Upcoming SlideShare
×

# Class 30: Sex, Religion, and Politics

968 views

Published on

Problems and Procedures
P=NP Recap
Endless Golden Ages
Golden Catastrophes
Optimism

Published in: Technology
0 Likes
Statistics
Notes
• Full Name
Comment goes here.

Are you sure you want to Yes No
• Be the first to comment

• Be the first to like this

Views
Total views
968
On SlideShare
0
From Embeds
0
Number of Embeds
2
Actions
Shares
0
10
0
Likes
0
Embeds 0
No embeds

No notes for slide
• (define (computing-power nyears) (if (= nyears 0) 1 (* 1.587 (computing-power (- nyears 1)))));;; Simulation is (n3) work(define (simulation-work scale) (* scale scalescale)) (define (log10 x) (/ (log x) (log 10))) ;;; log is base e;;; knowledge of the universe is log10 the scale of universe ;;; we can simulate(define (knowledge-of-universe scale) (log10 scale))
• (define (computing-power nyears) (if (= nyears 0) 1 (* 1.587 (computing-power (- nyears 1)))));;; Simulation is (n3) work(define (simulation-work scale) (* scale scalescale)) (define (log10 x) (/ (log x) (log 10))) ;;; log is base e;;; knowledge of the universe is log10 the scale of universe ;;; we can simulate(define (knowledge-of-universe scale) (log10 scale))
• ### Class 30: Sex, Religion, and Politics

1. 1. Class 30:Sex, Religion, andPoliticscs1120 Fall 2011David Evans2 November 2011
2. 2. PlanRecap big ideas from Monday (without Halloweenization)Tyson’s Science’s Endless Golden Age 2
3. 3. What is a problem? 3
4. 4. Procedures vs. ProblemsProcedure: Problem: a well-defined description of an sequence of steps input and desired that can be executed output mechanically (i.e., by a Turing Machine) 4
5. 5. Example: Factoring Factoring Procedure Factoring Problemdef factors(n): Input: a number, n, that is res = [] a product of two primes for d in range(2, n): numbers if n % d == 0: Output: two prime res.append(d) numbers, p and q, such return res that pq = nWhat is the running time of What is the time complexitythis factoring procedure? of the factoring problem? 5
6. 6. P=NP ?P = set of all problems that can be solved by algorithms with running times in O(Nk) where N is the input size and k is any constant.NP = set of all problems whose answer can be checked by algorithms with running times in O(Nk) where N is the input size and k is any constant.
7. 7. What about factoring?How hard is the factoring problem? Input: a number, n Output: two integers > 1, p and q, such that pq = nHow hard is the CHECK-FACTORS problem? Input: p, q, n (three integers, all greater than 1) Output: true if n = pq, false otherwise
8. 8. ACM1: Beyond Cyberspace (2001) Neil de Grasse Tyson: "Awash in Data, Awash in Discoveries: Defining the Golden Age of Science“ Rice Hall Dedication Speaker! Alan Kay: "The Computer Revolution Hasnt Happened Yet"Friday,President and Disney Fellow, Walt Disney (Vice Nov 18, 3pm: “The FutureBelongs to the Innovators” Imagineering) Dean Kamen: "Engineering the Future Depends on Future Engineers" (President and Owner, DEKA Research & Development Corporation and Founder of FIRST)
9. 9. Science’s Endless Golden Age
10. 10. “If you’re going to use your computer tosimulate some phenomenon in theuniverse, then it only becomes interestingif you change the scale of thatphenomenon by at least a factor of 10. …For a 3D simulation, an increase by a factorof 10 in each of the three dimensionsincreases your volume by a factor of 1000.” What is the asymptotic running time for simulating the universe?
11. 11. “If you’re going to use yourcomputer to simulate somephenomenon in theuniverse, then it only becomesinteresting if you change thescale of that phenomenon by atleast a factor of 10. … For a 3Dsimulation, an increase by afactor of 10 in each of the threedimensions increases yourvolume by a factor of 1000.”
12. 12. Simulating the UniverseWork scales linearly with volume ofsimulation: scales cubically with scale (n3) When we double the scale of the simulation, the work octuples! (Just like oceanography octopi simulations)
13. 13. Orders of Growth140 n3 simulating120 universe100 80 60 40 n2 20 n 0 1 2 3 4 5
14. 14. Orders of Growth1400000 simulating1200000 universe1000000 800000 600000 400000 200000 0 1 11 21 31 41 51 61 71 81 91 101
15. 15. Orders of Growth7E+32 factors 2n6E+325E+324E+323E+322E+321E+32 0 simulating 1 11 21 31 41 51 61 71 81 91 101 universe
16. 16. Astrophysics and Moore’s LawSimulating universe is (n 3)Moore’s “law”: computing power doubles every 18 monthsDr. Tyson: to understand something new about the universe, need to scale by 10x How long does it take to know twice as much about the universe?
17. 17. Knowledge of the Universeimport math# 18 months * 2 = 12 months * 3yearlyrate = math.pow(4, 1.0/3.0) # cube rootdef simulation_work(scale): return scale ** 3def knowledge_of_universe(scale): return math.log(scale, 10) # log base 10def computing_power(nyears):
18. 18. Knowledge of the Universeimport math# 18 months * 2 = 12 months * 3yearlyrate = math.pow(4, 1.0/3.0) # cube rootdef simulation_work(scale): return scale ** 3def knowledge_of_universe(scale): return math.log(scale, 10) # log base 10 def computing_power(nyears):def computing_power(nyears): return yearlyrate ** nyears if nyears == 0: return 1 else: return yearlyrate * computing_power(nyears - 1)
19. 19. Knowledge of the Universedef computing_power(nyears): return yearlyrate ** nyearsdef simulation_work(scale): return scale ** 3def knowledge_of_universe(scale): return math.log(scale, 10) # log base 10def relative_knowledge_of_universe(nyears): scale = 1 while simulation_work(scale + 1) <= 1000 * computing_power(nyears): scale = scale + 1 return knowledge_of_universe(scale)
20. 20. While Loop Statement ::= while Expression: Block (define (while pred body) x=1 (if (pred)x =res = 0 1 (begin (body)reswhile x < 100: =0 (while pred body))))while x < 100:+ x res = res (define x 1) resx= res + x =x+1 (define sum 0)print resres print (while (lambda () (< x 100)) (lambda () (set! sum (+ sum x)) (set! x (+ x 1))))
21. 21. Knowledge of the Universedef computing_power(nyears): return yearlyrate ** nyearsdef simulation_work(scale): return scale ** 3def knowledge_of_universe(scale): return math.log(scale, 10) # log base 10def relative_knowledge_of_universe(nyears): scale = 1 while simulation_work(scale + 1) <= 1000 * computing_power(nyears): scale = scale + 1 return knowledge_of_universe(scale) (Note: with a little bit of math, could compute this directly using a log instead.)
22. 22. > >>> relative_knowledge_of_universe(0)1.0>>> relative_knowledge_of_universe(1)1.0413926851582249>>> relative_knowledge_of_universe(2)1.1139433523068367>>> relative_knowledge_of_universe(10)1.6627578316815739>>> relative_knowledge_of_universe(15)2.0>>> relative_knowledge_of_universe(30)3.0064660422492313>>> relative_knowledge_of_universe(60)5.0137301937137719>>> relative_knowledge_of_universe(80)6.351644238569782 Will there be any mystery left in the Universe when you die?
23. 23. Only two things areinfinite, the universeand humanstupidity, and Imnot sure about theformer. Albert Einstein
24. 24. The Endless Golden AgeGolden Age – period in which knowledge/quality of something improves exponentiallyAt any point in history, half of what is known about astrophysics was discovered in the previous 15 years! Moore’s law today, but other advances previously: telescopes, photocopiers, clocks, agriculture, etc. Accumulating 4% per year => doubling every 15 years!
25. 25. Endless/Short Golden AgesEndless golden age: at any point in history, the amount known is twice what was known 15 years ago Continuous exponential growth: (kn) k is some constant (e.g., 1.04), n is number of yearsShort golden age: knowledge doubles during a short, “golden” period, but only improves linearly most of the time Mostly linear growth: (n) n is number of years
26. 26. Average Goals per Game, FIFA World Cups 3 4 5 6 21930193419381950195419581962 Goal-den age1966197019741978198219861990 passback rule1994 Changed goalkeeper1998200220062010
27. 27. 27
28. 28. Computing Power 1969-2011 (in Apollo Control Computer Units)300,000,000250,000,000200,000,000150,000,000100,000,000 50,000,000 0
29. 29. Computing Power 1969-1990 (in Apollo Control Computer Units)18,00016,00014,00012,00010,000 8,000 6,000 4,000 2,000 0 .5 .5 .5 .5 .5 .5 .5 69 72 75 78 81 84 87 90 70 73 76 79 82 85 88 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19
30. 30. Computing Power 2008-2050? 450,000 400,000 “There’s an unwritten rule in 350,000 astrophysics: your computer 30 years from now?! 300,000 simulation must end before you die.” 250,000 Neil deGrasse Tyson 200,000 150,000 Human brain:Brain simulator today (IBM Blue Brain): ~100 Billion 100,00010,000 neurons neurons30 Million connections 50,000 100 Trillion connections 0 2008 2011 2014 2017 2020 2023 2026 2029 2032 2035 2038 2041 2044 2047 2050 2011 = 1 computing unit
31. 31. More Moore’s Law? “Any physical quantity that’s growing Current technologies are already exponentially predicts a disaster, you simply slowing down... can’t go beyond certain major limits.” Gordon Moore (2007) An analysis of the history of technology shows that technological change is exponential, contrary to the common-sense intuitive linear view. So we wont experience 100 years of progress in the 21st century-it will be more like 20,000 years of progress (at today’s rate). The returns, such as chip speed and cost- effectiveness, also increase exponentially. There’s even exponential growth in the rate of exponential growth. Within a few decades, machine intelligence will surpass human intelligence, leading to the Singularity — technological change so rapid and profound it represents a rupture in the fabric of human history. The implications include the merger of biological and non-biological intelligence, immortal software-based humans, and ultra-high levels of intelligence that expand outward in the universe at the speed of light.but advances won’t come from more transistors with Ray Kurzweilcurrent technologies...newtechnologies, algorithms, parallelization, architectures,
32. 32. Log scale: straight line = exponential curve!32
33. 33. The Real Golden Rule?Why do fields like astrophysics, medicine, biology andcomputer science have “endless golden ages”, butfields likemusic (1775-1825)rock n’ roll (1962-1973)*philosophy (400BC-350BC?)art (1875-1925?)soccer (1950-1966)baseball (1925-1950?)movies (1920-1940?)have short golden ages? * or whatever was popular when you were 16.
34. 34. Golden Ages orGolden Catastrophes?
35. 35. PS5, Question 1eQuestion 1: For each f and g pair below, argueconvincingly whether or not g is (1) O(f), (2)Ω(f), and (3) Θ(g) …(e) g: the federal debt n years from today, f: the US population n years from today
36. 36. Malthusian CatastropheReverend ThomasRobertMalthus, Essay onthe Principle ofPopulation, 1798
37. 37. 37
38. 38. Malthusian Catastrophe“The great and unlooked for discoveries that have takenplace of late years in natural philosophy, the increasingdiffusion of general knowledge from the extension ofthe art of printing, the ardent and unshackled spirit ofinquiry that prevails throughout the lettered and evenunlettered world, … have all concurred to lead manyable men into the opinion that we were touching on aperiod big with the most important changes, changesthat would in some measure be decisive of the futurefate of mankind.”
39. 39. Malthus’ Postulates“I think I may fairly make two postulata. First, that food is necessary to the existence of man. Secondly, that the passion between the sexes is necessary and will remain nearly in its present state. These two laws, ever since we have had any knowledge of mankind, appear to have been fixed laws of our nature, and, as we have not hitherto seen any alteration in them, we have no right to conclude that they will ever cease to be what they now are…”
40. 40. Malthus’ Conclusion“Assuming then my postulata as granted, Isay, that the power of population is indefinitelygreater than the power in the earth to producesubsistence for man. Population, when unchecked, increases in ageometrical ratio. Subsistence increases only inan arithmetical ratio. A slight acquaintancewith numbers will show the immensity of thefirst power in comparison of the second.”
41. 41. Malthusian CatastrophePopulation growth is geometric: (kn) (k > 1)Food supply growth is linear: (n) What does this mean as n ?Food per person = food supply / population = (n) / (kn)As n approaches infinity, food per personapproaches zero!
42. 42. Malthus’Fallacy?
43. 43. Malthus’ FallacyHe forgot how he started:“The great and unlooked for discoveries thathave taken place of late years in naturalphilosophy, the increasing diffusion of generalknowledge from the extension of the art ofprinting, the ardent and unshackled spirit ofinquiry that prevails throughout the letteredand even unlettered world…”
44. 44. Golden Age of Food ProductionAgriculture is an “endless golden age”field: production from the same land increasesas ~ (1.02n) Increasing knowledge of farming, weather forecasting, plant domestication, genetic engineering, pest repellants, distribution channels, preservatives, etc.
45. 45. Growing Corn1906: < 1,000 2006: 10,000pounds per acre pounds per acre Michael Pollan’s The Omnivore’s Dilemma
46. 46. Note: Log axis! Corn Yield http://www.agbioforum.org/v2n1/v2n1a10-ruttan.htm
47. 47. 47
48. 48. Green Revolution Norman Borlaug (1914-2009)
49. 49. “At a time when doom-sayers were hopping around sayingeveryone was going to starve, Norman was working. He movedto Mexico and lived among the people there until he figuredout how to improve the output of the farmers. So that saved amillion lives. Then he packed up his family and moved toIndia, where in spite of a war with Pakistan, he managed tointroduce new wheat strains that quadrupled their foodoutput. So that saved another million. You get it? But he wasntdone. He did the same thing with a new rice in China. Hesdoing the same thing in Africa -- as much of Africa as hesallowed to visit. When he won the Nobel Prize in 1970, theysaid he had saved a billion people. Thats BILLION! BUH! ThatsCarl Sagan BILLION with a "B"! And most of them were adifferent race from him. Norman is the greatest humanbeing, and you probably never heard of him.” Penn Jillette (Penn & Teller)
50. 50. Malthus was wrong about #2 AlsoAdvances in science (birthcontrol), medicine (higher lifeexpectancy), education, and societaland political changes (e.g., regulationin China) have reduced k (it is < 1 inmany countries now!)
51. 51. Upcoming Malthusian Catastrophes?Human consumption of fossil fuels grows as (kn) (fairly large k like 1.08?)Available fuel is constant (?) http://wwwwp.mext.go.jp/hakusyo/book/hpag200001/hpag200001_2_006.html
52. 52. PS4, Question 1e g: the federal debt n years from today, f: the US population n years from todayDebt increases: Spending – Revenues this varies, but usually positive + Interest on the previous debt (exponential) = (kn)Population increase is not exponential:rate continues to decrease => as n increases, debt per person approaches infinity! This will eventually be a problem, but growth analysis doesn’t say when.
53. 53. “Cornucopian View”Few resources are really finiteAll scientific things have endless golden ages Knowledge accumulates Knowledge makes it easier to acquire more(We hope) Human ingenuity and economics and politics will continue solve problems before they become catastrophes No one will sell the last gallon of gas for \$3.35
54. 54. “Today, of Americansofficially designated as‘poor’, 99 percent haveelectricity, runningwater, flush toilets, and arefrigerator; 95 percent havea television, 88 percent atelephone, 71 percent a carand 70 percent airconditioning. CorneliusVanderbilt had none ofthese.” Matt Ridley 54
55. 55. Charles Vest’s slide idea! America will always do the right thing but only after exhausting all other options.Winston Churchill 55
56. 56. “Kay”-sian ViewThe best way topredict thefuture is toinvent it. — Alan Kay
57. 57. ChargeWhen picking majors:• Pick a short golden age field that is about to enter its short golden age: this requires vision and luck!• Or, play it safe by picking an endless golden age field (CS is a good choice for this!)Friday: Python Dictionaries History of Object-Oriented Programming