Rise of the Machines: What Marketers Need to Know about Machine Learning
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Originally presented by Andrew Ruegger at Insight Exchange Network's Marketing Metrics & Analytics Summit, this presentation discusses how marketers can leverage machine learning.
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Rise of the Machines: What Marketers Need to Know about Machine Learning
- 1. Andrew Ruegger Senior Partner, Head of Data Science – GroupM - Catalyst RISE OF MACHINES What Marketers Need to Know About Machine Learning
- 2. www.CatalystDigital.com @CatalystSEM @ARuegger AI is neither ‘Artificial’ nor ‘Intelligent’
- 3. www.CatalystDigital.com @CatalystSEM @ARuegger What is Machine Learning?
- 6. www.CatalystDigital.com @CatalystSEM @ARuegger How Does One Get a Machine to Learn?
- 7. www.CatalystDigital.com @CatalystSEM @ARuegger It’s Not Magic. It’s Math. Which visually looks like this
- 8. www.CatalystDigital.com @CatalystSEM @ARuegger Types of Learning
- 9. www.CatalystDigital.com @CatalystSEM @ARuegger 5 Approaches
- 10. www.CatalystDigital.com @CatalystSEM @ARuegger 5 Approaches to Machine Knowledge Acquisition Symbolists Connectionists Evolutionaries Bayesian Analogizers
- 11. www.CatalystDigital.com @CatalystSEM @ARuegger Symbolists Inverse deduction starts with some premises and conclusions, work backwards to fill in the gaps
- 12. www.CatalystDigital.com @CatalystSEM @ARuegger Symbolist – Decision Trees
- 13. www.CatalystDigital.com @CatalystSEM @ARuegger Symbolists – Random Forest
- 14. www.CatalystDigital.com @CatalystSEM @ARuegger Connectionists Reverse engineer the brain by building artificial neurons. It learns by adjusting the strengths between neurons based on errors seen within the collection.
- 17. www.CatalystDigital.com @CatalystSEM @ARuegger Connectionists Microsoft Image Recognition 2015
- 18. www.CatalystDigital.com @CatalystSEM @ARuegger Evolutionaries Simulate the evolutionary process by applying the idea of genomics to data structures.
- 20. www.CatalystDigital.com @CatalystSEM @ARuegger Bayesian Improve hypothesis from probabilistic inference using Bayes theorem.
- 22. www.CatalystDigital.com @CatalystSEM @ARuegger Analogizers Make contrasts between old and new sets of information. “All Intelligence is nothing but analogy” – Douglas Hofstadter
- 23. www.CatalystDigital.com @CatalystSEM @ARuegger K-Means: Locational Boundaries
- 24. www.CatalystDigital.com @CatalystSEM @ARuegger There Are a Lot of Different Ways Machines Can “Learn”
- 25. www.CatalystDigital.com @CatalystSEM @ARuegger All models are wrong, but some are useful.
- 27. www.CatalystDigital.com @CatalystSEM @ARuegger AI Symbolists AI Inverse Deduction Accuracy Logic The Master Algorithm, Pedro Domingos, University of Washington
- 38. www.CatalystDigital.com @CatalystSEM @ARuegger Retail Customers Seller Catalog Digital • Demand Forecasting • Vendor Lead Time • Pricing • Packaging • Substitute Prediction • Recommendations • Product Search • Visual Search • Product Ads • Shopping Advice • Problem Detection • Fraud Detection • Predictive Help • Seller Search • Seller Crawling • Browse Node Classification • Meta-data Validation • Review Analysis • Hazmat Prediction • Name Entity Extraction (x-ray) • Plagiarism Detection • Speech Recognition • Alexa Knowledge
- 39. www.CatalystDigital.com @CatalystSEM @ARuegger Data: 360 Degree Images Weights Producer Arrival Training Set: QC Specialists Classify On Days Until Expiration Machines 98% as Accurate as People (2016)
- 43. 617-663-1247 | www.CatalystDigital.com © 2017 Catalyst | All Rights Reserved Andrew Ruegger Head of Data Science THANK YOU!
Editor's Notes
- They all think they are right, that their method is the way to learn everything with enough data and computational power
- Symbolists - Instead of starting with the premise and looking for the conclusions, inverse deduction starts with some premises and conclusions, and essentially works backward to fill in the gaps. The system has to ask itself “what is the knowledge that is missing?” and acquire that knowledge through analysis of existing data sets.
- mimicking the human brain, building artificial neurons on AP related to s curves. Google are applying it to areas like vision and image processing, machine translation and experimental neural networks like Google’s Cat Network that helps the computer to recognize cat images.
- 1964 <<<<ALGORITHM >>>> Genetic Programming - makes an evolves computer programs in the same way nature evolves biologically Survival of the Fittest & Variation Evolutionaries - simulating the evolutionary process evolutionaries are applying the idea of genomes and DNA in the evolutionary process to data structures. The survival and offspring of units in an evolutionary model are the performance data.
- 1964 <<<<ALGORITHM >>>> Genetic Programming - makes an evolves computer programs in the same way nature evolves biologically Survival of the Fittest & Variation Evolutionaries - simulating the evolutionary process evolutionaries are applying the idea of genomes and DNA in the evolutionary process to data structures. The survival and offspring of units in an evolutionary model are the performance data.
- Bayesians – hot stove - apply a type of “a priori” thinking, believing that there will be some outcomes that are more likely. They then update a hypothesis as they see more data. After several iterations, hypotheses become more likely than others. -<<<<ALGORITHM >>>> Bayes THEORUM and Derivates <<<<< incorporate new information into out beliefs.
- Bayesians – hot stove - apply a type of “a priori” thinking, believing that there will be some outcomes that are more likely. They then update a hypothesis as they see more data. After several iterations, hypotheses become more likely than others. -<<<<ALGORITHM >>>> Bayes THEORUM and Derivates <<<<< incorporate new information into out beliefs.
- Analogizers - making contrast between old and new sets of information. one of the leading proponents of this method, Douglas Hofstadter, in saying that “all intelligence is nothing but analogy.”The master algorithm here, he says, is the “nearest neighbor” principle. Nearest neighbor outcomes can give results that are similar to neural network models. The example of two country models with defined city locations, but with undefined borders. Through the application of the analogy principles, the computer generates a likely border. this “generalizing from similarity” and suggests that it has economic ramifications for technology. One example, he says, is the movie advice technologies that supply movie ratings based on known data sets, where users get recommendations based off of what others have watched previously. <<<<ALGORITHM >>>> Support vector Machine - figures out which experiences to remember, and how to combine them to make new predictions.
- Analogizers - making contrast between old and new sets of information. one of the leading proponents of this method, Douglas Hofstadter, in saying that “all intelligence is nothing but analogy.”The master algorithm here, he says, is the “nearest neighbor” principle. Nearest neighbor outcomes can give results that are similar to neural network models. The example of two country models with defined city locations, but with undefined borders. Through the application of the analogy principles, the computer generates a likely border. this “generalizing from similarity” and suggests that it has economic ramifications for technology. One example, he says, is the movie advice technologies that supply movie ratings based on known data sets, where users get recommendations based off of what others have watched previously. <<<<ALGORITHM >>>> Support vector Machine - figures out which experiences to remember, and how to combine them to make new predictions.
- One day, a female teenager’s father received a Target mailers addressed to his daughter offering her discounts on variety of baby supplies The father went to target and complained to the local store and their corporate organization. After doing so the daughter admitted to her father that she was in fact pregnant and had not figured out how to tell him. Target brilliantly leverages the connected data from their Target credit card and the purchase behavior and online activity associated with that person. The modeling started to notice highly correlated sequential product purchases. When the beginning behavior pattern was witnessed by their system, baby mailers are automatically shipped to the name and address associated with the card, and behavior. They also became aware of the near certainty that individuals who buy Diapers, also buy Beer.
- Highest cost -> Overhead cost of employees for Amazon Fresh –Amazons Food Delivery Service Perishable goods require inspection to ensure expired items are not delivered to customers. Inspection requires Human Inspectors.. Is there a quantitative to measure a the degradation of produce? Amazon created a data set of 10,000 strawberries to train machines to identify how many days a strawberry had until it would expire (between 1 – 10 days) Collected weight, volume, density, 360 imaging to gauge color, grow location, arrival date, and a handful of other metrics. Had their Quality Assurance inspectors, classify the same strawberry’s on shelf life (1 day to expiration to 10 days to expiration) = The classification Field Then used Random Forrest (Symbolist) machine learning algorithm to predict how long the life of strawberry would be. After 2 months of running 1M new strawberries through the model and adjusting some weights, the machines could predict the expiration date with 99.9% accuracy. 1.7% higher than their Quality Assurance Experts. Amazon used Free opensource software to accomplish this.
- Highest cost -> Overhead cost of employees for Amazon Fresh –Amazons Food Delivery Service Perishable goods require inspection to ensure expired items are not delivered to customers. Inspection requires Human Inspectors.. Is there a quantitative to measure a the degradation of produce? Amazon created a data set of 10,000 strawberries to train machines to identify how many days a strawberry had until it would expire (between 1 – 10 days) Collected weight, volume, density, 360 imaging to gauge color, grow location, arrival date, and a handful of other metrics. Had their Quality Assurance inspectors, classify the same strawberry’s on shelf life (1 day to expiration to 10 days to expiration) = The classification Field Then used Random Forrest (Symbolist) machine learning algorithm to predict how long the life of strawberry would be. After 2 months of running 1M new strawberries through the model and adjusting some weights, the machines could predict the expiration date with 99.9% accuracy. 1.7% higher than their Quality Assurance Experts. Amazon used Free opensource software to accomplish this.
- Highest cost -> Overhead cost of employees for Amazon Fresh –Amazons Food Delivery Service Perishable goods require inspection to ensure expired items are not delivered to customers. Inspection requires Human Inspectors.. Is there a quantitative to measure a the degradation of produce? Amazon created a data set of 10,000 strawberries to train machines to identify how many days a strawberry had until it would expire (between 1 – 10 days) Collected weight, volume, density, 360 imaging to gauge color, grow location, arrival date, and a handful of other metrics. Had their Quality Assurance inspectors, classify the same strawberry’s on shelf life (1 day to expiration to 10 days to expiration) = The classification Field Then used Random Forrest (Symbolist) machine learning algorithm to predict how long the life of strawberry would be. After 2 months of running 1M new strawberries through the model and adjusting some weights, the machines could predict the expiration date with 99.9% accuracy. 1.7% higher than their Quality Assurance Experts. Amazon used Free opensource software to accomplish this.
- Highest cost -> Overhead cost of employees for Amazon Fresh –Amazons Food Delivery Service Perishable goods require inspection to ensure expired items are not delivered to customers. Inspection requires Human Inspectors.. Is there a quantitative to measure a the degradation of produce? Amazon created a data set of 10,000 strawberries to train machines to identify how many days a strawberry had until it would expire (between 1 – 10 days) Collected weight, volume, density, 360 imaging to gauge color, grow location, arrival date, and a handful of other metrics. Had their Quality Assurance inspectors, classify the same strawberry’s on shelf life (1 day to expiration to 10 days to expiration) = The classification Field Then used Random Forrest (Symbolist) machine learning algorithm to predict how long the life of strawberry would be. After 2 months of running 1M new strawberries through the model and adjusting some weights, the machines could predict the expiration date with 99.9% accuracy. 1.7% higher than their Quality Assurance Experts. Amazon used Free opensource software to accomplish this.