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The Learning Machine

The Learning Machine

Artificial Intelligence, in the form of Machine Learning (ML), has already transformed medicine, retail sales, and other industries. It is about to enter all our lives, whether we want it or not! In this session, we will quickly develop a basic understanding of AI, and its advantages, applications, and difficulties.

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J. Scott Christianson

September 15, 2020
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Transcript

  1. Meet the Learning Machine J Scott Christianson Associate Teaching Professor

    How Artificial Intelligence is transforming our world!
  2. Artificial Intelligence (AI) Definitions Artificial Intelligence: A machine that exhibits

    cognitive or decision-making behavior and can take action to achieve a goal.
  3. • General AI: A machine that can reason and adapt

    like a human. E.g, sci fi movies. Artificial Intelligence (AI) Definitions Artificial Intelligence: A machine that exhibits cognitive or decision-making behavior and can take action to achieve a goal.
  4. • General AI: A machine that can reason and adapt

    like a human. E.g, sci fi movies. Artificial Intelligence (AI) Definitions • : A machine that is optimized for a particular task or project. Narrow AI Artificial Intelligence: A machine that exhibits cognitive or decision-making behavior and can take action to achieve a goal.
  5. Machine Learning and Deep Learning Narrow AI Machine Learning Deep

    Learning Machine Learning’s goal is to develop predictions based on previously observed patterns. Various variables are weighed to predict the probabilities of the outcomes. The variables and formula used to make such predictions may be programmed by a human OR they can be developed by the machine itself (Deep Learning).
  6. Deep Learning 01 Collect Training Data 02 Analyze and Segment

    03 Setup and Train a Neural Network 04 Test and deploy
  7. solid vertical diagonal horizontal e2eml.school Deep Learning Input Case 1

    Case 2 Case 3 Case 4
  8. solid vertical diagonal horizontal e2eml.school Deep Learning Input Case 1

    Case 2 Case 3 Case 4
  9. solid vertical diagonal horizontal e2eml.school Deep Learning Input Case 1

    Case 2 Case 3 Case 4
  10. Types of Data For ML Processing • Motion Data •

    Audio/Voice Data • Image Data • Text Data • Geospatial Data • Physiological/Medical Data • Financial Data • Behavioral Data • and Much More
  11. Types of Data For ML Processing • Motion Data •

    Audio/Voice Data • Image Data • Text Data • Geospatial Data • Physiological/Medical Data • Financial Data • Behavioral Data • and Much More
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  14. Figure 2: Applications of AI algorithms in medicine. The left

    panel shows the image fed into an algorithm. The right panel shows a region of potentially dangerous cells, as identified by an algorithm, that a physician should look at more closely. (From Artificial Intelligence in Medicine: Applications, implications, and limitations by Daniel Greenfield.)
  15. More than 50 AI/ML algorithms have been cleared by the

    US Food and Drug Administration for uses that include identifying intracranial hemorrhage from brain computed tomographic scans and detecting seizures in real time. Algorithms are also used to inform clinical operations, such as predicting which patients will “no show” for scheduled appointments. More recently, algorithms that predict in- hospital mortality have been proposed to inform ventilator allocation during the coronavirus disease 2019 pandemic. JAMA Article by Stephanie Eaneff, MSP1,2; Ziad Obermeyer, MD3; Atul J. Butte, MD, PhD2,4
  16. Types of Data For ML Processing • Motion Data •

    Audio/Voice Data • Image Data • Text Data • Geospatial Data • Physiological/Medical Data • Financial Data • Behavioral Data • and Much More
  17. Interactive Voice Response IF..Then Based Systems

  18. Interactive Voice Response ML Based Systems

  19. When and How to Use ML • Autonomous Vehicles AI

    and Ethics
  20. When and How to Use ML • Autonomous Vehicles •

    Admissions and Grading AI and Ethics
  21. When and How to Use ML • Autonomous Vehicles •

    Admissions and Grading • Loans and Credit AI and Ethics
  22. When and How to Use ML • Autonomous Vehicles •

    Admissions and Grading • Loans and Credit • Social Media AI and Ethics
  23. When and How to Use ML • Autonomous Vehicles •

    Admissions and Grading • Loans and Credit • Social Media • Warfare AI and Ethics
  24. Problems with AI

  25. solid vertic diagonal horizontal e2eml.school Input Case 1 Case 2

    Case 3 Case 4 Problems with AI “Hidden Layers”
  26. Problems with AI Adversarial AI from Savan Visalpara

  27. Problems with AI Adversarial AI from Weijia Zhang

  28. Problems with AI Adversarial AI from MIT CSAIL

  29. Problems with AI Adversarial AI Images: Evtimov et al Camouflage

    graffiti and art stickers cause a neural network to misclassify stop signs as speed limit 45 signs or yield signs.
  30. http://LearnAbout.AI

  31. None