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Rethinking Who Gets What and Why (NABE)

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We like to think that we still live in a free-market economy, but our world is increasingly ruled by vast networked platforms (from Google and Facebook to our financial markets) that are ruled by algorithms. It is those algorithms that decide who gets what and why. It's time to take a closer look at what it takes to manage these digital djinns, who promise to give us what we say we want, but parse our requests so precisely that it is often not what we really want. My talk to the National Association of Business Economists #TEC2018 Conference in San Francisco on October 30, 2018. Many of the slides are just pictures, so be sure to read the narrative in the speaker notes.

Published in: Technology

Rethinking Who Gets What and Why (NABE)

  1. TIM O’REILLY Founder + CEO O’Reilly Media, Inc. Twitter » @timoreilly Rethinking Who Gets What and Why
  2. How is work changing? What does technology now make possible that was previously impossible? What work needs doing? How do we make the world prosperous for all? Why aren’t we doing it? wtfeconomy.com
  3. “My grandfather wouldn’t recognize what I do as work.” Hal Varian, Google Chief Economist
  4. Many of today’s workers are programs. Software developers are actually their managers. Every day, they are inspecting the performance of their workers and giving them instruction (in the form of code) about how to do a better job
  5. Software has become a set of ongoing business processes, not an artifact
  6. New skillsets are needed User Centered Design Site Reliability Engineering Data Science Deep Learning API Design Economics Market design
  7. We have to let go of the maps that are steering us wrong In 1625, we thought California was an island
  8. In 2018, it’s our maps of business and the economy that are wrong
  9. The invisible hand at work
  10. What happens when there’s only one queue? And it’s personalized for you?
  11. Amazon.com
  12. And what happens when there’s only one price for everything?
  13. Algorithms decide “who gets what – and why” Markets are outcomes. A better designed marketplace can have better outcomes.
  14. Price signaling is no longer the primary coordinator
  15. “Gradually, then suddenly” Ernest Hemingway
  16. Gradually, then suddenly Large segments of the economy are governed not by free markets but by centrally managed platforms
  17. “In an information-rich world, the wealth of information means a dearth of something else: a scarcity of whatever it is that information consumes. What information consumes is rather obvious: it consumes the attention of its recipients. Hence a wealth of information creates a poverty of attention and a need to allocate that attention efficiently.” Herbert Simon
  18. Algorithms have become a battleground Security: “That word does not mean what you think it means.”
  19. Users post 7 billion pieces of content to Facebook a day. Expecting human fact checkers to catch fake news is like asking workers to build a modern city with only picks and shovels. At internet scale, we now rely increasingly on algorithms to manage what we see and believe.
  20. Gradually, then suddenly Artificial Intelligence and algorithmic systems are everywhere, in new kinds of partnerships with humans
  21. “The hope is that, in not too many years, human brains and computing machines will be coupled together very tightly, and that the resulting partnership will think as no human brain has ever thought and process data in a way not approached by the information-handling machines we know today.” - J.C.R. Licklider, Man-Machine Symbiosis,1960
  22. We are all living and working inside a machine
  23. It’s no longer just in the digital realm
  24. An Amazon warehouse is a human-machine hybrid
  25. It makes things like this possible 68 million monthly users 440,000 employees 336 million monthly active users ~3400 employees
  26. Managing an algorithmic marketplace
  27. Governance in the age of algorithms  Must focus on outcomes, not on rules.  Must operate at the speed and scale of the systems it is trying to regulate.  Must incorporate real-time data feedback loops.  Must be robust in the face of failure and hostile attacks.  Must address the incentives that lead to misbehavior.  Must be constantly refined to meet ever-changing conditions.
  28. Real Time Digital Regulatory Systems Google search quality Social media feed organization Email spam filtering Credit card fraud detection Risk management and hedging
  29. Government and central bank statistics, economic modeling, and regulations are too slow for the pace and scale of the modern world “Would you cross the street with information that was five seconds old?”  - Jeff Jonas, CEO of Senzing, Former IBM Fellow
  30. “Why is policy still educated guesswork with a feedback loop measured in years?” Tom Loosemore, Former Deputy Director, UK Government Digital Service
  31. Governance too must be reshaped by the digital “This isn’t just how we should be developing software. It’s how we should be developing policy.” Cecilia Muñoz, Former Director, White House Domestic Policy Council
  32. Algorithmic systems have an “objective function” Google: Relevance Facebook: Engagement Uber and Lyft: Passenger pick up time Scheduling software used by McDonald’s, The Gap, or Walmart: Reduce employee costs and benefits Central banks: Control inflation? Employment? Interest rates?
  33. When platforms get their algorithms wrong, there can be serious consequences! When platforms get their objective function wrong, there can be serious consequences!
  34. Like the djinn of Arabian mythology, our digital djinni do exactly what we tell them to do
  35. Divergence of productivity and real median family income in the US
  36. “The art of debugging is figuring out what you really told your program to do rather than what you thought you told it to do.” Andrew Singer Andrew Singer
  37. The runaway objective function “Even robots with a seemingly benign task could indifferently harm us. ‘Let’s say you create a self- improving A.I. to pick strawberries,’ Musk said, ‘and it gets better and better at picking strawberries and picks more and more and it is self- improving, so all it really wants to do is pick strawberries. So then it would have all the world be strawberry fields. Strawberry fields forever.’ No room for human beings.” Elon Musk, quoted in Vanity Fair https://www.vanityfair.com/news/2017/03/elon-musk-billion-dollar-crusade-to-stop-ai-space-x
  38. We’ve built one of these already
  39. What is the objective function of our financial markets? “The Social Responsibility of Business Is to Increase Its Profits” Milton Friedman, 1970
  40. Is there really nothing left for humans to do?
  41. Dealing with climate change Rebuilding our infrastructure Feeding the world Ending disease Resettling refugees Caring for each other Educating the next generation Enjoying the fruits of shared prosperity
  42. This is what technology wants “Prosperity in human societies is best understood as the accumulation of solutions to human problems. We won’t run out of work until we run out of problems.” Nick Hanauer
  43. “A platform is when the economic value of everybody that uses it exceeds the value of the company that creates it. Then it's a platform.” – Bill Gates
  44. Once a platform stops creating more value for others than it captures for itself, people migrate elsewhere.
  45. Microsoft crushed its ecosystem
  46. How Industries Mature 1. Some new technology (the PC, the web, the smartphone) lowers the barriers to participation and innovation. 2. The market explodes as “hackers” push the envelope of possibility, and entrepreneurs make things easier for ordinary users. 3. The market stagnates as players become platforms, and raise barriers to entry. Hackers and entrepreneurs move on, looking for new frontiers. Or (rarely) 3. The industry builds a healthy ecosystem, in which hackers, entrepreneurs and platform companies play a creative game of "leapfrog". No one gets complete lock in, and everyone has to improve in order to stay competitive. Value is created for an entire ecosystem.
  47. Generosity takes us to the next peak Tim Berners-Lee, 1990 The World Wide Web Linus Torvalds, 1991 Linux Big Data and AI Tim Berners-Lee, 1990 The World Wide Web Linus Torvalds, 1991 Linux
  48. Google’s share of ad revenue over time O’Reilly Research
  49. Generous is also “Long-term greedy”
  50. “Self-interest properly regarded” A new inclusive opportunity ecosystem keeps the game going
  51. Nations fail for the same reason as tech platforms Inclusive economies outperform extractive economies. When inclusive economies fall prey to extractive elites, everyone is worse off.
  52. Growth goes on forever? One of the key drivers of corporate bad behavior is the command given them by financial markets that they must constantly grow and increase their profits
  53. An alternative: “Doughnut Economics” Kate Raworth
  54. Oikonomia vs Chrematistike
  55. O’Reilly Media ● Providing learning for almost 40 years ● Trends called – Open Source, Web 2.0, Maker Movement, Big Data ● 500 employees, thousands of contributors ● 5,000+ enterprise clients, 2.3m platform users globally ● 17 global technology events serving 20k individuals and 1,000 sponsor companies
  56. Change the world by spreading the knowledge of innovators
  57. “The opportunity for AI is to help humans model and manage complex interacting systems.” Paul R. Cohen
  58. “Computational Sustainability is a new interdisciplinary research field, with the overarching goal of studying and providing solutions to computational problems for balancing environmental, economic, and societal needs for a sustainable future. Such problems are unique in scale, impact, complexity, and richness, often involving combinatorial decisions, in highly dynamic and uncertain environments, offering challenges but also opportunities for the advancement of the state-of-the-art of computer and information science. Work in Computational Sustainability integrates in a unique way various areas within computer science and applied mathematics, such as constraint reasoning, optimization, machine learning, and dynamical systems.” Carla Gomes
  59. The great opportunity of the 21st century is to use our newfound cognitive tools to build sustainable businesses and economies
  60. Can we build an economic flywheel that keeps us in the doughnut?
  61. What’s the Future? It’s Up To us wtfeconomy.com
  62. Tim O’Reilly @timoreilly • O’Reilly AI Conference • Strata: The Business of Data • JupyterCon • O’Reilly Open Source Summit • Maker Faire • Foo Camp • … • 40,000+ ebooks • Tens of thousands of hours of video training • Live training • Millions of customers • A platform for knowledge exchange • Commercial internet • Open source software • Web 2.0 • Maker movement • Government as a platform • AI and The Next Economy Founder & CEO, O’Reilly Media Partner, O’Reilly AlphaTech Ventures Board member, Code for America Co-founder, Maker Media

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