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ADV Slides: The Impact of Machine Learning on the Enterprise Today


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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.

Published in: Data & Analytics
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ADV Slides: The Impact of Machine Learning on the Enterprise Today

  1. 1. The Impact of Machine Learning on the Enterprise Today Presented by: William McKnight President, McKnight Consulting Group @williammcknight (214) 514-1444
  2. 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. 3. Machine Learning Changes Everything • Spreadsheet What ifs? – AI: What are the possibilities? – DL: Adds variables • Without coding
  4. 4. Song Selection SONGINTENSITY TEMPO
  5. 5. AI Whiskey 5
  6. 6. Daddy’s Car 6 <iframe width="560" height="315" src=" b05W7o" frameborder="0" allow="accelerometer; autoplay; encrypted- media; gyroscope; picture-in-picture" allowfullscreen></iframe>
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  9. 9. AI Acquisitions 9
  10. 10. Machine Learning • Supervised Learning – Data = features and a label • Unsupervised Learning – Data is unlabeled • Reinforcement Learning – Giving feedback to machine 10
  11. 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 11
  12. 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 12
  13. 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 13
  14. 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) 14
  15. 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 15
  16. 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. 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. 18. Disruption in Jobs Drivers Printers and publishers Cashiers Insurance adjusters Recruiters Radiologist Travel agents Manufacturing Organizers/Middlemen Food Service Bank tellers Military 18
  19. 19. Jobs That Will Thrive Robotics Big data Artificial intelligence E-sports DNA Scientist Virtual world design Cybersecurity Drone makers 19
  20. 20. Deepfakes 20
  21. 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 21
  22. 22. What to do about it • Benefit Distribution of ML • Eliminate Fear of Change • Disrupt Yourself
  23. 23. The Impact of Machine Learning on the Enterprise Today Presented by: William McKnight President, McKnight Consulting Group @williammcknight (214) 514-1444