Despite the dramatic changes we have seen in business recently, another level of change looms. We are headed toward a future permeated with artificial intelligence and machine learning (ML), where machines take on more of the work people have traditionally done, and then some. The potential for ML is enormous. We are at the dawn of a whole new era of intelligent devices that will revolutionize our business and personal worlds. Corporations wishing to lead with AI/ML should make plans now to establish their initiatives and their technology framework and nurture the necessary skills.

1. The Impact of Machine
Learning on the
Enterprise Today
Presented by: William McKnight
President, McKnight Consulting Group
@williammcknight
www.mcknightcg.com
(214) 514-1444

2. Setting the Context
• AI
– Broad concept
– Smart machines
– Applied and General
• ML
– Subset of AI
– ”Learning by example”
– Days to seconds
– Enabling machines to make decisions informed by data
– Model-based
• Close to “thinking”: Turing Test
– Letting machines learn
– Fueled by Data
– Deep Learning is a further subset – many layered AI
– Neural Networks
• Key to teaching ML
• Classifies information
• Simulating human
– Shiny new term
• Natural Language Processing – Another field of AI

3. Machine Learning Changes Everything
• Spreadsheet What ifs?
– AI: What are the possibilities?
– DL: Adds variables
• Without coding

4. Song Selection
SONGINTENSITY
TEMPO

5. AI Whiskey
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https://techcrunch.com/2019/05/15/oh-no-theres-a-i-whiskey-now/

6. Daddy’s Car
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9. AI Acquisitions
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10. Machine Learning
• Supervised Learning
– Data = features and a label
• Unsupervised Learning
– Data is unlabeled
• Reinforcement Learning
– Giving feedback to machine
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11. Supervised Learning: Regression
• Regression model looks at features and
output score (i.e., price of house)
– Continuous prediction space
– Error defined as distance between prediction
and actual
– Linear, Polynomial, Logistic Regression
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12. Supervised Learning: Classification
• Like Regression with format of prediction
different
• Classification model predicts outcome
• Will be as good as the data and the labels
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13. Unsupervised Learning
• Pattern seeking algorithms
– Find the underlying patterns rather than the
mapping
• K-Means Clustering
– Find groups which have not been explicitly
labeled in the data
– Use domain knowledge of the dataset
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14. Reinforcement Learning
• Algorithm reacts to environment
• There are States, actions, reward, policy,
value
• In complex problems where there are tens
of thousands of moves that can be played,
creating a knowledge base (if this, do this) is
a tedious task (i.e., chess)
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15. Machine Learning Algorithms
• Naive Bayes Classification
• Decision Tree
• Ordinary Least Squares Regression
• Logistic Regression
• Linear Regression
• Naïve Bayes
• K-Nearest Neighbors
• Learning Vector Quantification
• Support Vector
• Random Forest
• Boosting
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16. Enhance in-car navigation
using computer vision
Reduce cost of handling
misplaced items
improve call center
experiences with chatbots
Improve financial fraud
detection and reduce costly
false positives
Automate paper-based,
human-intensive process
and reduce Document
Verification
Predict flight delays based
on maintenance records and
past flights, in order reduce
cost associated with delays
ML in Action in the Enterprise

17. ML Business Use Case Examples
• Marketing – segmentation analysis, campaign effectiveness
• Cybersecurity – proactive data collection and analysis of threats
• Smart Cities – track vehicle movements, traffic data, environmental factors
to optimize traffic lights, ensure smooth flow and manage tolling
• Retail, Manufacturing – Supply flow, Customer flow
• Oil and Gas - determine drilling patterns, ensure maximum utilization of
assets, manage operational expenses, ensure safety, predictive
maintenance
• Life Sciences – study human genome (100s MB/person) for improving
health

18. Disruption in Jobs
Drivers
Printers and publishers
Cashiers
Insurance adjusters
Recruiters
Radiologist
Travel agents
Manufacturing
Organizers/Middlemen
Food Service
Bank tellers
Military
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19. Jobs That Will Thrive
Robotics
Big data
Artificial intelligence
E-sports
DNA Scientist
Virtual world design
Cybersecurity
Drone makers
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20. Deepfakes
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21. Machine Learning Ethics
• Elon Musk: “AI is our biggest threat”
• Weapons
• Bias
• Generating Training Data
• Transparency
• Fake News
• Jobs
• Surveillance
• Birth traits
• AI Rights
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22. What to do about it
• Benefit Distribution of ML
• Eliminate Fear of Change
• Disrupt Yourself

23. The Impact of Machine
Learning on the
Enterprise Today
Presented by: William McKnight
President, McKnight Consulting Group
@williammcknight
www.mcknightcg.com
(214) 514-1444