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Deep learning for fun and profit (a simple introduction to Artificial Intelligence, Machine Learning, and Deep Learning)

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A simple introduction to Artificial Intelligence, Machine Learning, and Deep Learning. This talk happened at a local university in Porto Alegre and the target audience was comprised of people from many different backgrounds (including people that are not from Computer Science/Engineering fields). This is intended to be a presentation that gives intuition about these concepts.

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Deep learning for fun and profit (a simple introduction to Artificial Intelligence, Machine Learning, and Deep Learning)

  1. 1. DEEP LEARNING FOR FUN AND PROFIT Thomas Paula December, 13th 2018 Photo by Daniel Hjalmarsson on Unsplash
  2. 2. WHO AM I? 2 Thomas Paula tsp.thomas@gmail.com @tsp_thomas
  3. 3. AGENDA ● Introduction ○ Motivation ○ Definitions ● How Machine Learning works? ● What is Deep Learning? ● Examples ● Closing thoughts 3
  4. 4. 4 WHY SHOULD I CARE ABOUT IT?
  5. 5. ARTIFICIAL INTELLIGENCE ON THE NEWS 5
  6. 6. ARTIFICIAL INTELLIGENCE ON THE NEWS 6 Hinton said: "(...) people should stop training radiologists now, it's just completely obvious that in five years deep learning is going to do better than radiologists, it might be ten years".
  7. 7. EXAMPLE: RESEARCH IN HEALTHCARE 7 300+ papers regarding deep learning and medical image analysis Source: Litjens, Geert, et al. "A survey on deep learning in medical image analysis." Medical image analysis 42 (2017): 60-88.
  8. 8. TOP 20 EMERGING JOBS 8 Source: https://economicgraph.linkedin.com/research/LinkedIns-2017-US-Emerging-Jobs-Report
  9. 9. YOU’RE PROBABLY USING DEEP LEARNING AND YOU DON’T KNOW
  10. 10. DEFINITIONS 10 Source: Deep Learning Book (Goodfellow, Bengio, Courville)
  11. 11. ARTIFICIAL INTELLIGENCE ● "The effort to automate intellectual tasks normally performed by humans" ● Born in 1950s: people trying to make computers think ● People used to believe human-level artificial intelligence = hand-crafted set of rules ● 1950s to 1980s: Symbolic AI 11
  12. 12. 12 SO… WHAT IS AI? The definition of AI shapes according to the evolution of AI. This is called the AI Effect.
  13. 13. AI EFFECT 13 Has been solved Not true AI Hasn’t been solved True AI Source: https://medium.com/@katherinebailey/reframing-the-ai-effect-c445f87ea98b
  14. 14. MACHINE LEARNING 14 ● "Give computers the ability to learn without explicitly being programmed" ● Subfield of AI ● Term coined by Arthur Samuel in 1959 ● However, back in 1947 Alan Turing in a talk declared "what we want is a machine that can learn from experience"
  15. 15. 15 HOW MACHINE LEARNING WORKS? From programming to teaching
  16. 16. 16
  17. 17. SIMPLE EXAMPLE - HYPOTENUSE 17 4 3 c Computer Program Hypotenuse c = 5
  18. 18. SIMPLE EXAMPLE - HYPOTENUSE 18
  19. 19. Now... suppose we want to create a program to classify whether a given email is spam and not spam. 19 HOW COULD WE DO THAT?
  20. 20. SPAM OR NOT SPAM 20 Computer Program Not spam Spam
  21. 21. 21 HOW WE, AS HUMANS, RECOGNIZE AN EMAIL AS SPAM OR NOT SPAM?
  22. 22. 22 Observe Learn Know how to say what is spam and what is not based on seen emails Emails New, unseen emails Based on experience, can say whether new emails are spam or not
  23. 23. Let's define training the process of observing emails and learning from them. 23 Let's define the knowledge we built as model.
  24. 24. 24 HENCE, TO LEARN WE NEED ● Examples of spam and not spam: data ● A learning mechanism: ML algorithm ● A process to improve our learning over time: training ● A resulting knowledge: model
  25. 25. 25 THIS PARADIGM IS CALLED SUPERVISED LEARNING You need labeled examples to learn
  26. 26. 26 WHAT IF WE DON’T HAVE LABELS? THEN, WE HAVE UNSUPERVISED LEARNING
  27. 27. 27
  28. 28. 28 Computer Program Group 1 Group 2I want 2 groups
  29. 29. 29 NOW WE HAVE AN INTUITION ABOUT SUPERVISED AND UNSUPERVISED LEARNING Let’s take a look at Deep Learning :)
  30. 30. RECALLING THE DEFINITIONS 30 Source: Deep Learning Book (Goodfellow, Bengio, Courville)
  31. 31. WHAT IS DEEP LEARNING? Multiple definitions, however, these definitions have in common: ● Multiple layers of processing units; ● Supervised or unsupervised learning of feature representations in each layer, with the layers forming a hierarchy from low-level to high-level features. 31
  32. 32. DATA IS COMPOSITIONAL 32Sources: Convolutional Deep Belief Networks. Honglak Lee, et. al. and Large Scale Deep Learning. Jeff Dean, joint work with Google.
  33. 33. TRADITIONAL APPROACHES Input Feature Extraction Classification ● Expert knowledge ● Time-consuming hand-tuning ● In industrial applications, it is 90% of the time ● Domain-specific 33
  34. 34. “INTUITION” OF WHAT FEATURES ARE 34
  35. 35. TRADITIONAL VS DEEP LEARNING Traditional DeepLearning 35 Hand-crafted feature extractor Trained classifier Trained feature extractor Trained classifier
  36. 36. 36 WHERE THE IDEA CAME FROM?
  37. 37. VISUAL NEUROSCIENCE: INSPIRATION 37 Hubel and Wiesel (Nobel Prize in 1981)
  38. 38. CONVOLUTIONAL NEURAL NETWORK (CNN) 38Source: LeCun, Yann, et al. "Gradient-based learning applied to document recognition." Proceedings of the IEEE 86.11 (1998).
  39. 39. FIRST CNN APPLICATION: DIGITS RECOGNITION 39 Some mistakes made by the algorithm (correct -> prediction) Source: LeCun, Yann, et al. "Gradient-based learning applied to document recognition." Proceedings of the IEEE 86.11 (1998).
  40. 40. 40
  41. 41. WHY NOW? 41 Data Hardware Algorithms
  42. 42. 42 THE IMPACT OF DEEP LEARNING
  43. 43. WHY COMPUTER VISION IS HARD? 43 What we see What the computer sees Source: Inspired in CS231n slides
  44. 44. 44 Illumination Occlusion Background ClutterDeformation WHY COMPUTER VISION IS HARD? Source: Inspired in CS231n slides
  45. 45. 45 WHY COMPUTER VISION IS HARD?
  46. 46. IMAGENET 46
  47. 47. IMAGENET CHALLENGE RESULTS
  48. 48. IMAGENET CHALLENGE RESULTS
  49. 49. EXAMPLES OF THE EVOLUTION Source: ImageNet - http://image-net.org/
  50. 50. 50 EXAMPLES
  51. 51. DEEP PAINTERLY HARMONIZATION 51Source: Luan, Fujun, et al. "Deep Painterly Harmonization." arXiv preprint arXiv:1804.03189 (2018).
  52. 52. VIDEO-TO-VIDEO SYNTHESIS 52Source: Wang, Ting-Chun, et al. "Video-to-Video Synthesis." arXiv preprint arXiv:1808.06601 (2018).
  53. 53. AN AUGMENTED REALITY MICROSCOPE FOR CANCER DETECTION 53Source: https://ai.googleblog.com/2018/04/an-augmented-reality-microscope.html
  54. 54. AUDIO 54 Speech Synthesis (Apple) Music Recommendation (Spotify)
  55. 55. TEXT SUMMARIZATION 55
  56. 56. ROBOT GRASPING TASK Example of singulation 96% grasp success on unseen objects Source: QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation (Kalashnikov et al., 2018)
  57. 57. OBJECT RECOGNITION AND SEGMENTATION 57Source: He, Kaiming, et al. "Mask r-cnn." Computer Vision (ICCV), 2017 IEEE International Conference on. IEEE, 2017.
  58. 58. 58 CLOSING THOUGHTS
  59. 59. TAKE HOME MESSAGE #1 59 Machine Learning and Deep Learning are here to stay However, be aware of the hype!
  60. 60. 60 There are several challenges to move from research to production TAKE HOME MESSAGE #2
  61. 61. 61 Cross-area collaboration is essential TAKE HOME MESSAGE #3
  62. 62. MEETUP MACHINE LEARNING PORTO ALEGRE 62
  63. 63. DEEP LEARNING FOR FUN AND PROFIT Thomas Paula December, 13th 2018 Photo by Daniel Hjalmarsson on Unsplash THANK YOU! QUESTIONS?

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