Reasoning About Machine Intuition- David Colls (By ThoughtWorks)

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/

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 © Fjodor van Veen 2016 BEACH SEA SKY … 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

13. NEURAL NETWORKS HAVE BEEN AROUND FOR A WHILE… 13 Dave’s 1994 university internship

14. …BUT THEN THIS HAPPENED 14 WEB-SCALE DATA 1 Data volumes double every year

15. Massive adoption of GPU (and TPU, HPU, FPGA) 2 DEDICATED PARALLEL HARDWARE …BUT THEN THIS HAPPENED 15

16. 3 ADVANCED NETWORK DESIGNS …BUT THEN THIS HAPPENED 16 © Fjodor van Veen 2016

17. NETWORK PARAMETERS - STATE OF THE ART 17 2013 201720152014 20162012 160 B 11.2 B 1.7 B 1 Billion 10 Billion 100 Billion 1,000 Billion

18. SO, WHAT’S NEXT?

19. EXPLOSION OF MICRO-INTUITORS 19 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

20. HUMANS AND MACHINES COMPLEMENTING EACH OTHER 20 MACHINES Wider learners Scalable thinkers HUMANS Faster learners Flexible thinkers

21. DISTRIBUTED MACHINE INTUITION 21 Apple iOS Core ML Google Federated Learning CLOUD TRAINING DEVICE INTUITION AGGREGATED DATA PRIVATE DATA FEDERATING LEARNING

22. PRO-ACTIVE DISCUSSION AND MANAGEMENT OF RISKS 22

23. 23 com·put·er /kəmˈpyo͞ odər/ noun (17th century) “one who computes”

24. de·ci·sion mak·er /dəˈsiZHən/ ˈ/mākər/ noun (20th century) “one who decides” 24 Source: https://www.pokernews.com/strategy/how-to-bet-in-poker-tournaments-22371.htm

25. DESIGNING PRODUCTS WITH INTUITION

26. UNDERSTAND CUSTOMER PERCEPTION 26 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

27. BUILD CUSTOMER TRUST 27 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 Attribute s 4 AUTONOMY & CONTROL 5 GRADUAL INTRODUCTION 6 PRO-ACTIVE COMMUNICATIONS Key Strategie s

28. TAKE A HUMAN-CENTRED APPROACH 28 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

29. CHOOSE THE RIGHT PROBLEMS 29https://medium.com/intuitionmachine

30. BUSINESS VS GAMEPLAY 30 BUSINESS GAMEPLAY Goal Environment Limited Resources Moves Taking Turns Scoring More freedom. Act ethically! Less freedom. Follow the rules! Necessarily complex Simplification reveals insight

31. DEVELOPING TECHNOLOGY WITH INTUITION

32. THE PROBLEM 32 XKCD (CC BY-NC 2.5)

33. THE NEW NEW PRODUCT DEVELOPMENT GAME 33 OBJECTIVES PRODUCT Validate RULES + SOFTWARE ARCHITECTURE Research & Analyse Develop (PEOPLE) CODE Verify Deploy

34. THE NEW NEW NEW PRODUCT DEVELOPMENT GAME 34 OBJECTIVES PRODUCT Validate RULES + SOFTWARE ARCHITECTURE Research & Analyse DATA SET + NETWORK ARCHITECTURE Curate Data Deploy Develop (PEOPLE) CODE Verify Deploy Train (MACHINES) MODEL Verify

35. DATA CURATION 35 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

36. MACHINES TRAINING 36 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

37. SKILLS SETS 37 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 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 Edge deployment • iOS Core ML

38. BUILDING ORGANISATIONS WITH INTUITION

39. KNOW WHAT 39 Explicit, rule-based

40. KNOW HOW 40 Tacit, feel-based

41. KNOW WHY 41 Explicit, cause-based

42. THE NONAKA CYCLE 42 HUMAN KNOW HOW KNOW WHY KNOW WHATMACHINE KNOW HOW Organisational learning

43. MACHINES BETTERING HUMANS, NOT JUST BESTING HUMANS 43 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

44. Enhanced Learning BUILDING THE NEW NONAKA CYCLE 44 HUMAN KNOW HOW KNOW WHY KNOW WHATMACHINE KNOW HOW Enrich

45. CHANGING JOB DESIGN 45 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

46. MANAGING RISK & ETHICS WITH INTUITION

47. MACHINE FAILURE MODES 47 http://www.evolvingai.org/fooling Recognising the Wrong ThingsTraining Set Bias

48. HUMAN FAILURE MODES 48 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 Recognising the Wrong Things

49. NO EXPLANATION - THE “SEMANTIC GAP” 49 BLACK BOX INTUITION KNOW WHY KNOW HOWSCENARIO

50. SOCIETAL IMPLICATIONS 50 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/

51. SOME RESPONSES TO RISK & ETHICAL CHALLENGES 51 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 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.

52. CONCLUSION

53. OUTPUT LAYER 53 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.

54. END