Meet the Learning Machine: How Artificial Intelligence is transforming our world!
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The document discusses the transformation of the world through artificial intelligence (AI), defining different types such as general AI and narrow AI. It explains machine learning and deep learning, highlighting their goals and methods, and provides various applications of AI, particularly in medicine where over 50 algorithms have been approved for clinical use. Ethical concerns and potential problems with AI, such as adversarial AI, are also addressed.
Meet the Learning Machine: How Artificial Intelligence is transforming our world!
- 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
- 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
- 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.