AI ndash Sep2018 ndash Alisson Sol ndash [1]

AI circa 2018

bull How to solve quadratic equationbull 1198861199092 + 119887119909 + 119888 = 0

bull Who is learning better in new airplane mission simulator

bull What is the next number in the sequence11 21 1211 111221

Pictures Wikimedia Commons license or taken by author

Pilot Week 1 Week 2

Olivia 0 75

Oliver 25 100

bull While we wait to start think about problems in the righthellip

AI ndash Sep2018 ndash Alisson Sol ndash [2]

Quadratic equation solution

bull From Algebra

bull 119909 =minus119887plusmn 1198872minus4119886119888

2119886

bull Why do we care

AI ndash Sep2018 ndash Alisson Sol ndash [3]

Going to higher ordershellip

bull Cubic equationhellip Anyonebull 1198861199093 + 1198871199092 + 119888119909 + 119889 = 0

bull Specific examplehellipbull 1199093 minus 21199092 minus 5119909 + 6 = 0

bull What ifhellipbull We plot it

bull Or could 119904119900119897119907119890 119894119905

AI ndash Sep2018 ndash Alisson Sol ndash [2]

Quadratic equation solution

bull From Algebra

bull 119909 =minus119887plusmn 1198872minus4119886119888

2119886

bull Why do we care

AI ndash Sep2018 ndash Alisson Sol ndash [3]

Going to higher ordershellip

bull Cubic equationhellip Anyonebull 1198861199093 + 1198871199092 + 119888119909 + 119889 = 0

bull Specific examplehellipbull 1199093 minus 21199092 minus 5119909 + 6 = 0

bull What ifhellipbull We plot it

bull Or could 119904119900119897119907119890 119894119905

AI ndash Sep2018 ndash Alisson Sol ndash [3]

Going to higher ordershellip

bull Cubic equationhellip Anyonebull 1198861199093 + 1198871199092 + 119888119909 + 119889 = 0

bull Specific examplehellipbull 1199093 minus 21199092 minus 5119909 + 6 = 0

bull What ifhellipbull We plot it

bull Or could 119904119900119897119907119890 119894119905

AI ndash Sep2018 ndash Alisson Sol ndash [5]

Evolution of AI

bull Math

bull Memory

bull Models

AI ndash Sep2018 ndash Alisson Sol ndash [6]

Which AI

bull Todaybull Applied AI (aka ldquoweak AIrdquo or ldquonarrow AIrdquo)

bull Out of scopebull Artificial General Intelligence aka ldquostrong AIrdquo or ldquofull AIrdquo

bull Consciousnessbull Like in movies and TV series 2001 A Space Odyssey AI Her Humans Westworld hellip

AI ndash Sep2018 ndash Alisson Sol ndash [6]

Which AI

bull Todaybull Applied AI (aka ldquoweak AIrdquo or ldquonarrow AIrdquo)

bull Out of scopebull Artificial General Intelligence aka ldquostrong AIrdquo or ldquofull AIrdquo

bull Consciousnessbull Like in movies and TV series 2001 A Space Odyssey AI Her Humans Westworld hellip

AI ndash Sep2018 ndash Alisson Sol ndash [7]

Numerical Calculus Newton (~1685-1740)

AI ndash Sep2018 ndash Alisson Sol ndash [8]

1 second

2315 days

AI ndash Sep2018 ndash Alisson Sol ndash [9]

Usual development

Output

Input

Processor

Code

AI ndash Sep2018 ndash Alisson Sol ndash [10]

Computational power enabled brute forcehellip

bull k-Nearest Neighborsbull Pros accuracy insensitive to outliers little ldquodata preparationrdquo

bull Cons computationally expensive

bull Basic idea ldquoTell me who you walk withhelliprdquo

AI ndash Sep2018 ndash Alisson Sol ndash [11]

Movies data

Source book Machine Learning in Action by Peter Harrington

Movie of kicks of kisses Type

A 3 104 Romance

B 2 100 Romance

C 1 81 Romance

D 101 10 Action

E 99 5 Action

F 98 2 Action

18 90 Unknown

AI ndash Sep2018 ndash Alisson Sol ndash [8]

1 second

2315 days

AI ndash Sep2018 ndash Alisson Sol ndash [9]

Usual development

Output

Input

Processor

Code

AI ndash Sep2018 ndash Alisson Sol ndash [10]

Computational power enabled brute forcehellip

bull k-Nearest Neighborsbull Pros accuracy insensitive to outliers little ldquodata preparationrdquo

bull Cons computationally expensive

bull Basic idea ldquoTell me who you walk withhelliprdquo

AI ndash Sep2018 ndash Alisson Sol ndash [11]

Movies data

Source book Machine Learning in Action by Peter Harrington

Movie of kicks of kisses Type

A 3 104 Romance

B 2 100 Romance

C 1 81 Romance

D 101 10 Action

E 99 5 Action

F 98 2 Action

18 90 Unknown

AI ndash Sep2018 ndash Alisson Sol ndash [9]

Usual development

Output

Input

Processor

Code

AI ndash Sep2018 ndash Alisson Sol ndash [10]

Computational power enabled brute forcehellip

bull k-Nearest Neighborsbull Pros accuracy insensitive to outliers little ldquodata preparationrdquo

bull Cons computationally expensive

bull Basic idea ldquoTell me who you walk withhelliprdquo

AI ndash Sep2018 ndash Alisson Sol ndash [11]

Movies data

Source book Machine Learning in Action by Peter Harrington

Movie of kicks of kisses Type

A 3 104 Romance

B 2 100 Romance

C 1 81 Romance

D 101 10 Action

E 99 5 Action

F 98 2 Action

18 90 Unknown

AI ndash Sep2018 ndash Alisson Sol ndash [10]

Computational power enabled brute forcehellip

bull k-Nearest Neighborsbull Pros accuracy insensitive to outliers little ldquodata preparationrdquo

bull Cons computationally expensive

bull Basic idea ldquoTell me who you walk withhelliprdquo

AI ndash Sep2018 ndash Alisson Sol ndash [11]

Movies data

Source book Machine Learning in Action by Peter Harrington

Movie of kicks of kisses Type

A 3 104 Romance

B 2 100 Romance

C 1 81 Romance

D 101 10 Action

E 99 5 Action

F 98 2 Action

18 90 Unknown

AI ndash Sep2018 ndash Alisson Sol ndash [11]

Movies data

Source book Machine Learning in Action by Peter Harrington

Movie of kicks of kisses Type

A 3 104 Romance

B 2 100 Romance

C 1 81 Romance

D 101 10 Action

E 99 5 Action

F 98 2 Action

18 90 Unknown

AI ndash Sep2018 ndash Alisson Sol ndash [12]

Movies Scatter chart

AB

C

DEF

0

20

40

60

80

100

120

0 20 40 60 80 100 120

o

f ki

sse

s

of kicks

119889 = (119909119886 minus 119909119887)2+(119910119886 minus 119910119887)2

AI ndash Sep2018 ndash Alisson Sol ndash [13]

Movies Distance

Movie of kicks of kisses Type d

18 90 Unknown 00

A 3 104 Romance 205

B 2 100 Romance 189

C 1 81 Romance 192

D 101 10 Action 1153

E 99 5 Action 1174

F 98 2 Action 1189

AI ndash Sep2018 ndash Alisson Sol ndash [14]

Movies Sorted Distance

Movie of kicks of kisses Type d

18 90 Unknown 00

B 2 100 Romance 189

C 1 81 Romance 192

A 3 104 Romance 205

D 101 10 Action 1153

E 99 5 Action 1174

F 98 2 Action 1189

if kNN = 3NN thenhellip

AI ndash Sep2018 ndash Alisson Sol ndash [15]

What If (BI Scenarios)

AB

C

DEF

0

20

40

60

80

100

120

0 20 40 60 80 100 120

o

f ki

sse

s

of kicks

AI ndash Sep2018 ndash Alisson Sol ndash [16]

Pre-calculated ldquoborderrdquo

A

R

R

R

R

R

R

R

R

R

R

R

R

A

A

R

R

R

R

R

R

R

R

R

R

R

A

A

A

R

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R

R

R

R

R

R

R

R

A

A

A

A

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R

A

A

A

A

A

R

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R

R

R

R

R

A

A

A

A

A

A

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R

R

R

R

R

A

A

A

A

A

A

A

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R

R

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R

A

A

A

A

A

A

A

A

R

R

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R

R

A

A

A

A

A

A

A

A

A

R

R

R

R

A

A

A

A

A

A

A

A

A

A

R

R

R

A

A

A

A

A

A

A

A

A

A

A

R

R

A

A

A

A

A

A

A

A

A

A

A

A

R

A

A

A

A

A

A

A

A

A

A

A

A

A

0

20

40

60

80

100

120

0 20 40 60 80 100 120

AI ndash Sep2018 ndash Alisson Sol ndash [17]

Cluster representation

AB

C

DEF

0

20

40

60

80

100

120

0 20 40 60 80 100 120

o

f ki

sse

s

of kicks

CentroidsRomance = [295]Action = [9933567]

AI ndash Sep2018 ndash Alisson Sol ndash [18]

Right method and correctly implemented

A Few Useful Things to KnowAbout Machine Learningby Pedro Domingos

AI ndash Sep2018 ndash Alisson Sol ndash [13]

Movies Distance

Movie of kicks of kisses Type d

18 90 Unknown 00

A 3 104 Romance 205

B 2 100 Romance 189

C 1 81 Romance 192

D 101 10 Action 1153

E 99 5 Action 1174

F 98 2 Action 1189

AI ndash Sep2018 ndash Alisson Sol ndash [14]

Movies Sorted Distance

Movie of kicks of kisses Type d

18 90 Unknown 00

B 2 100 Romance 189

C 1 81 Romance 192

A 3 104 Romance 205

D 101 10 Action 1153

E 99 5 Action 1174

F 98 2 Action 1189

if kNN = 3NN thenhellip

AI ndash Sep2018 ndash Alisson Sol ndash [15]

What If (BI Scenarios)

AB

C

DEF

0

20

40

60

80

100

120

0 20 40 60 80 100 120

o

f ki

sse

s

of kicks

AI ndash Sep2018 ndash Alisson Sol ndash [16]

Pre-calculated ldquoborderrdquo

A

R

R

R

R

R

R

R

R

R

R

R

R

A

A

R

R

R

R

R

R

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R

A

A

A

R

R

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R

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R

A

A

A

A

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A

A

A

A

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A

A

A

A

A

A

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A

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A

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A

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A

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A

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A

A

A

A

A

A

A

R

A

A

A

A

A

A

A

A

A

A

A

A

A

0

20

40

60

80

100

120

0 20 40 60 80 100 120

AI ndash Sep2018 ndash Alisson Sol ndash [17]

Cluster representation

AB

C

DEF

0

20

40

60

80

100

120

0 20 40 60 80 100 120

o

f ki

sse

s

of kicks

CentroidsRomance = [295]Action = [9933567]

AI ndash Sep2018 ndash Alisson Sol ndash [18]

Right method and correctly implemented

A Few Useful Things to KnowAbout Machine Learningby Pedro Domingos

AI ndash Sep2018 ndash Alisson Sol ndash [14]

Movies Sorted Distance

Movie of kicks of kisses Type d

18 90 Unknown 00

B 2 100 Romance 189

C 1 81 Romance 192

A 3 104 Romance 205

D 101 10 Action 1153

E 99 5 Action 1174

F 98 2 Action 1189

if kNN = 3NN thenhellip

AI ndash Sep2018 ndash Alisson Sol ndash [15]

What If (BI Scenarios)

AB

C

DEF

0

20

40

60

80

100

120

0 20 40 60 80 100 120

o

f ki

sse

s

of kicks

AI ndash Sep2018 ndash Alisson Sol ndash [16]

Pre-calculated ldquoborderrdquo

A

R

R

R

R

R

R

R

R

R

R

R

R

A

A

R

R

R

R

R

R

R

R

R

R

R

A

A

A

R

R

R

R

R

R

R

R

R

R

A

A

A

A

R

R

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A

A

A

A

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R

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A

A

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A

A

A

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A

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A

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A

A

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A

A

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A

A

A

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A

A

A

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A

A

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A

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A

A

A

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A

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A

A

A

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A

A

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A

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A

A

A

A

A

A

R

A

A

A

A

A

A

A

A

A

A

A

A

A

0

20

40

60

80

100

120

0 20 40 60 80 100 120

AI ndash Sep2018 ndash Alisson Sol ndash [17]

Cluster representation

AB

C

DEF

0

20

40

60

80

100

120

0 20 40 60 80 100 120

o

f ki

sse

s

of kicks

CentroidsRomance = [295]Action = [9933567]

AI ndash Sep2018 ndash Alisson Sol ndash [18]

Right method and correctly implemented

A Few Useful Things to KnowAbout Machine Learningby Pedro Domingos

AI ndash Sep2018 ndash Alisson Sol ndash [15]

What If (BI Scenarios)

AB

C

DEF

0

20

40

60

80

100

120

0 20 40 60 80 100 120

o

f ki

sse

s

of kicks

AI ndash Sep2018 ndash Alisson Sol ndash [16]

Pre-calculated ldquoborderrdquo

A

R

R

R

R

R

R

R

R

R

R

R

R

A

A

R

R

R

R

R

R

R

R

R

R

R

A

A

A

R

R

R

R

R

R

R

R

R

R

A

A

A

A

R

R

R

R

R

R

R

R

R

A

A

A

A

A

R

R

R

R

R

R

R

R

A

A

A

A

A

A

R

R

R

R

R

R

R

A

A

A

A

A

A

A

R

R

R

R

R

R

A

A

A

A

A

A

A

A

R

R

R

R

R

A

A

A

A

A

A

A

A

A

R

R

R

R

A

A

A

A

A

A

A

A

A

A

R

R

R

A

A

A

A

A

A

A

A

A

A

A

R

R

A

A

A

A

A

A

A

A

A

A

A

A

R

A

A

A

A

A

A

A

A

A

A

A

A

A

0

20

40

60

80

100

120

0 20 40 60 80 100 120

AI ndash Sep2018 ndash Alisson Sol ndash [17]

Cluster representation

AB

C

DEF

0

20

40

60

80

100

120

0 20 40 60 80 100 120

o

f ki

sse

s

of kicks

CentroidsRomance = [295]Action = [9933567]

AI ndash Sep2018 ndash Alisson Sol ndash [18]

Right method and correctly implemented

A Few Useful Things to KnowAbout Machine Learningby Pedro Domingos

AI ndash Sep2018 ndash Alisson Sol ndash [16]

Pre-calculated ldquoborderrdquo

A

R

R

R

R

R

R

R

R

R

R

R

R

A

A

R

R

R

R

R

R

R

R

R

R

R

A

A

A

R

R

R

R

R

R

R

R

R

R

A

A

A

A

R

R

R

R

R

R

R

R

R

A

A

A

A

A

R

R

R

R

R

R

R

R

A

A

A

A

A

A

R

R

R

R

R

R

R

A

A

A

A

A

A

A

R

R

R

R

R

R

A

A

A

A

A

A

A

A

R

R

R

R

R

A

A

A

A

A

A

A

A

A

R

R

R

R

A

A

A

A

A

A

A

A

A

A

R

R

R

A

A

A

A

A

A

A

A

A

A

A

R

R

A

A

A

A

A

A

A

A

A

A

A

A

R

A

A

A

A

A

A

A

A

A

A

A

A

A

0

20

40

60

80

100

120

0 20 40 60 80 100 120

AI ndash Sep2018 ndash Alisson Sol ndash [17]

Cluster representation

AB

C

DEF

0

20

40

60

80

100

120

0 20 40 60 80 100 120

o

f ki

sse

s

of kicks

CentroidsRomance = [295]Action = [9933567]

AI ndash Sep2018 ndash Alisson Sol ndash [18]

Right method and correctly implemented

A Few Useful Things to KnowAbout Machine Learningby Pedro Domingos

AI ndash Sep2018 ndash Alisson Sol ndash [17]

Cluster representation

AB

C

DEF

0

20

40

60

80

100

120

0 20 40 60 80 100 120

o

f ki

sse

s

of kicks

CentroidsRomance = [295]Action = [9933567]

AI ndash Sep2018 ndash Alisson Sol ndash [18]

Right method and correctly implemented

A Few Useful Things to KnowAbout Machine Learningby Pedro Domingos

AI ndash Sep2018 ndash Alisson Sol ndash [18]

Right method and correctly implemented

A Few Useful Things to KnowAbout Machine Learningby Pedro Domingos

AI ndash Sep2018 ndash Alisson Sol ndash [19]

Training is costly

AI ndash Sep2018 ndash Alisson Sol ndash [20]

Data analysis for an experiment

From Kinect for Windowspresentation duringMicrosoft Build 2013 event

AI ndash Sep2018 ndash Alisson Sol ndash [21]

What is this

AI ndash Sep2018 ndash Alisson Sol ndash [22]

What is Deep Learning

AI ndash Sep2018 ndash Alisson Sol ndash [23]

What is deep learning

bull Deep learning (also known as deep structured learning hierarchical learning or deep machine learning) is a class of machine learning algorithms that use a cascade of many layers of nonlinear processing units for feature extraction and transformation

bull But what is a Neural Network

bull Visualize httpplaygroundtensorfloworg

AI ndash Sep2018 ndash Alisson Sol ndash [20]

Data analysis for an experiment

From Kinect for Windowspresentation duringMicrosoft Build 2013 event

AI ndash Sep2018 ndash Alisson Sol ndash [21]

What is this

AI ndash Sep2018 ndash Alisson Sol ndash [22]

What is Deep Learning

AI ndash Sep2018 ndash Alisson Sol ndash [23]

What is deep learning

bull Deep learning (also known as deep structured learning hierarchical learning or deep machine learning) is a class of machine learning algorithms that use a cascade of many layers of nonlinear processing units for feature extraction and transformation

bull But what is a Neural Network

bull Visualize httpplaygroundtensorfloworg

AI ndash Sep2018 ndash Alisson Sol ndash [21]

What is this

AI ndash Sep2018 ndash Alisson Sol ndash [22]

What is Deep Learning

AI ndash Sep2018 ndash Alisson Sol ndash [23]

What is deep learning

bull Deep learning (also known as deep structured learning hierarchical learning or deep machine learning) is a class of machine learning algorithms that use a cascade of many layers of nonlinear processing units for feature extraction and transformation

bull But what is a Neural Network

bull Visualize httpplaygroundtensorfloworg

AI ndash Sep2018 ndash Alisson Sol ndash [22]

What is Deep Learning

AI ndash Sep2018 ndash Alisson Sol ndash [23]

What is deep learning

bull Deep learning (also known as deep structured learning hierarchical learning or deep machine learning) is a class of machine learning algorithms that use a cascade of many layers of nonlinear processing units for feature extraction and transformation

bull But what is a Neural Network

bull Visualize httpplaygroundtensorfloworg

AI ndash Sep2018 ndash Alisson Sol ndash [23]

What is deep learning

bull Deep learning (also known as deep structured learning hierarchical learning or deep machine learning) is a class of machine learning algorithms that use a cascade of many layers of nonlinear processing units for feature extraction and transformation

bull But what is a Neural Network

bull Visualize httpplaygroundtensorfloworg

AI ndash Sep2018 ndash Alisson Sol ndash [24]

Who is learning better

bull Two pilots have been learning how to complete a difficult mission in an airplane simulator Rates of success per week are in the table

Pilot Week 1 Week 2 Aggregated

Olivia 0 75

Oliver 25 100

AI ndash Sep2018 ndash Alisson Sol ndash [25]

Simpsons paradox

bull Trend in different groups of data disappears or reverses when these groups are combined (aka reversal or amalgamation paradox)

Pilot Week 1 Week 2 Aggregated

Olivia (01) 0 (34) 75 (35) 60

Oliver (14) 25 (11) 100 (25) 40

AI ndash Sep2018 ndash Alisson Sol ndash [26]

Accuracy paradox

Confusion matrix

PredictivePositive

PredictiveNegative

Positivesamples

True Positive

False Negative

Negativesamples

False Positive

True Negative

Accuracy

bull119860 =119879119875+119879119873

119879119875+119865119875+119879119873+119865119873

AI ndash Sep2018 ndash Alisson Sol ndash [27]

Model comparison by accuracy

Model A

PredictivePositive

PredictiveNegative

Positivesamples

100 50

Negativesamples

150 9700

Model B

PredictivePositive

PredictiveNegative

Positivesamples

1 149

Negativesamples

1 9849

119860(119860) =100 + 9700

100 + 150 + 9700 + 50= 098 119860(119861) =

1 + 9849

1 + 1 + 9849 + 149= 0985

AI ndash Sep2018 ndash Alisson Sol ndash [28]

F1 Score

Confusion matrix

PredictivePositive

PredictiveNegative

Positivesamples

True Positive

False Negative

Negativesamples

False Positive

True Negative

Measurements (Precision Recall F1)

bull119875 =119879119875

119879119875+119865119875

bull119877 =119879119875

119879119875+119865119873

bull1198651 = 2119875lowast119877

119875+119877

AI ndash Sep2018 ndash Alisson Sol ndash [29]

Model comparison by F1 Score

Model A

PredictivePositive

PredictiveNegative

Positivesamples

100 50

Negativesamples

150 9700

Model B

PredictivePositive

PredictiveNegative

Positivesamples

1 149

Negativesamples

1 9849

119875 = 04 119877 = 067 1198651 = 05 119875 = 05 119877 = 00067 1198651 = 0013

AI ndash Sep2018 ndash Alisson Sol ndash [30]

Takeaways Math and AI

bull Computers evolved enabling expanded use of Numerical Calculus

bull Someone in the team needs to understand the Math

bull Math may be right yet its interpretation may be incorrect

AI ndash Sep2018 ndash Alisson Sol ndash [31]

Any questions before we go to ldquoMemoryrdquo

bull Math

bull Memory

bull Models

AI ndash Sep2018 ndash Alisson Sol ndash [32]

这是什么

AI ndash Sep2018 ndash Alisson Sol ndash [33]

这是什么

AI ndash Sep2018 ndash Alisson Sol ndash [34]

这是什么

AI ndash Sep2018 ndash Alisson Sol ndash [35]

What just happened

AI ndash Sep2018 ndash Alisson Sol ndash [36]

Your mindhellip

bull Analyzed images

bull Split it into components

bull Associated such components with a verbal description

AI ndash Sep2018 ndash Alisson Sol ndash [37]

你有这段记忆吗

AI ndash Sep2018 ndash Alisson Sol ndash [38]

你有这段记忆吗

AI ndash Sep2018 ndash Alisson Sol ndash [39]

你有这段记忆吗

AI ndash Sep2018 ndash Alisson Sol ndash [40]

Playing with Image Search

bull httpsimagesgooglecom

bull httpswwwbingcomimages

AI ndash Sep2018 ndash Alisson Sol ndash [25]

Simpsons paradox

bull Trend in different groups of data disappears or reverses when these groups are combined (aka reversal or amalgamation paradox)

Pilot Week 1 Week 2 Aggregated

Olivia (01) 0 (34) 75 (35) 60

Oliver (14) 25 (11) 100 (25) 40

AI ndash Sep2018 ndash Alisson Sol ndash [26]

Accuracy paradox

Confusion matrix

PredictivePositive

PredictiveNegative

Positivesamples

True Positive

False Negative

Negativesamples

False Positive

True Negative

Accuracy

bull119860 =119879119875+119879119873

119879119875+119865119875+119879119873+119865119873

AI ndash Sep2018 ndash Alisson Sol ndash [27]

Model comparison by accuracy

Model A

PredictivePositive

PredictiveNegative

Positivesamples

100 50

Negativesamples

150 9700

Model B

PredictivePositive

PredictiveNegative

Positivesamples

1 149

Negativesamples

1 9849

119860(119860) =100 + 9700

100 + 150 + 9700 + 50= 098 119860(119861) =

1 + 9849

1 + 1 + 9849 + 149= 0985

AI ndash Sep2018 ndash Alisson Sol ndash [28]

F1 Score

Confusion matrix

PredictivePositive

PredictiveNegative

Positivesamples

True Positive

False Negative

Negativesamples

False Positive

True Negative

Measurements (Precision Recall F1)

bull119875 =119879119875

119879119875+119865119875

bull119877 =119879119875

119879119875+119865119873

bull1198651 = 2119875lowast119877

119875+119877

AI ndash Sep2018 ndash Alisson Sol ndash [29]

Model comparison by F1 Score

Model A

PredictivePositive

PredictiveNegative

Positivesamples

100 50

Negativesamples

150 9700

Model B

PredictivePositive

PredictiveNegative

Positivesamples

1 149

Negativesamples

1 9849

119875 = 04 119877 = 067 1198651 = 05 119875 = 05 119877 = 00067 1198651 = 0013

AI ndash Sep2018 ndash Alisson Sol ndash [30]

Takeaways Math and AI

bull Computers evolved enabling expanded use of Numerical Calculus

bull Someone in the team needs to understand the Math

bull Math may be right yet its interpretation may be incorrect

AI ndash Sep2018 ndash Alisson Sol ndash [31]

Any questions before we go to ldquoMemoryrdquo

bull Math

bull Memory

bull Models

AI ndash Sep2018 ndash Alisson Sol ndash [32]

这是什么

AI ndash Sep2018 ndash Alisson Sol ndash [33]

这是什么

AI ndash Sep2018 ndash Alisson Sol ndash [34]

这是什么

AI ndash Sep2018 ndash Alisson Sol ndash [35]

What just happened

AI ndash Sep2018 ndash Alisson Sol ndash [36]

Your mindhellip

bull Analyzed images

bull Split it into components

bull Associated such components with a verbal description

AI ndash Sep2018 ndash Alisson Sol ndash [37]

你有这段记忆吗

AI ndash Sep2018 ndash Alisson Sol ndash [38]

你有这段记忆吗

AI ndash Sep2018 ndash Alisson Sol ndash [39]

你有这段记忆吗

AI ndash Sep2018 ndash Alisson Sol ndash [40]

Playing with Image Search

bull httpsimagesgooglecom

bull httpswwwbingcomimages

AI ndash Sep2018 ndash Alisson Sol ndash [26]

Accuracy paradox

Confusion matrix

PredictivePositive

PredictiveNegative

Positivesamples

True Positive

False Negative

Negativesamples

False Positive

True Negative

Accuracy

bull119860 =119879119875+119879119873

119879119875+119865119875+119879119873+119865119873

AI ndash Sep2018 ndash Alisson Sol ndash [27]

Model comparison by accuracy

Model A

PredictivePositive

PredictiveNegative

Positivesamples

100 50

Negativesamples

150 9700

Model B

PredictivePositive

PredictiveNegative

Positivesamples

1 149

Negativesamples

1 9849

119860(119860) =100 + 9700

100 + 150 + 9700 + 50= 098 119860(119861) =

1 + 9849

1 + 1 + 9849 + 149= 0985

AI ndash Sep2018 ndash Alisson Sol ndash [28]

F1 Score

Confusion matrix

PredictivePositive

PredictiveNegative

Positivesamples

True Positive

False Negative

Negativesamples

False Positive

True Negative

Measurements (Precision Recall F1)

bull119875 =119879119875

119879119875+119865119875

bull119877 =119879119875

119879119875+119865119873

bull1198651 = 2119875lowast119877

119875+119877

AI ndash Sep2018 ndash Alisson Sol ndash [29]

Model comparison by F1 Score

Model A

PredictivePositive

PredictiveNegative

Positivesamples

100 50

Negativesamples

150 9700

Model B

PredictivePositive

PredictiveNegative

Positivesamples

1 149

Negativesamples

1 9849

119875 = 04 119877 = 067 1198651 = 05 119875 = 05 119877 = 00067 1198651 = 0013

AI ndash Sep2018 ndash Alisson Sol ndash [30]

Takeaways Math and AI

bull Computers evolved enabling expanded use of Numerical Calculus

bull Someone in the team needs to understand the Math

bull Math may be right yet its interpretation may be incorrect

AI ndash Sep2018 ndash Alisson Sol ndash [31]

Any questions before we go to ldquoMemoryrdquo

bull Math

bull Memory

bull Models

AI ndash Sep2018 ndash Alisson Sol ndash [32]

这是什么

AI ndash Sep2018 ndash Alisson Sol ndash [33]

这是什么

AI ndash Sep2018 ndash Alisson Sol ndash [34]

这是什么

AI ndash Sep2018 ndash Alisson Sol ndash [35]

What just happened

AI ndash Sep2018 ndash Alisson Sol ndash [36]

Your mindhellip

bull Analyzed images

bull Split it into components

bull Associated such components with a verbal description

AI ndash Sep2018 ndash Alisson Sol ndash [37]

你有这段记忆吗

AI ndash Sep2018 ndash Alisson Sol ndash [38]

你有这段记忆吗

AI ndash Sep2018 ndash Alisson Sol ndash [39]

你有这段记忆吗

AI ndash Sep2018 ndash Alisson Sol ndash [40]

Playing with Image Search

bull httpsimagesgooglecom

bull httpswwwbingcomimages

AI ndash Sep2018 ndash Alisson Sol ndash [27]

Model comparison by accuracy

Model A

PredictivePositive

PredictiveNegative

Positivesamples

100 50

Negativesamples

150 9700

Model B

PredictivePositive

PredictiveNegative

Positivesamples

1 149

Negativesamples

1 9849

119860(119860) =100 + 9700

100 + 150 + 9700 + 50= 098 119860(119861) =

1 + 9849

1 + 1 + 9849 + 149= 0985

AI ndash Sep2018 ndash Alisson Sol ndash [28]

F1 Score

Confusion matrix

PredictivePositive

PredictiveNegative

Positivesamples

True Positive

False Negative

Negativesamples

False Positive

True Negative

Measurements (Precision Recall F1)

bull119875 =119879119875

119879119875+119865119875

bull119877 =119879119875

119879119875+119865119873

bull1198651 = 2119875lowast119877

119875+119877

AI ndash Sep2018 ndash Alisson Sol ndash [29]

Model comparison by F1 Score

Model A

PredictivePositive

PredictiveNegative

Positivesamples

100 50

Negativesamples

150 9700

Model B

PredictivePositive

PredictiveNegative

Positivesamples

1 149

Negativesamples

1 9849

119875 = 04 119877 = 067 1198651 = 05 119875 = 05 119877 = 00067 1198651 = 0013

AI ndash Sep2018 ndash Alisson Sol ndash [30]

Takeaways Math and AI

bull Computers evolved enabling expanded use of Numerical Calculus

bull Someone in the team needs to understand the Math

bull Math may be right yet its interpretation may be incorrect

AI ndash Sep2018 ndash Alisson Sol ndash [31]

Any questions before we go to ldquoMemoryrdquo

bull Math

bull Memory

bull Models

AI ndash Sep2018 ndash Alisson Sol ndash [32]

这是什么

AI ndash Sep2018 ndash Alisson Sol ndash [33]

这是什么

AI ndash Sep2018 ndash Alisson Sol ndash [34]

这是什么

AI ndash Sep2018 ndash Alisson Sol ndash [35]

What just happened

AI ndash Sep2018 ndash Alisson Sol ndash [36]

Your mindhellip

bull Analyzed images

bull Split it into components

bull Associated such components with a verbal description

AI ndash Sep2018 ndash Alisson Sol ndash [37]

你有这段记忆吗

AI ndash Sep2018 ndash Alisson Sol ndash [38]

你有这段记忆吗

AI ndash Sep2018 ndash Alisson Sol ndash [39]

你有这段记忆吗

AI ndash Sep2018 ndash Alisson Sol ndash [40]

Playing with Image Search

bull httpsimagesgooglecom

bull httpswwwbingcomimages

AI ndash Sep2018 ndash Alisson Sol ndash [28]

F1 Score

Confusion matrix

PredictivePositive

PredictiveNegative

Positivesamples

True Positive

False Negative

Negativesamples

False Positive

True Negative

Measurements (Precision Recall F1)

bull119875 =119879119875

119879119875+119865119875

bull119877 =119879119875

119879119875+119865119873

bull1198651 = 2119875lowast119877

119875+119877

AI ndash Sep2018 ndash Alisson Sol ndash [29]

Model comparison by F1 Score

Model A

PredictivePositive

PredictiveNegative

Positivesamples

100 50

Negativesamples

150 9700

Model B

PredictivePositive

PredictiveNegative

Positivesamples

1 149

Negativesamples

1 9849

119875 = 04 119877 = 067 1198651 = 05 119875 = 05 119877 = 00067 1198651 = 0013

AI ndash Sep2018 ndash Alisson Sol ndash [30]

Takeaways Math and AI

bull Computers evolved enabling expanded use of Numerical Calculus

bull Someone in the team needs to understand the Math

bull Math may be right yet its interpretation may be incorrect

AI ndash Sep2018 ndash Alisson Sol ndash [31]

Any questions before we go to ldquoMemoryrdquo

bull Math

bull Memory

bull Models

AI ndash Sep2018 ndash Alisson Sol ndash [32]

这是什么

AI ndash Sep2018 ndash Alisson Sol ndash [33]

这是什么

AI ndash Sep2018 ndash Alisson Sol ndash [34]

这是什么

AI ndash Sep2018 ndash Alisson Sol ndash [35]

What just happened

AI ndash Sep2018 ndash Alisson Sol ndash [36]

Your mindhellip

bull Analyzed images

bull Split it into components

bull Associated such components with a verbal description

AI ndash Sep2018 ndash Alisson Sol ndash [37]

你有这段记忆吗

AI ndash Sep2018 ndash Alisson Sol ndash [38]

你有这段记忆吗

AI ndash Sep2018 ndash Alisson Sol ndash [39]

你有这段记忆吗

AI ndash Sep2018 ndash Alisson Sol ndash [40]

Playing with Image Search

bull httpsimagesgooglecom

bull httpswwwbingcomimages

AI ndash Sep2018 ndash Alisson Sol ndash [29]

Model comparison by F1 Score

Model A

PredictivePositive

PredictiveNegative

Positivesamples

100 50

Negativesamples

150 9700

Model B

PredictivePositive

PredictiveNegative

Positivesamples

1 149

Negativesamples

1 9849

119875 = 04 119877 = 067 1198651 = 05 119875 = 05 119877 = 00067 1198651 = 0013

AI ndash Sep2018 ndash Alisson Sol ndash [30]

Takeaways Math and AI

bull Computers evolved enabling expanded use of Numerical Calculus

bull Someone in the team needs to understand the Math

bull Math may be right yet its interpretation may be incorrect

AI ndash Sep2018 ndash Alisson Sol ndash [31]

Any questions before we go to ldquoMemoryrdquo

bull Math

bull Memory

bull Models

AI ndash Sep2018 ndash Alisson Sol ndash [32]

这是什么

AI ndash Sep2018 ndash Alisson Sol ndash [33]

这是什么

AI ndash Sep2018 ndash Alisson Sol ndash [34]

这是什么

AI ndash Sep2018 ndash Alisson Sol ndash [35]

What just happened

AI ndash Sep2018 ndash Alisson Sol ndash [36]

Your mindhellip

bull Analyzed images

bull Split it into components

bull Associated such components with a verbal description

AI ndash Sep2018 ndash Alisson Sol ndash [37]

你有这段记忆吗

AI ndash Sep2018 ndash Alisson Sol ndash [38]

你有这段记忆吗

AI ndash Sep2018 ndash Alisson Sol ndash [39]

你有这段记忆吗

AI ndash Sep2018 ndash Alisson Sol ndash [40]

Playing with Image Search

bull httpsimagesgooglecom

bull httpswwwbingcomimages

AI ndash Sep2018 ndash Alisson Sol ndash [30]

Takeaways Math and AI

bull Computers evolved enabling expanded use of Numerical Calculus

bull Someone in the team needs to understand the Math

bull Math may be right yet its interpretation may be incorrect

AI ndash Sep2018 ndash Alisson Sol ndash [31]

Any questions before we go to ldquoMemoryrdquo

bull Math

bull Memory

bull Models

AI ndash Sep2018 ndash Alisson Sol ndash [32]

这是什么

AI ndash Sep2018 ndash Alisson Sol ndash [33]

这是什么

AI ndash Sep2018 ndash Alisson Sol ndash [34]

这是什么

AI ndash Sep2018 ndash Alisson Sol ndash [35]

What just happened

AI ndash Sep2018 ndash Alisson Sol ndash [36]

Your mindhellip

bull Analyzed images

bull Split it into components

bull Associated such components with a verbal description

AI ndash Sep2018 ndash Alisson Sol ndash [37]

你有这段记忆吗

AI ndash Sep2018 ndash Alisson Sol ndash [38]

你有这段记忆吗

AI ndash Sep2018 ndash Alisson Sol ndash [39]

你有这段记忆吗

AI ndash Sep2018 ndash Alisson Sol ndash [40]

Playing with Image Search

bull httpsimagesgooglecom

bull httpswwwbingcomimages

AI ndash Sep2018 ndash Alisson Sol ndash [31]

Any questions before we go to ldquoMemoryrdquo

bull Math

bull Memory

bull Models

AI ndash Sep2018 ndash Alisson Sol ndash [32]

这是什么

AI ndash Sep2018 ndash Alisson Sol ndash [33]

这是什么

AI ndash Sep2018 ndash Alisson Sol ndash [34]

这是什么

AI ndash Sep2018 ndash Alisson Sol ndash [35]

What just happened

AI ndash Sep2018 ndash Alisson Sol ndash [36]

Your mindhellip

bull Analyzed images

bull Split it into components

bull Associated such components with a verbal description

AI ndash Sep2018 ndash Alisson Sol ndash [37]

你有这段记忆吗

AI ndash Sep2018 ndash Alisson Sol ndash [38]

你有这段记忆吗

AI ndash Sep2018 ndash Alisson Sol ndash [39]

你有这段记忆吗

AI ndash Sep2018 ndash Alisson Sol ndash [40]

Playing with Image Search

bull httpsimagesgooglecom

bull httpswwwbingcomimages

AI ndash Sep2018 ndash Alisson Sol ndash [32]

这是什么

AI ndash Sep2018 ndash Alisson Sol ndash [33]

这是什么

AI ndash Sep2018 ndash Alisson Sol ndash [34]

这是什么

AI ndash Sep2018 ndash Alisson Sol ndash [35]

What just happened

AI ndash Sep2018 ndash Alisson Sol ndash [36]

Your mindhellip

bull Analyzed images

bull Split it into components

bull Associated such components with a verbal description

AI ndash Sep2018 ndash Alisson Sol ndash [37]

你有这段记忆吗

AI ndash Sep2018 ndash Alisson Sol ndash [38]

你有这段记忆吗

AI ndash Sep2018 ndash Alisson Sol ndash [39]

你有这段记忆吗

AI ndash Sep2018 ndash Alisson Sol ndash [40]

Playing with Image Search

bull httpsimagesgooglecom

bull httpswwwbingcomimages

AI ndash Sep2018 ndash Alisson Sol ndash [33]

这是什么

AI ndash Sep2018 ndash Alisson Sol ndash [34]

这是什么

AI ndash Sep2018 ndash Alisson Sol ndash [35]

What just happened

AI ndash Sep2018 ndash Alisson Sol ndash [36]

Your mindhellip

bull Analyzed images

bull Split it into components

bull Associated such components with a verbal description

AI ndash Sep2018 ndash Alisson Sol ndash [37]

你有这段记忆吗

AI ndash Sep2018 ndash Alisson Sol ndash [38]

你有这段记忆吗

AI ndash Sep2018 ndash Alisson Sol ndash [39]

你有这段记忆吗

AI ndash Sep2018 ndash Alisson Sol ndash [40]

Playing with Image Search

bull httpsimagesgooglecom

bull httpswwwbingcomimages

AI ndash Sep2018 ndash Alisson Sol ndash [34]

这是什么

AI ndash Sep2018 ndash Alisson Sol ndash [35]

What just happened

AI ndash Sep2018 ndash Alisson Sol ndash [36]

Your mindhellip

bull Analyzed images

bull Split it into components

bull Associated such components with a verbal description

AI ndash Sep2018 ndash Alisson Sol ndash [37]

你有这段记忆吗

AI ndash Sep2018 ndash Alisson Sol ndash [38]

你有这段记忆吗

AI ndash Sep2018 ndash Alisson Sol ndash [39]

你有这段记忆吗

AI ndash Sep2018 ndash Alisson Sol ndash [40]

Playing with Image Search

bull httpsimagesgooglecom

bull httpswwwbingcomimages

AI ndash Sep2018 ndash Alisson Sol ndash [35]

What just happened

AI ndash Sep2018 ndash Alisson Sol ndash [36]

Your mindhellip

bull Analyzed images

bull Split it into components

bull Associated such components with a verbal description

AI ndash Sep2018 ndash Alisson Sol ndash [37]

你有这段记忆吗

AI ndash Sep2018 ndash Alisson Sol ndash [38]

你有这段记忆吗

AI ndash Sep2018 ndash Alisson Sol ndash [39]

你有这段记忆吗

AI ndash Sep2018 ndash Alisson Sol ndash [40]

Playing with Image Search

bull httpsimagesgooglecom

bull httpswwwbingcomimages

AI ndash Sep2018 ndash Alisson Sol ndash [36]

Your mindhellip

bull Analyzed images

bull Split it into components

bull Associated such components with a verbal description

AI ndash Sep2018 ndash Alisson Sol ndash [37]

你有这段记忆吗

AI ndash Sep2018 ndash Alisson Sol ndash [38]

你有这段记忆吗

AI ndash Sep2018 ndash Alisson Sol ndash [39]

你有这段记忆吗

AI ndash Sep2018 ndash Alisson Sol ndash [40]

Playing with Image Search

bull httpsimagesgooglecom

bull httpswwwbingcomimages

AI ndash Sep2018 ndash Alisson Sol ndash [37]

你有这段记忆吗

AI ndash Sep2018 ndash Alisson Sol ndash [38]

你有这段记忆吗

AI ndash Sep2018 ndash Alisson Sol ndash [39]

你有这段记忆吗

AI ndash Sep2018 ndash Alisson Sol ndash [40]

Playing with Image Search

bull httpsimagesgooglecom

bull httpswwwbingcomimages

AI ndash Sep2018 ndash Alisson Sol ndash [38]

你有这段记忆吗

AI ndash Sep2018 ndash Alisson Sol ndash [39]

你有这段记忆吗

AI ndash Sep2018 ndash Alisson Sol ndash [40]

Playing with Image Search

bull httpsimagesgooglecom

bull httpswwwbingcomimages

AI ndash Sep2018 ndash Alisson Sol ndash [39]

你有这段记忆吗

AI ndash Sep2018 ndash Alisson Sol ndash [40]

Playing with Image Search

bull httpsimagesgooglecom

bull httpswwwbingcomimages

AI ndash Sep2018 ndash Alisson Sol ndash [40]

Playing with Image Search

bull httpsimagesgooglecom

bull httpswwwbingcomimages

AI ndash Sep2018 ndash Alisson Sol ndash [41]

Your thoughts

bull How important are the features for the search

bull ldquoAn image is worth a thousand wordsrdquobull Would any 1000 words be equivalent

AI ndash Sep2018 ndash Alisson Sol ndash [42]

Movies another dataset

Movie Running Time (Minutes) Revenue ($ million) Type

A 88 407 Romance

B 129 1811 Romance

C 119 4634 Romance

D 116 129 Action

E 102 534 Action

F 108 332 Action

115 15 Unknown

AI ndash Sep2018 ndash Alisson Sol ndash [43]

Movies Scatter chart kicks and kisses

AB

C

DEF

0

20

40

60

80

100

120

0 20 40 60 80 100 120

o

f ki

sse

s

of kicks

AI ndash Sep2018 ndash Alisson Sol ndash [44]

Movies Scatter chart revenue and duration

A

B

C

DE

F

0

100

200

300

400

500

80 90 100 110 120 130 140

Rev

en

ue

($

mill

ion

)

Running Time (Minutes)

AI ndash Sep2018 ndash Alisson Sol ndash [45]

Orphaned MLAI projects

bull Pattern for failed ML projectbull Data was accumulated Volume Variety Velocity Veracity

bull Model was built with ldquopotentialrdquo success for initial anecdotes

bull Then different questions or need for more precise answers

bull Result project collapses

bull Corollary data is thrown away or lostbull httpstoolboxgooglecomdatasetsearch

AI ndash Sep2018 ndash Alisson Sol ndash [42]

Movies another dataset

Movie Running Time (Minutes) Revenue ($ million) Type

A 88 407 Romance

B 129 1811 Romance

C 119 4634 Romance

D 116 129 Action

E 102 534 Action

F 108 332 Action

115 15 Unknown

AI ndash Sep2018 ndash Alisson Sol ndash [43]

Movies Scatter chart kicks and kisses

AB

C

DEF

0

20

40

60

80

100

120

0 20 40 60 80 100 120

o

f ki

sse

s

of kicks

AI ndash Sep2018 ndash Alisson Sol ndash [44]

Movies Scatter chart revenue and duration

A

B

C

DE

F

0

100

200

300

400

500

80 90 100 110 120 130 140

Rev

en

ue

($

mill

ion

)

Running Time (Minutes)

AI ndash Sep2018 ndash Alisson Sol ndash [45]

Orphaned MLAI projects

bull Pattern for failed ML projectbull Data was accumulated Volume Variety Velocity Veracity

bull Model was built with ldquopotentialrdquo success for initial anecdotes

bull Then different questions or need for more precise answers

bull Result project collapses

bull Corollary data is thrown away or lostbull httpstoolboxgooglecomdatasetsearch

AI ndash Sep2018 ndash Alisson Sol ndash [43]

Movies Scatter chart kicks and kisses

AB

C

DEF

0

20

40

60

80

100

120

0 20 40 60 80 100 120

o

f ki

sse

s

of kicks

AI ndash Sep2018 ndash Alisson Sol ndash [44]

Movies Scatter chart revenue and duration

A

B

C

DE

F

0

100

200

300

400

500

80 90 100 110 120 130 140

Rev

en

ue

($

mill

ion

)

Running Time (Minutes)

AI ndash Sep2018 ndash Alisson Sol ndash [45]

Orphaned MLAI projects

bull Pattern for failed ML projectbull Data was accumulated Volume Variety Velocity Veracity

bull Model was built with ldquopotentialrdquo success for initial anecdotes

bull Then different questions or need for more precise answers

bull Result project collapses

bull Corollary data is thrown away or lostbull httpstoolboxgooglecomdatasetsearch

AI ndash Sep2018 ndash Alisson Sol ndash [44]

Movies Scatter chart revenue and duration

A

B

C

DE

F

0

100

200

300

400

500

80 90 100 110 120 130 140

Rev

en

ue

($

mill

ion

)

Running Time (Minutes)

AI ndash Sep2018 ndash Alisson Sol ndash [45]

Orphaned MLAI projects

bull Pattern for failed ML projectbull Data was accumulated Volume Variety Velocity Veracity

bull Model was built with ldquopotentialrdquo success for initial anecdotes

bull Then different questions or need for more precise answers

bull Result project collapses

bull Corollary data is thrown away or lostbull httpstoolboxgooglecomdatasetsearch

AI ndash Sep2018 ndash Alisson Sol ndash [45]

Orphaned MLAI projects

bull Pattern for failed ML projectbull Data was accumulated Volume Variety Velocity Veracity

bull Model was built with ldquopotentialrdquo success for initial anecdotes

bull Then different questions or need for more precise answers

bull Result project collapses

bull Corollary data is thrown away or lostbull httpstoolboxgooglecomdatasetsearch

AI ndash Sep2018 ndash Alisson Sol ndash [46]

AI ndash Sep2018 ndash Alisson Sol ndash [47]

Personal data collection half-marathons

00000

03000

10000

13000

20000

23000

30000

0 2 4 6 8 10 12 14

Seattle2015

Portland2015

SF2014

LakeSam2016

SFRnR2016

CDA2016

SEARnR2016

Portland2016

LARnR2016

LakeWA2016

AZRnR2017

LakeSam2017

Mercer2017

SEARnR2017

AI ndash Sep2018 ndash Alisson Sol ndash [48]

Takeaways Memory and AI

bull Human memory is still poorly understood

bull Try the reverse of current trendbull Start with the questions

bull Build a model (proof of concept)

bull Then seek for the data

bull Iteratehellipbull Everything questions model data

AI ndash Sep2018 ndash Alisson Sol ndash [49]

Any questions before we go to ldquoModelsrdquo

bull Math

bull Memory

bull Models

AI ndash Sep2018 ndash Alisson Sol ndash [50]

Humans and modeling our intuition is bad

bull Intelligence ldquohellip can be more generally described as the ability to perceive or infer information and to retain it as knowledge to be applied towards adaptive behaviors within an environment or contextrdquo

bull Earth = center of universe flat

bull Birds fly have feathers hellip from Icarus to Leonardo da Vinci

bull Humans donrsquot understand the ldquofeaturesrdquobull Sports stock market relationships hellip

bull Models include abstractions ldquogravityrdquo

hellip perceiveor inferhellip

AI ndash Sep2018 ndash Alisson Sol ndash [51]

Cognitive biases

httpsenwikipediaorgwikiList_of_cognitive_biases

hellip perceiveor inferhellip

AI ndash Sep2018 ndash Alisson Sol ndash [52]

Biases and modeling

bull Decision-making belief behaviorbull DunningndashKruger effect

bull Irrational escalation (sunk cost)

bull Parkinsons law of triviality (bikeshedding)

bull Social biasesbull Illusory superiority (Lake Wobegon effect)

bull Fundamental attribution error

bull Memory errors and biasesbull Bizarreness effect

bull Hindsight bias

hellip perceiveor inferhellip

AI ndash Sep2018 ndash Alisson Sol ndash [53]

How is Physics applied

bull Acceptance of multiple modelsbull No wrong or right model the best predictor wins the day

bull Acceptance of imprecise answersbull Simplifies models eliminating a lot of noise

bull 119865 = 119898119886 and 119864 = 1198981198882 only for ideal conditions

bull Detaching models from databull Nobel Prize in Physics 2017 Rainer Weiss Barry C Barish Kip S Thorne

LIGO Laser Interferometer Gravitational-Wave Observatoryconfirmed predictions by Albert Einstein 100 years ago

hellip perceiveor inferhellip

AI ndash Sep2018 ndash Alisson Sol ndash [48]

Takeaways Memory and AI

bull Human memory is still poorly understood

bull Try the reverse of current trendbull Start with the questions

bull Build a model (proof of concept)

bull Then seek for the data

bull Iteratehellipbull Everything questions model data

AI ndash Sep2018 ndash Alisson Sol ndash [49]

Any questions before we go to ldquoModelsrdquo

bull Math

bull Memory

bull Models

AI ndash Sep2018 ndash Alisson Sol ndash [50]

Humans and modeling our intuition is bad

bull Intelligence ldquohellip can be more generally described as the ability to perceive or infer information and to retain it as knowledge to be applied towards adaptive behaviors within an environment or contextrdquo

bull Earth = center of universe flat

bull Birds fly have feathers hellip from Icarus to Leonardo da Vinci

bull Humans donrsquot understand the ldquofeaturesrdquobull Sports stock market relationships hellip

bull Models include abstractions ldquogravityrdquo

hellip perceiveor inferhellip

AI ndash Sep2018 ndash Alisson Sol ndash [51]

Cognitive biases

httpsenwikipediaorgwikiList_of_cognitive_biases

hellip perceiveor inferhellip

AI ndash Sep2018 ndash Alisson Sol ndash [52]

Biases and modeling

bull Decision-making belief behaviorbull DunningndashKruger effect

bull Irrational escalation (sunk cost)

bull Parkinsons law of triviality (bikeshedding)

bull Social biasesbull Illusory superiority (Lake Wobegon effect)

bull Fundamental attribution error

bull Memory errors and biasesbull Bizarreness effect

bull Hindsight bias

hellip perceiveor inferhellip

AI ndash Sep2018 ndash Alisson Sol ndash [53]

How is Physics applied

bull Acceptance of multiple modelsbull No wrong or right model the best predictor wins the day

bull Acceptance of imprecise answersbull Simplifies models eliminating a lot of noise

bull 119865 = 119898119886 and 119864 = 1198981198882 only for ideal conditions

bull Detaching models from databull Nobel Prize in Physics 2017 Rainer Weiss Barry C Barish Kip S Thorne

LIGO Laser Interferometer Gravitational-Wave Observatoryconfirmed predictions by Albert Einstein 100 years ago

hellip perceiveor inferhellip

AI ndash Sep2018 ndash Alisson Sol ndash [49]

Any questions before we go to ldquoModelsrdquo

bull Math

bull Memory

bull Models

AI ndash Sep2018 ndash Alisson Sol ndash [50]

Humans and modeling our intuition is bad

bull Intelligence ldquohellip can be more generally described as the ability to perceive or infer information and to retain it as knowledge to be applied towards adaptive behaviors within an environment or contextrdquo

bull Earth = center of universe flat

bull Birds fly have feathers hellip from Icarus to Leonardo da Vinci

bull Humans donrsquot understand the ldquofeaturesrdquobull Sports stock market relationships hellip

bull Models include abstractions ldquogravityrdquo

hellip perceiveor inferhellip

AI ndash Sep2018 ndash Alisson Sol ndash [51]

Cognitive biases

httpsenwikipediaorgwikiList_of_cognitive_biases

hellip perceiveor inferhellip

AI ndash Sep2018 ndash Alisson Sol ndash [52]

Biases and modeling

bull Decision-making belief behaviorbull DunningndashKruger effect

bull Irrational escalation (sunk cost)

bull Parkinsons law of triviality (bikeshedding)

bull Social biasesbull Illusory superiority (Lake Wobegon effect)

bull Fundamental attribution error

bull Memory errors and biasesbull Bizarreness effect

bull Hindsight bias

hellip perceiveor inferhellip

AI ndash Sep2018 ndash Alisson Sol ndash [53]

How is Physics applied

bull Acceptance of multiple modelsbull No wrong or right model the best predictor wins the day

bull Acceptance of imprecise answersbull Simplifies models eliminating a lot of noise

bull 119865 = 119898119886 and 119864 = 1198981198882 only for ideal conditions

bull Detaching models from databull Nobel Prize in Physics 2017 Rainer Weiss Barry C Barish Kip S Thorne

LIGO Laser Interferometer Gravitational-Wave Observatoryconfirmed predictions by Albert Einstein 100 years ago

hellip perceiveor inferhellip

AI ndash Sep2018 ndash Alisson Sol ndash [50]

Humans and modeling our intuition is bad

bull Intelligence ldquohellip can be more generally described as the ability to perceive or infer information and to retain it as knowledge to be applied towards adaptive behaviors within an environment or contextrdquo

bull Earth = center of universe flat

bull Birds fly have feathers hellip from Icarus to Leonardo da Vinci

bull Humans donrsquot understand the ldquofeaturesrdquobull Sports stock market relationships hellip

bull Models include abstractions ldquogravityrdquo

hellip perceiveor inferhellip

AI ndash Sep2018 ndash Alisson Sol ndash [51]

Cognitive biases

httpsenwikipediaorgwikiList_of_cognitive_biases

hellip perceiveor inferhellip

AI ndash Sep2018 ndash Alisson Sol ndash [52]

Biases and modeling

bull Decision-making belief behaviorbull DunningndashKruger effect

bull Irrational escalation (sunk cost)

bull Parkinsons law of triviality (bikeshedding)

bull Social biasesbull Illusory superiority (Lake Wobegon effect)

bull Fundamental attribution error

bull Memory errors and biasesbull Bizarreness effect

bull Hindsight bias

hellip perceiveor inferhellip

AI ndash Sep2018 ndash Alisson Sol ndash [53]

How is Physics applied

bull Acceptance of multiple modelsbull No wrong or right model the best predictor wins the day

bull Acceptance of imprecise answersbull Simplifies models eliminating a lot of noise

bull 119865 = 119898119886 and 119864 = 1198981198882 only for ideal conditions

bull Detaching models from databull Nobel Prize in Physics 2017 Rainer Weiss Barry C Barish Kip S Thorne

LIGO Laser Interferometer Gravitational-Wave Observatoryconfirmed predictions by Albert Einstein 100 years ago

hellip perceiveor inferhellip

AI ndash Sep2018 ndash Alisson Sol ndash [51]

Cognitive biases

httpsenwikipediaorgwikiList_of_cognitive_biases

hellip perceiveor inferhellip

AI ndash Sep2018 ndash Alisson Sol ndash [52]

Biases and modeling

bull Decision-making belief behaviorbull DunningndashKruger effect

bull Irrational escalation (sunk cost)

bull Parkinsons law of triviality (bikeshedding)

bull Social biasesbull Illusory superiority (Lake Wobegon effect)

bull Fundamental attribution error

bull Memory errors and biasesbull Bizarreness effect

bull Hindsight bias

hellip perceiveor inferhellip

AI ndash Sep2018 ndash Alisson Sol ndash [53]

How is Physics applied

bull Acceptance of multiple modelsbull No wrong or right model the best predictor wins the day

bull Acceptance of imprecise answersbull Simplifies models eliminating a lot of noise

bull 119865 = 119898119886 and 119864 = 1198981198882 only for ideal conditions

bull Detaching models from databull Nobel Prize in Physics 2017 Rainer Weiss Barry C Barish Kip S Thorne

LIGO Laser Interferometer Gravitational-Wave Observatoryconfirmed predictions by Albert Einstein 100 years ago

hellip perceiveor inferhellip

AI ndash Sep2018 ndash Alisson Sol ndash [52]

Biases and modeling

bull Decision-making belief behaviorbull DunningndashKruger effect

bull Irrational escalation (sunk cost)

bull Parkinsons law of triviality (bikeshedding)

bull Social biasesbull Illusory superiority (Lake Wobegon effect)

bull Fundamental attribution error

bull Memory errors and biasesbull Bizarreness effect

bull Hindsight bias

hellip perceiveor inferhellip

AI ndash Sep2018 ndash Alisson Sol ndash [53]

How is Physics applied

bull Acceptance of multiple modelsbull No wrong or right model the best predictor wins the day

bull Acceptance of imprecise answersbull Simplifies models eliminating a lot of noise

bull 119865 = 119898119886 and 119864 = 1198981198882 only for ideal conditions

bull Detaching models from databull Nobel Prize in Physics 2017 Rainer Weiss Barry C Barish Kip S Thorne

LIGO Laser Interferometer Gravitational-Wave Observatoryconfirmed predictions by Albert Einstein 100 years ago

hellip perceiveor inferhellip

AI ndash Sep2018 ndash Alisson Sol ndash [53]

How is Physics applied

bull Acceptance of multiple modelsbull No wrong or right model the best predictor wins the day

bull Acceptance of imprecise answersbull Simplifies models eliminating a lot of noise

bull 119865 = 119898119886 and 119864 = 1198981198882 only for ideal conditions

bull Detaching models from databull Nobel Prize in Physics 2017 Rainer Weiss Barry C Barish Kip S Thorne

LIGO Laser Interferometer Gravitational-Wave Observatoryconfirmed predictions by Albert Einstein 100 years ago

hellip perceiveor inferhellip

AI ndash Sep2018 ndash Alisson Sol ndash [54]

Applying AI

bull Do you need to solve the general problem

bull Going from applied ML to applied AI

hellip perceiveor inferhellip

AI ndash Sep2018 ndash Alisson Sol ndash [55]

Airplane autopilot

bull Shiprsquos gyroscopic-compass set 1242065 Elmer A Sperry

bull Automatic pilot for airplanes1707690A Lawrence B Sperrybull Control of position course or

altitude of land water air or space vehicles eg automatic pilot with safety arrangements for transition from automatic pilot to manual pilot and vice versa

hellip perceiveor inferhellip

AI ndash Sep2018 ndash Alisson Sol ndash [55]

Airplane autopilot

bull Shiprsquos gyroscopic-compass set 1242065 Elmer A Sperry

bull Automatic pilot for airplanes1707690A Lawrence B Sperrybull Control of position course or

altitude of land water air or space vehicles eg automatic pilot with safety arrangements for transition from automatic pilot to manual pilot and vice versa

hellip perceiveor inferhellip

AI ndash Sep2018 ndash Alisson Sol ndash [56]

Applied Machine Learning

bull Get a goal

bull Make a goal-related model

bull Collect data

bull Test hypothesis

bull Apply prediction

bull Maximize flight occupation

bull Model for no-shows

bull Gather ticket info

bull Check predictions for no-shows

bull Actions based on predictionsbull Reminderspenalty for no-show

bull Allow more overbooking

hellip perceiveor inferhellip

AI ndash Sep2018 ndash Alisson Sol ndash [57]

hellip perceiveor inferhellip

AI ndash Sep2018 ndash Alisson Sol ndash [58]

End-to-end trip workflow

1 Luggage + documentation

2 Passenger gets to airport

3 Security checkpoints

4 Boarding

5 Flight + service

6 Leaving plane + connections

7 Luggage + customs + immigration

8 Airport to destination

9 Unpackhellip

1 Ouibring + documentation

2 Uber

3 Clear TSA PreCheck

4 Waiting room + priority check-in

5 Classes of service

6 Class of service + connections

7 Priority luggage + entry systems

8 Uber

9 Unpackhellip

hellip perceiveor inferhellip

AI ndash Sep2018 ndash Alisson Sol ndash [57]

hellip perceiveor inferhellip

AI ndash Sep2018 ndash Alisson Sol ndash [58]

End-to-end trip workflow

1 Luggage + documentation

2 Passenger gets to airport

3 Security checkpoints

4 Boarding

5 Flight + service

6 Leaving plane + connections

7 Luggage + customs + immigration

8 Airport to destination

9 Unpackhellip

1 Ouibring + documentation

2 Uber

3 Clear TSA PreCheck

4 Waiting room + priority check-in

5 Classes of service

6 Class of service + connections

7 Priority luggage + entry systems

8 Uber

9 Unpackhellip

hellip perceiveor inferhellip

AI ndash Sep2018 ndash Alisson Sol ndash [58]

End-to-end trip workflow

1 Luggage + documentation

2 Passenger gets to airport

3 Security checkpoints

4 Boarding

5 Flight + service

6 Leaving plane + connections

7 Luggage + customs + immigration

8 Airport to destination

9 Unpackhellip

1 Ouibring + documentation

2 Uber

3 Clear TSA PreCheck

4 Waiting room + priority check-in

5 Classes of service

6 Class of service + connections

7 Priority luggage + entry systems

8 Uber

9 Unpackhellip

hellip perceiveor inferhellip

AI ndash Sep2018 ndash Alisson Sol ndash [59]

Workflows and ldquointelligencerdquo

bull Intelligence will come from optimizing the end-to-end workflow

bull You wanted to play musicbull Before buy tapevinylCD put media on device locate song press play

bull Then the media disappeared song went directly to your iPod

bull Then full process was streamlined ldquoAlexa play [song]rdquo

bull Conversational user interfacebull Trying on assistant Google httpsassistantgooglecomexplorehl=en_us

hellip perceiveor inferhellip

AI ndash Sep2018 ndash Alisson Sol ndash [60]

What if airlines couldhellip

bull Predict flights by a class of passengers (holidays Super Bowl)

bull Offer full package flight accommodation etc

bull Alert that a passport is about to expire ahead of trip

bull Send transportation to customer houses

bull hellip

bull Summary anticipate and optimize complete job end-to-end

hellip perceiveor inferhellip

AI ndash Sep2018 ndash Alisson Sol ndash [61]

Models and data

bull Which modelsdata can be sharedbull Models about passenger behavior

bull Models about airline operations (Crew equipment supply chain)

bull Models about services

bull Should models be ldquoregulatedrdquobull EU PNR (Passenger Name Record)

bull What about auditing

hellip perceiveor inferhellip

AI ndash Sep2018 ndash Alisson Sol ndash [62]

AI and Ethics

bull Why is this a topic at all

bull Security and privacy

bull Will the intelligence change the worldmodel

bull How could intelligence maximize revenue for a hospital

bull Overbooking scenario which passenger would have to wait

hellip perceiveor inferhellip

AI ndash Sep2018 ndash Alisson Sol ndash [63]

Takeaways Models and AI

bull Requirementsbull Machine scalability

bull Organizational expertise

bull Data

bull Experiments

bull Libraries (software)

bull ldquoThink big and long termrdquo

hellip perceiveor inferhellip

AI ndash Sep2018 ndash Alisson Sol ndash [64]

QampA

bull Math

bull Memory

bull Models

bull Alisson SolemailAlissonSolcom

bull What was the next number in the sequence11 21 1211 111221

AI ndash Sep2018 ndash Alisson Sol ndash [61]

Models and data

bull Which modelsdata can be sharedbull Models about passenger behavior

bull Models about airline operations (Crew equipment supply chain)

bull Models about services

bull Should models be ldquoregulatedrdquobull EU PNR (Passenger Name Record)

bull What about auditing

hellip perceiveor inferhellip

AI ndash Sep2018 ndash Alisson Sol ndash [62]

AI and Ethics

bull Why is this a topic at all

bull Security and privacy

bull Will the intelligence change the worldmodel

bull How could intelligence maximize revenue for a hospital

bull Overbooking scenario which passenger would have to wait

hellip perceiveor inferhellip

AI ndash Sep2018 ndash Alisson Sol ndash [63]

Takeaways Models and AI

bull Requirementsbull Machine scalability

bull Organizational expertise

bull Data

bull Experiments

bull Libraries (software)

bull ldquoThink big and long termrdquo

hellip perceiveor inferhellip

AI ndash Sep2018 ndash Alisson Sol ndash [64]

QampA

bull Math

bull Memory

bull Models

bull Alisson SolemailAlissonSolcom

bull What was the next number in the sequence11 21 1211 111221

AI ndash Sep2018 ndash Alisson Sol ndash [62]

AI and Ethics

bull Why is this a topic at all

bull Security and privacy

bull Will the intelligence change the worldmodel

bull How could intelligence maximize revenue for a hospital

bull Overbooking scenario which passenger would have to wait

hellip perceiveor inferhellip

AI ndash Sep2018 ndash Alisson Sol ndash [63]

Takeaways Models and AI

bull Requirementsbull Machine scalability

bull Organizational expertise

bull Data

bull Experiments

bull Libraries (software)

bull ldquoThink big and long termrdquo

hellip perceiveor inferhellip

AI ndash Sep2018 ndash Alisson Sol ndash [64]

QampA

bull Math

bull Memory

bull Models

bull Alisson SolemailAlissonSolcom

bull What was the next number in the sequence11 21 1211 111221

AI ndash Sep2018 ndash Alisson Sol ndash [63]

Takeaways Models and AI

bull Requirementsbull Machine scalability

bull Organizational expertise

bull Data

bull Experiments

bull Libraries (software)

bull ldquoThink big and long termrdquo

hellip perceiveor inferhellip

AI ndash Sep2018 ndash Alisson Sol ndash [64]

QampA

bull Math

bull Memory

bull Models

bull Alisson SolemailAlissonSolcom

bull What was the next number in the sequence11 21 1211 111221

AI ndash Sep2018 ndash Alisson Sol ndash [64]

QampA

bull Math

bull Memory

bull Models

bull Alisson SolemailAlissonSolcom

bull What was the next number in the sequence11 21 1211 111221