Practical Introduction to AI, Deep Learning, and Large Scale Image Analytics
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Practical Introduction to AI, Deep Learning, and Large Scale Image Analytics given as a Pantalk in Zurich in March 2016
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Practical Introduction to AI, Deep Learning, and Large Scale Image Analytics
- 1. PRACTICAL INTRODUCTION TO ARTIFICIAL INTELLIGENCE DEEP LEARNING LARGE-SCALE IMAGE ANALYTICS KEVIN MADER / FLAVIO TROLESE 4QUANT | BIG IMAGE ANALYTICS PANTALK TUESDAY, MARCH 19 2016 / IMPACTHUB GARAGE ZURICH
- 2. 4Quant | BIG IMAGE ANALYTICS
- 6. Die Länder, die Österreich umgeben. ↓ Was sind Schweiz, Italien, Slowenien, Ungarn, Tschechische Republik, Deutschland, Slovakei?
- 10. 4Quant | BIG IMAGE ANALYTICS
- 11. 4Quant | BIG IMAGE ANALYTICS
- 12. 4Quant | BIG IMAGE ANALYTICS
- 13. 4Quant | BIG IMAGE ANALYTICS
- 15. 4Quant | BIG IMAGE ANALYTICS
- 16. 4Quant | BIG IMAGE ANALYTICS
- 17. 4Quant | BIG IMAGE ANALYTICS
- 20. 4Quant | BIG IMAGE ANALYTICS
- 22. 4Quant | BIG IMAGE ANALYTICS
- 23. http://4quant.com/javascript-breakout/ 4Quant | BIG IMAGE ANALYTICS
- 24. HOW? 4Quant | BIG IMAGE ANALYTICS
- 25. STANDARD MACHINE LEARNING 4Quant | BIG IMAGE ANALYTICS
- 26. CORE IDEAS What is an image? What a human sees What a machine sees 4Quant | BIG IMAGE ANALYTICS
- 27. CORE IDEAS Feature Generation → Making the computer see more 4Quant | BIG IMAGE ANALYTICS
- 28. CORE IDEAS Training / Validation With all machine learning techniques it is critical to divide data into training and validation sets. The algorithm can then be tested (validated) on data it has never seen before to ensure it generalizes 4Quant | BIG IMAGE ANALYTICS
- 29. OUTPUT / LOSS FUNCTION 4Quant | BIG IMAGE ANALYTICS The in order for machine learning to work there has to be a single output for the system which quantifies how well it is working - the number of correctly identified structures (true- positives) - the number of correct letters in a sentences - the score of a game
- 30. CORE IDEAS Learning from tagged data (supervised) 4Quant | BIG IMAGE ANALYTICS What is this?
- 31. PROBLEMS Features can be very difficult to ‘engineer’. What makes a person a person? More data doesn’t always lead to better results. 4Quant | BIG IMAGE ANALYTICS
- 32. DEEP LEARNING 4Quant | BIG IMAGE ANALYTICS
- 33. One of these can recognize without any programming by just experiencing and getting feedback. THE IDEA 4Quant | BIG IMAGE ANALYTICS https://flic.kr/p/2eryEj
- 34. The human brain is a large, layered, connected network of neurons. THE IDEA 4Quant | BIG IMAGE ANALYTICS https://flic.kr/p/5J4uci
- 35. We understand how some of these layers work and can make computationally fast models for simulating their behavior THE IDEA 4Quant | BIG IMAGE ANALYTICS
- 36. DEEP LEARNING Deep learning is a set of algorithms in machine learning that attempt to learn in multiple levels, corresponding to different levels of abstraction. 4Quant | BIG IMAGE ANALYTICS
- 37. THE IDEA 4Quant | BIG IMAGE ANALYTICS A machine learning system with millions of inputs And 1 output
- 38. THE IDEA 4Quant | BIG IMAGE ANALYTICS The networks can get very large (hence the deep) Here is the Inception Network from Google
- 39. TYPES OF ARTIFICIAL NEURONS 4Quant | BIG IMAGE ANALYTICS Fully-connected → everything connected to everything Convolutional (CNN) → mix things together Recurring (RNN) → remember parts of sequences
- 40. Recurring Networks 4Quant | BIG IMAGE ANALYTICS http://karpathy.github.io/2015/05/21/rnn-effectiveness/
- 41. Recurring Networks 4Quant | BIG IMAGE ANALYTICS http://karpathy.github.io/2015/05/21/rnn-effectiveness/ Given the starting letter h Predict the rest of the letters
- 42. SCHWIIZERDÜTSCH 4Quant | BIG IMAGE ANALYTICS Goes through hundreds of pages of text character by character and trains neurons to predict the correct output
- 43. The text shows the algorithm learning to complete the sentence. The curve shows how confident it is in each guess SCHWIIZERDÜTSCH 4Quant | BIG IMAGE ANALYTICS
- 44. 100 gu sisxt n eigeiua a esSWctaicobemhat,E out?s v t t uew 10K Uhe uf Hountigm don d’Bomura fürsyn al jerisim Sbeour Rucch 65K Übschamt wiänä wo und ebs haGscham, üblart uls zä flusch, zänsert. De Unner sindämzalagsel 100K Totatwärt. Dischtä Tittä vo dä ues und erwiä Gsacht agä schtüswongeilä. Beterischtiongehärne vordä em Verbichunt. Diä Mieräng ader h d Zientlichnig vu CHF 4Quant | BIG IMAGE ANALYTICS SCHWIIZERDÜTSCH
- 46. Spell/Grammar Check (for a language with ‘no rules’) Dialect Detector Autocomplete APPLICATIONS 4Quant | BIG IMAGE ANALYTICS
- 47. Automatic C code Wikipedia Text BEYOND SCHWIIZERDÜTSCH 4Quant | BIG IMAGE ANALYTICS
- 48. CONVOLUTIONAL NETWORKS 4Quant | BIG IMAGE ANALYTICS Pixels Edges Object parts Object models → → →
- 49. CONVOLUTIONAL NETWORKS 4Quant | BIG IMAGE ANALYTICS
- 50. CONVOLUTIONAL NETWORKS 4Quant | BIG IMAGE ANALYTICS
- 51. 4Quant | BIG IMAGE ANALYTICS CONVOLUTIONAL NETWORKS
- 52. Street, Trees, Fence, Bicycle UNDERSTANDING COMPLEX SCENES 4Quant | BIG IMAGE ANALYTICS
- 53. Self-driving cars need to be able to identify walkways automatically All point geo- referenced IDENTIFY WALKWAYS 4Quant | BIG IMAGE ANALYTICS
- 54. Understanding what is happening inside of these complex networks DREAMING 4Quant | BIG IMAGE ANALYTICS
- 55. 4Quant | BIG IMAGE ANALYTICS DREAMING
- 56. Applying parts of trained networks to other types of images. TRANSFER 4Quant | BIG IMAGE ANALYTICS
- 58. A challenging field - noisy - highly variable - many tissues / diseases look the same MEDICAL IMAGES 4Quant | BIG IMAGE ANALYTICS
- 59. Red are lungs Yellow are bones Blue are the other organs MEDICAL IMAGES 4Quant | BIG IMAGE ANALYTICS
- 60. FINDING CANCER 4Quant | BIG IMAGE ANALYTICS
- 61. MEDICAL IMAGES 4Quant | BIG IMAGE ANALYTICS Convolutional neurons act on the image and learn to extract the relevant information
- 62. MEDICAL IMAGES 4Quant | BIG IMAGE ANALYTICS These representations can then be used to automatically find organs like the heart and measure blood flow → →
- 63. MEDICAL IMAGES 4Quant | BIG IMAGE ANALYTICS These representations can then be used to automatically find organs like the heart and measure blood flow → →
- 64. Open Challenges 4Quant | BIG IMAGE ANALYTICS You need a lot of data to identify (1K-100M) Some networks learn well, others do not Parameters can make a huge difference Intermediate layers can be difficult to interpret
- 65. RESOURCES Google Cloud Vision API IBM Watson Vision Service Microsoft Project Oxford TensorFlow Caffe Moodstocks 4Quant
- 66. PRACTICAL INTRODUCTION TO ARTIFICIAL INTELLIGENCE DEEP LEARNING LARGE-SCALE IMAGE ANALYTICS THANK YOU KEVIN MADER / FLAVIO TROLESE 4Quant Ltd. PANTALK THURSDAY MARCH 19 2016 / IMPACTHUB GARAGE ZURICH 4Quant| BIG IMAGE ANALYTICS
Editor's Notes
- Programmieren -> Einer Maschine beibringen was sie machen soll. Etwas beibringen was man nicht selber kann?
- Arthur Samuel 1956 -> ihn im Spiel Dame schlagen Computer tausende Male gegen sich selbst spielen, sodass er Dame spielen lernte. Das funktionierte wirklich, und schon 1962 besiegte dieser Computer den Landesmeister von Connecticut. Arthur Samuel -> Urvater des Maschinellen Lernens
- 1997 gewann Deep Blue gegen Kasparow einen ganzen Wettkampf aus sechs Partien unter Turnierbedingungen.
- Amazon, Netflix -> Kaufempfehlungen oder Filmvorschläge. LinkedIn oder Facebook
- IBM Watson -> zwei Weltmeister der "Jeopardy" schlagen, "2003 verschwand u. a. der antike 'Löwe von Nimrud' aus dem Museum dieser Stadt."
- selbstfahrende Autos. Unterschied etwa zwischen Baum und Fußgänger erkennen
- Dieses Auto ist schon über 1 Mio. km ohne den kleinsten Unfall auf normalen Straßen gefahren.
- Computer können lernen -> auch Dinge, von denen wir nicht wissen, wie sie funktionieren, und manchmal sogar besser als wir. kein Vorwissen zu Chemie oder Biowissenschaften hatte und nur zwei Wochen brauchte. -> Deep Learning. Deep Learning basiert auf der Funktion des menschlichen Gehirns und deswegen ist es ein Algorithmus, dessen Funktion theoretisch keine Grenzen gesetzt sind. Je mehr Daten und Rechenzeit man hat, desto besser wird er.
- zuhören und ‘verstehen’ Chinese, English, French, German, Italian, Portuguese, Spanish -> programmierer kann kein chinesisch
- Deep Learning -> ein einziger Algorithmus Wettbewerb der Universität Bochum zum Erkennen von Verkehrszeichen hat Deep Learning gelernt, Menschen übertraf und zwar um das Doppelte. 2011 gab es also das erste Beispiel für Computer, die besser sehen können als Menschen.
- 2012 gab Google bekannt, dass sie einen Deep-Learning-Algorithmus Youtube -> 16 000 Computern einen Monat allein Konzepte Menschen oder Katzen erkannt hat.