Paradoxes and theorems every developer should know

Transcript

1. 1
   Joshua Thijssen
   jaytaph
   namespace

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2. Disclaimer:
   I'm not a (mad)
   scientist nor a
   mathematician.
   2
   

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3. Second disclaimer:
   I will only tell lies
   3
   

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4. German Tank
   Problem
   4
   

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9. 7
   k = number of elements
   m = largest number
   

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10. 72 + (72 / 4) - 1 = 89
    8
    

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11. 9
    Intelligence Statistics Actual
    June 1940 1000 169
    June 1941 1550 244
    August
    1942
    1550 327
    https://en.wikipedia.org/wiki/German_tank_problem
    

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12. 9
    Intelligence Statistics Actual
    June 1940 1000 169
    June 1941 1550 244
    August
    1942
    1550 327
    https://en.wikipedia.org/wiki/German_tank_problem
    122
    

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13. 9
    Intelligence Statistics Actual
    June 1940 1000 169
    June 1941 1550 244
    August
    1942
    1550 327
    https://en.wikipedia.org/wiki/German_tank_problem
    122
    271
    

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14. 9
    Intelligence Statistics Actual
    June 1940 1000 169
    June 1941 1550 244
    August
    1942
    1550 327
    https://en.wikipedia.org/wiki/German_tank_problem
    122
    271
    342
    

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16. 10
    ➡ Data leakage.
    

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17. 10
    ➡ Data leakage.
    ➡ User-id's, invoice-id's, etc
    

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18. 10
    ➡ Data leakage.
    ➡ User-id's, invoice-id's, etc
    ➡ Used to approximate the number of
    iPhones sold in 2008.
    

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19. 10
    ➡ Data leakage.
    ➡ User-id's, invoice-id's, etc
    ➡ Used to approximate the number of
    iPhones sold in 2008.
    ➡ Calculate approximations of datasets with
    (incomplete) information.
    

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20. ➡ Avoid (semi) sequential data to be leaked.
    ➡ Adding randomness and offsets will NOT
    solve the issue.
    ➡ Use UUIDs
    (better: timebased short IDs, you don't need UUIDs)
    11
    

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21. 12
    Collecting (big) data is easy
    Analyzing big data is the hard part.
    

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22. Confirmation Bias
    13
    

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23. 2 4 6
    14
    Z={…,−2,−1,0,1,2,…}
    

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24. 21%
    15
    

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25. 16
    5 8 ? ?
    If a card shows an even number on one face,
    then its opposite face is blue.
    

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26. < 10%
    17
    

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27. 18
    coke beer 35 17
    If you drink beer
    then you must be 18 yrs or older.
    

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28. 18
    coke beer 35 17
    If you drink beer
    then you must be 18 yrs or older.
    

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29. 18
    coke beer 35 17
    If you drink beer
    then you must be 18 yrs or older.
    

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30. Cognitive Adaption
    for social exchange
    19
    

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31. hint:
    Try and place your "technical
    problem" in a more social context.
    20
    

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32. BDD
    21
    

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33. 22
    5 8 ? ?
    If a card shows an even number on one face,
    then its opposite face is blue.
    

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34. 22
    5 8 ? ?
    If a card shows an even number on one face,
    then its opposite face is blue.
    

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35. 22
    5 8 ? ?
    If a card shows an even number on one face,
    then its opposite face is blue.
    

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36. TESTING
    23
    

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37. 24
    ➡ Step 1: Write code
    ➡ Step 2: Write tests
    ➡ Step 3: Profit
    

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38. public function isLeapYeap($year) {
    return ($year % 4 == 0);
    }
    25
    https://www.sundoginteractive.com/blog/confirmation-bias-in-unit-testing
    testIs1996ALeapYeap();
    testIs2000ALeapYeap();
    testIs2004ALeapYeap();
    testIs2008ALeapYeap();
    testIs2012ALeapYeap();
    testIs1997NotALeapYear();
    testIs1998NotALeapYear();
    testIs2001NotALeapYear();
    testIs2013NotALeapYear();
    

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39. public function isLeapYeap($year) {
    return ($year % 4 == 0);
    }
    25
    https://www.sundoginteractive.com/blog/confirmation-bias-in-unit-testing
    testIs1996ALeapYeap();
    testIs2000ALeapYeap();
    testIs2004ALeapYeap();
    testIs2008ALeapYeap();
    testIs2012ALeapYeap();
    testIs1997NotALeapYear();
    testIs1998NotALeapYear();
    testIs2001NotALeapYear();
    testIs2013NotALeapYear();
    

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40. public function isLeapYeap($year) {
    return ($year % 4 == 0);
    }
    26
    https://www.sundoginteractive.com/blog/confirmation-bias-in-unit-testing
    

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41. 27
    ➡ Tests where written based on actual code.
    ➡ Tests where written to CONFIRM actual
    code, not to DISPROVE actual code!
    

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42. 28
    TDD
    

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43. 29
    ➡ Step 1: Write tests
    ➡ Step 2: Write code
    ➡ Step 3: Profit, as less prone to confirmation
    bias (as there is nothing to bias!)
    

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44. Birthday paradox
    30
    

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45. Question:
    31
    > 50% chance
    4 march
    18 september
    5 december
    25 juli
    2 februari
    9 october
    

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46. 23 people
    32
    

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47. 366 persons = 100%
    33
    

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48. Collisions occur more
    often than you realize
    34
    

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49. Hash collisions
    35
    

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50. 16 bits means
    300 values before >50%
    collision probability
    36
    

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51. Watch out for:
    37
    ➡ Too small hashes.
    ➡ Unique data.
    ➡ Your data might be less "protected" as
    you might think.
    

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52. Heisenberg
    uncertainty
    principle
    38
    

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53. It's not about
    star trek
    (heisenberg compensators)
    39
    

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54. nor crystal meth
    40
    

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55. 41
    x position
    p momentum (mass x velocity)
    ħ 0.0000000000000000000000000000000001054571800 (1.054571800E-34)
    

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56. The more precise you
    know one property, the
    less you know the other.
    42
    

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57. This is NOT about
    observing!
    43
    

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58. Observer effect
    44
    heisenbug
    

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59. It's about trade-offs
    45
    

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60. Benford's law
    46
    

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61. Numbers beginning with 1 are
    more common than numbers
    beginning with 9.
    47
    

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62. Default behavior for
    natural numbers.
    48
    

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64. find . -name \*.php -exec wc -l {} \; | sort | cut -b 1 | uniq -c
    50
    

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65. find . -name \*.php -exec wc -l {} \; | sort | cut -b 1 | uniq -c
    50
    1073 1
    886 2
    636 3
    372 4
    352 5
    350 6
    307 7
    247 8
    222 9
    

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67. Bayesian filtering
    52
    

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68. What's the probability of an
    event, based on conditions that
    might be related to the event.
    53
    

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69. What is the chance that a
    message is spam when it
    contains certain words?
    54
    

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70. 55
    P(A|B)
    P(A)
    P(B)
    P(B|A)
    Probability event A, if event B (conditional)
    Probability event A
    Probability event B
    Probability event B, if event A
    

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71. 56
    ➡ Figure out the probability a {mail, tweet,
    comment, review} is {spam, negative} etc.
    

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72. ➡ 10 out of 50 comments are "negative".
    ➡ 25 out of 50 comments uses the word
    "horrible".
    ➡ 8 comments with the word "horrible" are
    marked as "negative".
    57
    

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73. 58
    negative
    "horrible"
    10 comments
    25 comments
    8 comments
    

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75. 60
    ➡ More words?
    ➡ Complex algorithm,
    ➡ but, we can assume that words are not
    independent from eachother
    ➡ Naive Bayes approach
    

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77. 62
    We must know
    beforehand which
    comments are
    negative?
    

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78. TRAINING SET
    63
    

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79. 64
    "Your product is horrible and does
    not work properly. Also, you suck."
    "I had a horrible experience with
    another product. But yours really
    worked well. Thank you!"
    Negative:
    Positive:
    

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80. $trainingset = [
    'negative' => [
    'count' => 1,
    'words' => [
    'product' => 1,
    'horrible' => 1,
    'properly' => 1,
    'suck' => 1,
    ],
    ],
    'positive' => [
    'count' => 1,
    'words' => [
    'horrible' => 1,
    'experience' => 1,
    'product' => 1,
    'thank' => 1,
    ],
    ],
    ];
    65
    

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81. 66
    $trainingset = [
    'negative' => [
    'count' => 631,
    'words' => [
    'product' => 521,
    'horrible' => 52,
    'properly' => 36,
    'suck' => 272,
    ],
    ],
    'positive' => [
    'count' => 1263,
    'words' => [
    'horrible' => 62,
    'experience' => 16,
    'product' => 311,
    'great' => 363
    'thank' => 63,
    ],
    ],
    ];
    

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82. 67
    ➡ You might want to filter stop-words first.
    ➡ You might want to make sure negatives are
    handled property "not great" => negative.
    ➡ Bonus points if you can spot sarcasm.
    

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83. ➡ Collaborative filtering (mahout):
    ➡ If user likes product A, B and C, what is the
    chance that they like product D?
    68
    

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84. 69
    Mess up your (training) data, and nothing can save you
    (except a training set reboot)
    

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85. ➡ Binomial probability
    70
    

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86. 71
    ➡ 30% change of acceptance for CFP
    ➡ 5 CFP's
    

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87. 71
    ➡ 30% change of acceptance for CFP
    ➡ 5 CFP's
    1 - (0.7 * 0.7 * 0.7 * 0.7 * 0.7) = 1 - 0.168 = 0.832
    83% on getting selected at least once!
    

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88. Ockham's Razor
    72
    

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89. 73
    Among competing hypotheses, the one with
    the fewest assumptions should be selected.
    

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90. 74
    82
    Everything should be made as
    simple as possible, but no simpler.
    

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91. YAGNI
    75
    

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92. 76
    Actually,
    ➡ The principle of plurality
    Plurality should not be posited with
    necessity.
    ➡ The principle of parsimony
    It is pointless to do more with what is
    done with less.
    

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93. ➡ Every element you add needs: design,
    development, maintenance, connectivity,
    support, etc etc.
    ➡ When "adding" elements, you are not
    adding, you are multiplying!
    77
    

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94. 78
    Food for thought:
    Would Ockham accept a
    Service Oriented
    Architecture?
    

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95. http://farm1.static.flickr.com/73/163450213_18478d3aa6_d.jpg 79
    

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96. 80
    Find me on twitter: @jaytaph
    Find me for development and training:
    www.noxlogic.nl / www.techademy.nl
    Find me on email: jthijssen@noxlogic.nl
    Find me for blogs: www.adayinthelifeof.nl
    

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