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Reasoning About Machine intuition- David Colls

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Machines are now bettering people at a range of specific intuition tasks. How are they doing this and what might be next?
What does this mean for the development and governance of intelligent products and services that we access as consumers and citizens, and what are the implications for the organisations that provide them?

David explores the impact across the product lifecycle, from customer perception, to job design, technology development, knowledge management, risk and ethics.

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Reasoning About Machine intuition- David Colls

  1. 1. Reasoning About MACHINE INTUITION David Colls
  2. 2. GENERAL NARROW MACHINE LEARNING DEEP LEARNING* REASONING INTUITING ARTIFICIAL INTELLIGENCE *AKA ARTIFICIAL NEURAL NETWORKS
  3. 3. MACHINE INTUITION, FTW
  4. 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. 5. PERFORMANCE LEAPS 5
  6. 6. PERFORMANCE LEAPS 6
  7. 7. PERFORMANCE LEAPS 7
  8. 8. BESTING HUMANS 8
  9. 9. BESTING HUMANS 9
  10. 10. BESTING HUMANS 10 Source: https://www.pokernews.com/strategy/how-to-bet-in-poker-tournaments-22371.htm
  11. 11. WHAT JUST HAPPENED?
  12. 12. WHAT IS AN ARTIFICIAL NEURAL NETWORK? 12 BEACH SEA SKY …
  13. 13. WHAT IS AN ARTIFICIAL NEURAL NETWORK? 12 © Fjodor van Veen 2016 BEACH SEA SKY …
  14. 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. 15. EXERCISE 13 1 2 3 4 5 A BEACH B C
  16. 16. NEURAL NETWORKS HAVE BEEN AROUND FOR A WHILE… 14 Dave’s 1994 university internship
  17. 17. …BUT THEN THIS HAPPENED 15 WEB-SCALE DATA 1 Data volumes double every year
  18. 18. Massive adoption of GPU (and TPU, HPU, FPGA) 2 DEDICATED PARALLEL HARDWARE …BUT THEN THIS HAPPENED 16
  19. 19. 3 ADVANCED NETWORK DESIGNS …BUT THEN THIS HAPPENED 17 © Fjodor van Veen 2016
  20. 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. 21. SO, WHAT’S NEXT?
  22. 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. 23. HUMANS AND MACHINES COMPLEMENTING EACH OTHER 21 MACHINES Wider learners Scalable thinkers HUMANS Faster learners Flexible thinkers
  24. 24. DISTRIBUTED MACHINE INTUITION 22 Apple iOS Core ML Google Federated Learning CLOUD TRAINING DEVICE INTUITION AGGREGATED DATA PRIVATE DATA FEDERATING LEARNING
  25. 25. PRO-ACTIVE DISCUSSION AND MANAGEMENT OF RISKS 23
  26. 26. 24 com·put·er /kəmˈpyo͞ odər/ noun (17th century) “one who computes”
  27. 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. 28. DESIGNING PRODUCTS WITH INTUITION
  29. 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. 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. 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. 32. CHOOSE THE RIGHT PROBLEMS 30https://medium.com/intuitionmachine
  33. 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. 34. DEVELOPING TECHNOLOGY WITH INTUITION
  35. 35. THE PROBLEM 33 XKCD (CC BY-NC 2.5)
  36. 36. THE NEW NEW PRODUCT DEVELOPMENT GAME 34 OBJECTIVES PRODUCT Validate RULES + SOFTWARE ARCHITECTURE Research & Analyse Develop (PEOPLE) CODE Verify Deploy
  37. 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. 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. 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. 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. 41. A GOOD PROBLEM TO HAVE? 39 XKCD (CC BY-NC 2.5)
  42. 42. BUILDING ORGANISATIONS WITH INTUITION
  43. 43. KNOW WHAT 41 Explicit, rule-based
  44. 44. KNOW HOW 42 Tacit, feel-based * Video of a FULLY SICK skid
  45. 45. KNOW WHY 43 Explicit, cause-based
  46. 46. THE NONAKA CYCLE 44 HUMAN KNOW HOW KNOW WHY KNOW WHAT Organisational learning
  47. 47. THE NONAKA CYCLE 44 HUMAN KNOW HOW KNOW WHY KNOW WHATMACHINE KNOW HOW Organisational learning
  48. 48. THE NONAKA CYCLE 44 KNOW WHY KNOW WHATMACHINE KNOW HOW Organisational learning
  49. 49. THE NONAKA CYCLE 44 KNOW WHATMACHINE KNOW HOW Organisational learning
  50. 50. THE NONAKA CYCLE 44 MACHINE KNOW HOW
  51. 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. 52. BUILDING THE NEW NONAKA CYCLE 46 HUMAN KNOW HOW KNOW WHY KNOW WHATMACHINE KNOW HOW Enrich
  53. 53. Enhanced Learning BUILDING THE NEW NONAKA CYCLE 46 HUMAN KNOW HOW KNOW WHY KNOW WHATMACHINE KNOW HOW Enrich
  54. 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. 55. MANAGING RISK & ETHICS
 WITH INTUITION
  56. 56. MACHINE FAILURE MODES 49 http://www.evolvingai.org/fooling Training Set Bias Fooled by Hidden Heuristics
  57. 57. HUMAN FAILURE MODES 50 Training Set Bias Google image search: “faces in things”@thisismoonlight Fooled by Hidden Heuristics
  58. 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. 59. NO EXPLANATION - THE “SEMANTIC GAP” 51 BLACK BOX INTUITION KNOW WHY KNOW HOWSCENARIO
  60. 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. 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. 62. CONCLUSION
  63. 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. 64. END
  • HYDN

    Jul. 7, 2018
  • DavidQuenault

    Oct. 23, 2017
  • DavidHead18

    Oct. 20, 2017

Machines are now bettering people at a range of specific intuition tasks. How are they doing this and what might be next? What does this mean for the development and governance of intelligent products and services that we access as consumers and citizens, and what are the implications for the organisations that provide them? David explores the impact across the product lifecycle, from customer perception, to job design, technology development, knowledge management, risk and ethics.

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