Applications of Artificial Intelligence
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[AppTalks] Fawzi, notre docteur en vision par ordinateur, est revenu sur son sujet de thèse : l'Intelligence Artificielle pour nous expliquer les différences entre le Machine learning et le Deep learning
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Applications of Artificial Intelligence
- 1. Applications of Artificial Intelligence FAWZI KHATTAR COMPUTER VISION RESEARCHER AT APPSTUD 11th February 2020
- 2. Outline •What is artificial intelligence? Machine learning? Deep learning? •Application of AI in robotics. •How does machine learning works? •Example of deep learning applications: Face recognition, age estimation… 2
- 3. Artificial Intelligence? Machine learning? Deep learning? •AI is the ability of a computer program or computer controlled robot to perform tasks commonly Associated with intelligent beings (Face recognition, self-driving cars…). •Machine learning is the science of getting computers to act without being explicitly programmed. •Deep learning is a subset of machine learning where artificial neural networks algorithms learn from large amounts of data. 3
- 4. Machine learning vs Traditional programming 4
- 5. Deep learning 5 Human brain Deep Neural Network
- 6. AI in remote labs 6
- 7. Motivation Advantages: Elimination of the spatial-temporal constraints Sharing of expensive equipment Enhance quality of learning Enhance security and protection of students (chemical labs,…) Boost motivation of students (game approach, new technology) Drawbacks: Absence of human-human interaction (no teacher) 7
- 8. Remote lab in electronics: LaboREM • Remote lab developped at the IUT of Bayonne • Main objective: increase student motivation • Wiring circuits is done using a robotic hand • Collaboration tools, TOP 10, video feedback (web camera) 8
- 10. Remote labs No Teacher No Teacher student Desired concentration level Observe student Interventions Degreeof concentration Bad learning outcome Lack of Motivation Frustration 10
- 11. Traditional lab Teacher Teacher student Desired concentration level Observe student Interventions Degreeof concentration Good learning outcome Motivation 11
- 12. Enhanced LaboREM Machine Machine student Desired concentration level Observe student Interventions Degreeof concentration Quadrocopter control to increase motivation Head pose estimation, facial expression analysis Good learning outcome Motivation 12
- 13. Scenario Search for instruments (on board camera) Detection of instruments Remote Student- teacher communicationReturn home and landing 13
- 14. Low cost drone: AR Drone 2.0 •Advantage: • Low cost < 100 euros (education domain, low budget…) •Drawbacks: • Bad sensor quality • Delay problems (cannot directly access the on-board controller) • Reduced battery life 14
- 15. Available sensors • Camera pointing forward : • 30 fps, resolution of 640 × 480 pixels • Significant radial distortion • Strong motion blur, linear distortion (rolling shutter) • Camera pointing downwards: • 60 fps • Resolution 176 × 144 pixels • Negligible radial distortion • Motion blur and rolling shutter effects • Gyroscope, Altimeter 15
- 16. Autonomous flight using computer vision • Objective: Autonomous flight to center the object of interest in the image • 3 Questions: What is the current position of the drone? What is the desired final position? How to reach the desired position from the current position? Localization module Where am I? Feedback control module How should I move to go there? Autonomous flight to a desired position Object of interest position estimation. What is the desired position? 16
- 17. Feedback control loop 17 Localization and Prediction: SLAM + Kalman filter PID Drone Desired 3D pose Sensor data with delay 𝑚𝑒𝑎𝑠 Posepredictedat 𝑡𝑐𝑢𝑟𝑟+𝑐𝑜𝑚 Commands with delay 𝑐𝑜𝑚
- 18. How does machine learning work? •Problem: Classify people by their Gender based on weight and height data. 18
- 19. Cost Function 19
- 23. Deep learning 23 Human brain Deep Neural Network
- 24. Concepts 24 Deep Neural Network •Many hidden layers with many neurones. •Cost function •Optimization: Gradient descent, ADAM…
- 25. Concepts 25
- 26. Age estimation •How to transform this network to predict the age of a person? •New cost function: Pictures with same age are pushed closer, pictures with different age are pushed farther apart. 26
- 28. Learn AI •Basics of Linear Algebra •Programming skills: Python, C++ •Deep learning libraries: Tensorflow, PyTorch… •Online courses: Coursera, Udemy… 28
- 29. QUESTIONS 29