Machine Platform Crowd by McAfee and Brynjolfsson , 2017

1. Machine Platform Crowdby McAfee and Brynjolfsson, 2017 Summary of Book – Part 1 by Charles Tappert, CSIS, Pace University The information presented here, although greatly condensed, comes almost entirely from the course textbook.

2. Chap 1 The Triple Revolution • Machine versus Human Mind (Part 1) • Alpha Go by Google DeepMind defeats top player • Platforms versus Products (Part 2) • Uber owns no vehicles, Facebook creates no content, Airbnb owns no real estate • Crowd versus Core (Part 3) • GE’s crowd sourcing development process for its Opal ice maker

3. Part 1: Chap 2The hardest thing to accept about ourselves • Humans reason in two different ways • System 1: intuition – fast, automatic, little effort, touted in books by former CEOs as “tough choices” • System 2: slow, conscious, lot of work, complex computations, refined by taking math/logic courses • Standard partnership of minds and machines • Twenty-year-old division of labor concept • Humans make smart decisions and computers take care of the math and record keeping

4. Part 1: Chap 2The hardest thing to accept about ourselves • Human judgement is often bad because System-1 reasoning is subject to many biases • We aggressively filter the information overload • We fill information gaps with assumptions • We need to act fast and jump to conclusions • Our memory is limited so we try to remember the important stuff but we often make errors

5. Part 1: Chap 2The hardest thing to accept about ourselves • The evidence is overwhelming that relying on data and algorithms alone is often best • Many decisions, judgements, and forecasts now made by humans should be made by algorithms • Perhaps with humans providing commonsense checks • In some cases the standard partnership should be inverted with human input quantified and included in the quantitative analyses • As technology improves we should move more to data-driven decision making

6. Part 1: Chap 3Our most mind-like machines • Learning a language • Adults learn a second language with lessons, rules, memorizing vocabulary, and lots of work • Children easily learn without lessons and rules • Similarly, the AI community split into two camps • Rule-based or symbolic AI had early successes • Statistical pattern recognition systems • Rosenblatt’s perceptrons learned by example

7. Part 1: Chap 3Our most mind-like machines • Rule-based, symbolic AI is now dormant • Machine learning is fulfilling its early promise • Getting better with increased size, specialized hardware (GPUs), and access to more data • Deep learning neural networks leading the way • Machine learning systems still lack common sense

8. Part 1: Chap 4Hi, robot • Virtualization has become a reality – digital devices almost everywhere • ATMs replace bank tellers, and PC-based online banking and banking apps add more convenience • Self-checkout technologies currently confusing but getting better • McDonald’s self-service ordering and payment system • Wealthfront’s digital wealth management advisor

9. Part 1: Chap 4Hi, robot • DANCE of the robots – robotics “Cambrian Explosion” [time major forms of life appeared] • Data – 90% generated in last 2 years • Algorithms – rapidly improving • Networks – wireless communication improving • Cloud – unprecedented computing power available • Exponential hardware improvement – Moore’s law

10. Part 1: Chap 4Hi, robot • Where the work is dull, dirty, dangerous, dear • Construction sites – drones check work against plans • Agriculture – drone sensors check crop health • 90% crop spraying in Japan by unmanned helicopters • Insurance companies – drones assess bldg damage • Rio Tinto remote-controlled trucks move iron ore • Automated milking machines

11. Part 1: Chap 4Hi, robot • People still more agile and dexterous than even the most advanced robots • Amazon not yet found a digital hand grabber • Amazon brings the shelves to the human who grabs the right products and boxes them for shipment • Humans still have a place working together with the machines, but for how much longer?

12. Part 1: Chap 4Hi, robot • The shape of things to come • We build things in ways never before possible • We build plastic parts of all shapes and sizes • Requires molds that must be precise, limits the possible shapes, and handle the heating and cooling processes • Now we have 3D printing – fuse thin layers • We even make metal parts – laser melts thin layers • 3D printing is an example of a new trend – the spread of digital tools into traditional manufacturing processes

13. Part 1: Chap 5Where technology & industry still need humanity • What abilities will remain uniquely human? • Some say “creativity” • But a computer recently used generative design software to design a unique heat exchanger

14. Part 1: Chap 5Where technology & industry still need humanity • A racecar chassis model was also created by machine

15. Part 1: Chap 5Where technology & industry still need humanity • But the arts are different – aren’t they? • Digital music composers make very good music • IBM’s Watson created a cookbook of recipes • Shanghai Tower design was computer generated • But it may be a while before a decent novel is written by a machine

16. Part 1: Chap 5Where technology & industry still need humanity • Digital technologies do a poor job of satisfying most of our social drive s • Tasks that require empathy, leadership, teamwork, and coaching • As technology advances, high-level social skills could become even more valuable

17. The Triple Revolution • Machine versus Human Mind (Part 1) • Alpha Go by Google DeepMind defeats top player • Platforms versus Products (Part 2) • Uber owns no vehicles, Facebook creates no content, Airbnb owns no real estate • Crowd versus Core (Part 3) • GE’s crowd sourcing development process for its Opal ice maker