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Künstlich intelligent?

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Nicht zuletzt durch die medienwirksame Erfolge des maschinellen Lernens durch DeepMind, OpenAI und Kollegen ist Künstliche Intelligenz im Moment wieder in aller Munde. Einerseits locken zahlreiche neue, vorher undenkbare Anwendungen wie die automatische Diagnose von Krankheiten, autonome Fahrzeuge und Drohnen, oder die automatische Übersetzung gesprochener Wörter. Andererseits warnen mahnenden Stimmen wird vor dem zunehmendem Einflussnahme der „Algorithmen“ auf fast alle Bereiche unseres Lebens sowie vor unerwünschten Folgen von sich verselbstständigenden Computern gewarnt. Einige träumen von – oder fürchten sich vor – der vermeintlich unausweichlichen Singularität, an der sich nichts weniger als das Schicksal der gesamten Menschheit entscheiden wird. Doch was verbirgt sich hinter dem Begriff Künstliche Intelligenz? Je nachdem, wen man fragt, erhält man unterschiedliche, bisweilen gegensätzliche Antworten. Dieser Vortrag stellt einige dieser Antworten vor und versucht sie (nicht nur) anhand von Beispielen aus Forschung und Anwendung einzuordnen.

Event: Business Analytics Day, 07.03.2019
Speaker: Dr. Matthias Richter, Dr. Stefan Igel (inovex)

Mehr Tech-Vorträge: inovex.de/vortraege
Mehr Tech-Artikel: inovex.de/blog

Published in: Software
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Künstlich intelligent?

  1. 1. Künstlich Intelligent? Stefan Igel, Matthias Richter Offenburg, 7. März 2019
  2. 2. stefan.igel@inovex.de Stefan Igel matthias.richter@inovex.de Head of Big Data Solutions Machine Learning Engineer Matthias Richter 2
  3. 3. inovex ist ein innovations- und qualitätsgetriebenes IT-Projekthaus mit dem Leistungsschwerpunkt Digitale Transformation.  ‣ Product Discovery · Product Ownership ‣ Web · UI/UX · Replatforming · Microservices ‣ Mobile · Apps · Smart Devices · Robotics ‣ Big Data & Business Intelligence Platforms ‣ Data Science · Data Products · Search · Deep Learning ‣ Data Center Automation · DevOps · Cloud · Hosting ‣ Agile Training · Technology Training · Coaching Wir nutzen Technologien, um unsere Kunden glücklich zu mach Und uns selbst. Karlsruhe · Pforzheim · Stuttgart · München · Köln · Hamburg www.inovex.de
  4. 4. Die Jahreszeiten der KI 1950 heute 1. Winter 1973: Lighthill Report 1974: SUR-Debakel Vertrauen/Funding STUDENT SAINT SHRDLU Perzeptron Konnektionismus Expertensysteme Symbolische KI 2. Winter 1987: Ende der LISP-Maschine 1990: Ende der Expertensysteme Statistisches Machine Learning Agentensysteme Deep Learning Big Data 4
  5. 5. Was ist künstliche Intelligenz? 5
  6. 6. Künstliche Intelligenz im Film negativ positiv 6
  7. 7. Deep Blue vs. Garri Kasparov (1997) Quelle: Pedro Villavicencio via flickr Quelle: S.M.S.I., Inc. - Owen Williams, The Kasparov Agency 7
  8. 8. Deep Blue vs. Garri Kasparov (1997) Quelle: Pedro Villavicencio via flickr Quelle: S.M.S.I., Inc. - Owen Williams, The Kasparov Agency „nur Brute Force” 7
  9. 9. IBM Watson vs. Ken Jennings, Brad Rutter (2011) Quelle: Atomic Taco via Wikimedia 8
  10. 10. IBM Watson vs. Ken Jennings, Brad Rutter (2011) Quelle: Atomic Taco via Wikimedia „nur eine Datenbank” 8
  11. 11. Alpha Go vs. Lee Sedol (2016) Quelle: Buster Benson via flickr 9
  12. 12. Alpha Go vs. Lee Sedol (2016) Quelle: Buster Benson via flickr OK, das ist cool 9
  13. 13. Alpha Go vs. Lee Sedol (2016) Quelle: Buster Benson via flickr OK, das ist cool 9
  14. 14. Alpha Go vs. Lee Sedol (2016) Quelle: Buster Benson via flickr OK, das ist cool„kann nur spielen” 9
  15. 15. MENACE (1960) Machine Educable Noughts And Crosses Engine 10
  16. 16. MENACE (1960) Machine Educable Noughts And Crosses Engine 10
  17. 17. MENACE (1960) Machine Educable Noughts And Crosses Engine Matt Parker – MENACE: the pile of matchboxes which can learn https://youtu.be/R9c-_neaxeU 10
  18. 18. Was ist künstliche Intelligenz? 11
  19. 19. Quelle: Artificial Intelligence - A Modern Approach (Norvig & Russell 2003) Definitionen von KI Was? Denken vs. Handeln Wie? Menschlich vs. Rational 12 Menschlich Denkend Rational Denkend Menschlich Handelnd Rational Handelnd Dimensionen
  20. 20. Quelle: Artificial Intelligence - A Modern Approach (Norvig & Russell 2003) Definitionen von KI Menschlich Handelnd “The art of creating machines that perform functions that require intelligence when performed by people.” (Kurzweil, 1990) “The study of how to make computers do things at which, at the moment, people are better.” (Rich and Knight, 1991) 13
  21. 21. Quelle: Artificial Intelligence - A Modern Approach (Norvig & Russell 2003) Definitionen von KI Menschlich Handelnd “The art of creating machines that perform functions that require intelligence when performed by people.” (Kurzweil, 1990) “The study of how to make computers do things at which, at the moment, people are better.” (Rich and Knight, 1991) The Turing Test approach 13
  22. 22. Quelle: Artificial Intelligence - A Modern Approach (Norvig & Russell 2003) Definitionen von KI Menschlich Denkend “The exciting new effort to make computers think . . . machines with minds, in the full and literal sense.” (Haugeland, 1985) “[The automation of] activities that we associate with human thinking, activities such as decision-making, problem solving, learning . . .” (Bellman, 1978) 14
  23. 23. Quelle: Artificial Intelligence - A Modern Approach (Norvig & Russell 2003) Definitionen von KI Menschlich Denkend “The exciting new effort to make computers think . . . machines with minds, in the full and literal sense.” (Haugeland, 1985) “[The automation of] activities that we associate with human thinking, activities such as decision-making, problem solving, learning . . .” (Bellman, 1978) The cognitive modeling approach 14
  24. 24. Quelle: Artificial Intelligence - A Modern Approach (Norvig & Russell 2003) Definitionen von KI Rational Denkend “The study of mental faculties through the use of computational models.” (Charniak and McDermott, 1985) “The study of the computations that make it possible to perceive, reason, and act.” (Winston, 1992) 15
  25. 25. Quelle: Artificial Intelligence - A Modern Approach (Norvig & Russell 2003) Definitionen von KI Rational Denkend “The study of mental faculties through the use of computational models.” (Charniak and McDermott, 1985) “The study of the computations that make it possible to perceive, reason, and act.” (Winston, 1992) The “laws of thought” approach 15
  26. 26. Quelle: Artificial Intelligence - A Modern Approach (Norvig & Russell 2003) Definitionen von KI Rational Handelnd “Computational Intelligence is the study of the design of intelligent agents.” (Poole et al., 1998) “AI … is concerned with intelligent behavior in artifacts.” (Nilsson, 1998) 16
  27. 27. Quelle: Artificial Intelligence - A Modern Approach (Norvig & Russell 2003) Definitionen von KI Rational Handelnd “Computational Intelligence is the study of the design of intelligent agents.” (Poole et al., 1998) “AI … is concerned with intelligent behavior in artifacts.” (Nilsson, 1998) The rational agent approach 16
  28. 28. Der Werkzeugkasten der KI 17
  29. 29. Der Turing Test einfacher Turing Test ● kommunizieren ● Wissen speichern ● antworten und schlussfolgern ● adaptieren und extrapolieren totaler Turing Test ● Objekte erkennen ● Objekte manipulieren und bewegen 18
  30. 30. Wissensrepräsentation Automatisches Schließen MachineLearning: Adaptation,Detektion,(Extrapolation) erfolgreiche Kommunikation (text to speech) Natural Language Processing Computer Vision Robotics Wahrnehmung, Bildverstehen Manipulation, Bewegung Teilbereiche der Künstlichen Intelligenz 19
  31. 31. Umsetzung von KI-Fähigkeiten 1950 – 1980 1970 – 2010 2000 – heute 20
  32. 32. Künstliche Intelligenz = Deep Learning? 21
  33. 33. https://hackernoon.com/the-ai-hierarchy-of-needs-18f111fcc007 https://www.digitalsource.io/data-engineer-vs-data-scientist 22
  34. 34. https://hackernoon.com/the-ai-hierarchy-of-needs-18f111fcc007 https://www.digitalsource.io/data-engineer-vs-data-scientist Research Scientist Core Data Scientist ML Engineer Data Scientist / Analyst Software Developer Data Engineer 22
  35. 35. https://hackernoon.com/the-ai-hierarchy-of-needs-18f111fcc007 Data Scientist ML Engineer Big Data Engineer Software Entwickler mit Schwerpunkt Big Data Big Data Systems Engineer 22
  36. 36. Künstliche Intelligenz am Beispiel 23 mehr unter https://www.inovex.de/blog/category/analytics/
  37. 37. State-of-the-Art in NLP Spam detection mostly solved making good progress still really hard Sentiment Analysis Question Answering (QE) Part-of-speech (POS) tagging Named entity recognition (NER) Coreference resolution Word sense disambiguation (WSD) Dialog Summarization ParaphraseParsing Machine Translation Information extraction Topic models 24
  38. 38. Sebastian Blank: Querying NoSQL with Deep Learning to Answer Natural Language Questions NLP in Action: What do you want to know? 25 › Who was the writer of True Lies? Model › James Cameron Query movieQuery field true liesQuery entity authorResponse field
  39. 39. Sebastian Blank: Querying NoSQL with Deep Learning to Answer Natural Language Questions NLP in Action: What do you want to know? 26 Who Encoder Decoder movie true was the writer of true lies ? lies author Word Embedding
  40. 40. Sebastian Blank: Querying NoSQL with Deep Learning to Answer Natural Language Questions NLP in Action: What do you want to know? 26 Who Encoder Decoder movie true was the writer of true lies ? lies author Word Embedding
  41. 41. NLP in Action: Give me similar things! 27 Text extraction Semantic enrichment (embeddings) NLP? enriched query NLP basics AI How to train a model Machine Learning query expander
  42. 42. NLP in Action: Give me similar things! Word Embeddings Model semantic content (Word2Vec) Query Expansion Capture what user really means Preprocessing Process and enrich huge amounts of documents Data Exploration Classification (multinomial Bayes, SVM, LSTM + Attention) Topic Modeling (LDA) Named Entity Recognition (CRFs) 28
  43. 43. State-of-the-Art in CV Object Detection mostly solved making good progress still really hard Object Detection Object Detection Small-set Recognition / Matching Pose Estimation (rigid bodies) Content-based retrieval Pose Estimation (non-rigid) Action Recognition Scene Understanding Viewpoint TransferObject Categorization Image Restoration/Inpainting Segmentation/Semantic Labeling 3D-Reconstruction Object Tracking Domain Transfer Object Hierarchies 29 Novelty Detection
  44. 44. CV in Action: Image Augmentation Grow a temporary moustache 30 ● Recognize identity​ ● Estimate age, drowsiness, expression, … ● Extract shape and pose ● Estimate attention and gaze direction detectface findlandmarks augmentimage otheruses
  45. 45. CV in Action: Image Retrieval 31 • • • Similarity Measure Database Database ranked according to similarity • • • • • • CNNFeatures CNN Features CNN Features CNN Features CNN Features Query Image learned from
  46. 46. Jan Bender: Development and Evaluation of Deep Neural Networks for Detection of Pedestrian Crossing Intentions CV in Action: Pedestrian Crossing Intention 32
  47. 47. Datenqualität bestimmt Modellqualität 33 http://gendershades.org/ South China Morning Post (scmp.com)
  48. 48. Datenqualität bestimmt Modellqualität 33 http://gendershades.org/ South China Morning Post (scmp.com)
  49. 49. Datenqualität bestimmt Modellqualität 33 http://gendershades.org/ South China Morning Post (scmp.com) Wikipedia
  50. 50. Robotics Pepper & Nao 34 Pepper - Benutzerinteraktion - Informationsquelle - Emotionserkennung Nao - Erste Programmiererfahrung (nicht nur) für Kinder - Perfekte Plattform für junge Talente (devoxx4kids, Girls' Day, …)
  51. 51. Quelle: https://blog.openai.com/learning-dexterity/ Fingerfertigkeit 35
  52. 52. Was ist künstliche Intelligenz? 36
  53. 53. Was kann ich mit künstlicher Intelligenz machen? 36
  54. 54. https://xkcd.org/1425, vom 24.09.2014 Wo stehen wir heute? 37 narrow AI wide AI Lösungen für konkrete Probleme Systeme mit AI Komponenten menschliches Lernen Extrapolation und Kausalität Humor und Satire Umfangreiche Datensätze Rechenkapazität und Infrastruktur Frameworks und Tools
  55. 55. https://xkcd.org/1425, vom 24.09.2014 Wo stehen wir heute? I’ll need a big dataset and a GPU cluster 37 narrow AI wide AI Lösungen für konkrete Probleme Systeme mit AI Komponenten menschliches Lernen Extrapolation und Kausalität Humor und Satire Umfangreiche Datensätze Rechenkapazität und Infrastruktur Frameworks und Tools
  56. 56. https://xkcd.org/1425, vom 24.09.2014 Wo stehen wir heute? … and check why they’re there. 37 narrow AI wide AI Lösungen für konkrete Probleme Systeme mit AI Komponenten menschliches Lernen Extrapolation und Kausalität Humor und Satire Umfangreiche Datensätze Rechenkapazität und Infrastruktur Frameworks und Tools
  57. 57. Künstliche Intelligenz braucht Kreativität 38 Mensch: emotional, irrational, empathisch, neugierig, passioniert und kreativ Maschine: präzise, berechnend, reproduzierbar, realistisch und getaktet
  58. 58. http://www.bbc.com/future/story/20151201-the-cyborg-chess-players-that-cant-be-beaten Mensch + Maschine = Dream Team 39
  59. 59. http://www.bbc.com/future/story/20151201-the-cyborg-chess-players-that-cant-be-beaten Mensch + Maschine = Dream Team 39

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