Neural Networks
● Parsing sentences
● Sentiment Analysis
● Paraphrase detection
● Relation Classification
● Object detection
Convolutional Neural Network
● Sentence / Text classification
● Relation extraction and
classification
● Spam detection
● Categorization of search queries
● Semantic relation extraction
Deep Learning Techniques

12. Foundations: Image recognition, Voice recognition, Natural language
processing (NLP), Online user behavior
Utilized to create a wide range of applications:
• Automated Content Creation
• Automated Suggestions
• Content Personalization
• Ad Personalization, Targeting, & Monetization
• Chatbots, Smart assistants, Q&A
• Concept extraction from text and images
• Sentiment analysis
• Dynamic A/B testing
Keywords: Personalized, Automated, and Humanless
AI in Marketing

13. • Data can be biased
• AI doesn’t know the broader world
• Complex models do not always equal to better results
• AI can only do what you train it to do
• Most of AI is a black box
“A robot can learn to pick up a bottle, but if it has to pick up a cup,
it starts from scratch.”
“We can build these models, but we don’t know how they work”
AI Challenges

14. Part 2
AI in Business

15. Building AI vs. Applying AI

16. • Has the problem been solved already?
• Can I solve the same problem without ML?
• Do I have the data and resources?
When to Build AI?

17. Data Engineer
Build data pipelines for training, and model application
Data Scientist
Model training, testing, and validation
Hiring for AI

18. Part 3
A Case Study in Advertising

19. How ads are brought and sold online

20. Machine learning is used to determine which users to show specific ads
to, and how to deliver the message
• Maximizing an ad’s click-through-rate, time on site, and conversions
• By understanding the user and who they are
• Dynamically creative variations (headlines/imageneratingges) that
provide optimized and personalized messaging
How do we leverage AI?

21. Machine learning pipeline that analyzed billions of data points to predict
user actions such as clicks and engagements
Custom Segments
NLP to understand what 1 billion users are reading about in real-time
Thompson Sampling
A method of automatically selecting bidding strategies, to improve the
performance of campaigns.
Age/Gender prediction
Cross Device prediction
What we built

22. Old Algorithm
Machine Learning Algorithm
Newest Algorithm
(Not yet released)

24. User profiles based on browsing history of over 1 billion online users
Stackadapt User Profile

25. • Platforms require the marketer to understand who to target,
and how to communicate the message
• Platforms track and identify actions (ads clicked, sites visited,
products purchased)
• Machine learning uses actions as data points, in addition to
current attributes of a user (demographics, location, device,
keywords on the page)
• Optimizations result in amplifying your current actions. This is
reactive.
Advertising today

26. • Platforms will play a bigger role in automating messaging and targeting
• Platforms will harness massive sequences of user actions and context to
understand the individual and their behavior
• Machines will be able to predict upcoming intent that seem unrelated to
current actions. This is predictive.
• Machines will know if you’re a customer, even before you do
How it will work tomorrow

27. Part 4
AI in the Future

28. • The easiest problem is clear goals in a predictable environment.
Example: Image recognition
• A harder problem is clear goals in an unpredictable environment.
Example: Self driving cars, Video
• Another hard problem is indirect goals in a predictable environment.
Example: Winning at a board game.
• Hardest case: undefined or distant goals in an unpredictable
environment. These can’t be solved by current AI at all.
Current State of AI

29. How did image recognition become possible?
Synergies: the foundation of AI evolves with
• Evolution of Data
Today’s examples: IoT, Sensors, Blockchain
Ecosystems and interactivity are key
• Computing Power
Enterprises & industries evolve by applying AI
Evolution of AI

30. Human vs AI

31. Thank you
Q&A