Reasoning About Machine intuition- David Colls

1. Reasoning About MACHINE INTUITION David Colls

2. GENERAL NARROW MACHINE LEARNING DEEP LEARNING* REASONING INTUITING ARTIFICIAL INTELLIGENCE *AKA ARTIFICIAL NEURAL NETWORKS

3. MACHINE INTUITION, FTW

4. MACHINES BESTING HUMANS 4 1997 20172007 Chess* Face recognition Jeopardy! Go Poker Conversational speech recognition, Lipreading, Guessing locations Atari games, Image classification,  Deceptive pain expressions, Skin cancer diagnosis https://finnaarupnielsen.wordpress.com/2015/03/15/status-on-human-vs-machines/ Dota-2

5. PERFORMANCE LEAPS 5

8. BESTING HUMANS 8

9. BESTING HUMANS 9

10. BESTING HUMANS 10 Source: https://www.pokernews.com/strategy/how-to-bet-in-poker-tournaments-22371.htm

11. WHAT JUST HAPPENED?

12. WHAT IS AN ARTIFICIAL NEURAL NETWORK? 12 BEACH SEA SKY …

13. WHAT IS AN ARTIFICIAL NEURAL NETWORK? 12 © Fjodor van Veen 2016 BEACH SEA SKY …

14. WHAT IS AN ARTIFICIAL NEURAL NETWORK? 12 Weights of connections between nodes repeatedly adjusted as network “learns” training set of labelled images 1. TRAINING Network can classify novel images with “learned” labels 2. USAGE

15. EXERCISE 13 1 2 3 4 5 A BEACH B C

16. NEURAL NETWORKS HAVE BEEN AROUND FOR A WHILE… 14 Dave’s 1994 university internship

17. …BUT THEN THIS HAPPENED 15 WEB-SCALE DATA 1 Data volumes double every year

18. Massive adoption of GPU (and TPU, HPU, FPGA) 2 DEDICATED PARALLEL HARDWARE …BUT THEN THIS HAPPENED 16

19. 3 ADVANCED NETWORK DESIGNS …BUT THEN THIS HAPPENED 17 © Fjodor van Veen 2016

20. NETWORK PARAMETERS - STATE OF THE ART 18 2013 201720152014 20162012 160 B 11.2 B 1.7 B 1 Billion 10 Billion 100 Billion 1,000 Billion

21. SO, WHAT’S NEXT?

22. EXPLOSION OF MICRO-INTUITORS 20 There is almost nothing we can think of that cannot be made new, diﬀerent, or more valuable by infusing it with some extra IQ. The business plans of the next 10,000 start-ups are easy to forecast:  take X and add AI. Kevin Kelly The Inevitable topbots.com 133 Enterprise AI Startups

23. HUMANS AND MACHINES COMPLEMENTING EACH OTHER 21 MACHINES Wider learners Scalable thinkers HUMANS Faster learners Flexible thinkers

24. DISTRIBUTED MACHINE INTUITION 22 Apple iOS Core ML Google Federated Learning CLOUD TRAINING DEVICE INTUITION AGGREGATED DATA PRIVATE DATA FEDERATING LEARNING

25. PRO-ACTIVE DISCUSSION AND MANAGEMENT OF RISKS 23

26. 24 com·put·er /kəmˈpyo͞ odər/ noun (17th century) “one who computes”

27. de·ci·sion mak·er /dəˈsiZHən/ ˈ/mākər/ noun (20th century) “one who decides” 25 Source: https://www.pokernews.com/strategy/how-to-bet-in-poker-tournaments-22371.htm

28. DESIGNING PRODUCTS WITH INTUITION

29. UNDERSTAND CUSTOMER PERCEPTION 27 Just 7 percent of respondents would trust a robot with their savings, versus the 14 percent willing to submit to a machine for heart surgery. Andy Maguire COO HSBC Europe

30. BUILD CUSTOMER TRUST 28 https://hbr.org/2017/04/to-get-consumers-to-trust-ai-show-them-its-benefits OPERATIONAL SAFETY & DATA SECURITYHygiene 1 COGNITIVE COMPATIBILITY 2 TRIALABILITY 3 USABILITY Key Attributes 4 AUTONOMY & CONTROL 5 GRADUAL INTRODUCTION 6 PRO-ACTIVE COMMUNICATIONS Key Strategies

31. TAKE A HUMAN-CENTRED APPROACH 29 GOOGLE BRAIN People + AI Research institute Prototype with Real People Instead of an Algorithm Machine Learning Doesn’t Solve Everything Design with the System’s Failure in Mind Get Feedback, Forever

32. CHOOSE THE RIGHT PROBLEMS 30https://medium.com/intuitionmachine

33. BUSINESS VS GAMEPLAY 31 BUSINESS GAMEPLAY Goal Environment Limited Resources Moves Taking Turns Scoring Results More freedom. Act ethically! Less freedom. Follow the rules! Necessarily complex Simplification reveals insight

34. DEVELOPING TECHNOLOGY WITH INTUITION

35. THE PROBLEM 33 XKCD (CC BY-NC 2.5)

36. THE NEW NEW PRODUCT DEVELOPMENT GAME 34 OBJECTIVES PRODUCT Validate RULES + SOFTWARE ARCHITECTURE Research & Analyse Develop (PEOPLE) CODE Verify Deploy

37. THE NEW NEW NEW PRODUCT DEVELOPMENT GAME 35 OBJECTIVES PRODUCT Validate RULES + SOFTWARE ARCHITECTURE Research & Analyse DATA SET + NETWORK ARCHITECTURE Curate Data Deploy Develop (PEOPLE) CODE Verify Deploy Train (MACHINES) MODEL Verify

38. DATA CURATION 36 DATA SET + NETWORK ARCHITECTURE Curate Data OBJECTIVESYour existing data • Needs 10,000-100,000 samples • Normalisation • Bias treatment Generating new data • Secondary app • Simulation • Generative networks

39. MACHINES TRAINING 37 DATA SET + NETWORK ARCHITECTURE Train (MACHINES) MODEL Verify Concerns • Number crunching • Fine-tuning & debugging • Evolving network architecture Approaches • Dedicated hardware • Pre-trained models • Adversarial networks • High-level frameworks & automation

40. SKILLS SETS 38 Diverse teams • More robust approaches • Less risk of inadvertent bias Data scientists • High-level direction - hybrid approaches • Deep technical expertise • Risk and quality assurance Architecture & Development • AI API • Integrate data pipelines & UI • Develop automation tools • Run network iterations and training cycles TOOLS & FRAMEWORKS (EG) Platforms • TensorFlow - general purpose and cross-architecture • Caffe - specifically vision - includes Model Zoo with pre-trained models High-level frameworks • KERAS - python based flexible network definition • DeepLearning.scala - differentiable functional programming • PyTorch - evolving and flexible

41. A GOOD PROBLEM TO HAVE? 39 XKCD (CC BY-NC 2.5)

42. BUILDING ORGANISATIONS WITH INTUITION

43. KNOW WHAT 41 Explicit, rule-based

44. KNOW HOW 42 Tacit, feel-based * Video of a FULLY SICK skid

45. KNOW WHY 43 Explicit, cause-based

46. THE NONAKA CYCLE 44 HUMAN KNOW HOW KNOW WHY KNOW WHAT Organisational learning

47. THE NONAKA CYCLE 44 HUMAN KNOW HOW KNOW WHY KNOW WHATMACHINE KNOW HOW Organisational learning

48. THE NONAKA CYCLE 44 KNOW WHY KNOW WHATMACHINE KNOW HOW Organisational learning

49. THE NONAKA CYCLE 44 KNOW WHATMACHINE KNOW HOW Organisational learning

50. THE NONAKA CYCLE 44 MACHINE KNOW HOW

51. MACHINES BETTERING HUMANS, NOT JUST BESTING HUMANS 45 As Fan’s losses piled up against AlphaGo, [he] came to see Go in an entirely new way. Against other humans, he started winning more. Cade Metz Wired Just as machines made human muscles a thousand times stronger, machines will make the human brain a thousand times more powerful. Sebastian Thrun Google X

52. BUILDING THE NEW NONAKA CYCLE 46 HUMAN KNOW HOW KNOW WHY KNOW WHATMACHINE KNOW HOW Enrich

53. Enhanced Learning BUILDING THE NEW NONAKA CYCLE 46 HUMAN KNOW HOW KNOW WHY KNOW WHATMACHINE KNOW HOW Enrich

54. CHANGING JOB DESIGN 47 Alexandra Heath Head of Economic Analysis Department, RBA Carlos Perez Intuition Machine, University of Massachusetts Lowell Jobs that use automation as a tool Jobs that use humans as a safety valve against automation failure Jobs that interpret the decisions of machines Jobs that design human-machine interfaces Jobs that design automation to manipulate human behaviour

55. MANAGING RISK & ETHICS  WITH INTUITION

56. MACHINE FAILURE MODES 49 http://www.evolvingai.org/fooling Training Set Bias Fooled by Hidden Heuristics

57. HUMAN FAILURE MODES 50 Training Set Bias Google image search: “faces in things”@thisismoonlight Fooled by Hidden Heuristics

58. HUMAN FAILURE MODES 50 Training Set Bias Google image search: “faces in things”@thisismoonlight Execution Variability You are anywhere between two and six times as likely to be released if you're one of the ﬁrst three prisoners considered versus the last three prisoners considered. https://www.theguardian.com/law/2011/apr/11/judges-lenient-break Fooled by Hidden Heuristics

59. NO EXPLANATION - THE “SEMANTIC GAP” 51 BLACK BOX INTUITION KNOW WHY KNOW HOWSCENARIO

60. SOCIETAL IMPLICATIONS 52 According to recent reports, every Chinese citizen will receive a so- called ”Citizen Score” [based in part on deep learning against Baidu history], which will determine under what conditions they may get loans, jobs, or travel visa to other countries. https://www.scientificamerican.com/article/will-democracy-survive-big-data-and-artificial-intelligence/

61. SOME RESPONSES TO RISK & ETHICAL CHALLENGES 53 WEAPONS OF MATH DESTRUCTION 1. Are your objectives aligned with your customer’s? 2. Is your model’s operation opaque? 3. Is it “scaled” from a similar application, or likely to “scale” in turn to related applications? 4. Does your model create its own reality with feedback loops? EU RIGHT TO EXPLANATION General Data Protection Regulation to take eﬀect 2018. The law will eﬀectively create a “right to explanation” for users about whom algorithmic decisions were made. In its current form, the GDPR’s requirements could require a complete overhaul of standard and widely used algorithmic techniques. SELF-DRIVING CARS Mercedes will save occupants as a priority - they have taken a pre- meditated position. Volvo will accept liability for any incident involving one of their vehicles in autonomous mode.

62. CONCLUSION

63. OUTPUT LAYER 55 Machines are outperforming humans in narrow intuition tasks. This is due to a confluence of recent developments,  and innovation continues at incredible pace. This delivers enormous improvement potential, and has significant implications for how we design and develop products, and how we build and manage organisations. There are potentially huge benefits for society, but ethics and risk to be managed. Understanding machine intuition better will help us better manage these developments, and ultimately help us understand humans better.

64. END

Reasoning About Machine intuition- David Colls

Reasoning About Machine intuition- David Colls

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