1.2 Expert Systems

How rules-based systems can be used for binary classification

Human decision making

How do humans make decisions?

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Dengue

headache

cold cough

vomiting

fever

skin rash

Human decision making

How do humans make decisions from past experiences?

(c) One Fourth Labs

fever

 

headache

cold cough

vomiting

Rash

Inputs

Output

Human decision making

How do humans make decisions from past experiences?

(c) One Fourth Labs

fever

 

headache

cold cough

vomiting

Rash

inputs 

output 

1 0 0 0 0
1 0 0 1 0
1 1 1 0 1
1 0 1 1 0
0
0
1
0

0

1

Human decision making

Is this applicable in multiple domains ?

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LBW out

Impact

Height

no ball

shot attempted

Pitching in line

Inputs

Output

Human decision making

Is this applicable in multiple domains ?

(c) One Fourth Labs

LBW out

Impact

Height

no ball

shot attempted

Pitching in line

Inputs

Output

1 1 0 1 0
1 1 1 0 0
1 1 1 0 1
1 0 1 1 0
0
1
1
0

0

1

Features and Rules

What is the semantics of decision making?

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Output

Inputs

headache

cold cough

vomiting

fever

skin rash

Fever

Rash

Cold

(c) One Fourth Labs

if HIGH_FEVER and COLD:
    return no_dengue
elif HIGH_FEVER and VOMITING and not RASH:
    return no_dengue
elif HIGH_FEVER and VOMITING and RASH:
    return dengue

Output

Inputs

Fever

Rash

Cold

Expert Systems

How do we outsource this to a machine?

headache

cold cough

vomiting

fever

skin rash

Use-cases

Where have expert systems been used - past and recent applications

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(1990)

(1990)

(1965)

(1995)

(2018)

Hanane et. al., Expert Systems, May 2018.

Limitations

Do we need to look beyond expert systems?

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Task of  hiring someone for a job

10th marks

12th marks

  graduate

post graduate

   projects

if 10th>90 and 12>90 and GRADUATE:
    return hired
elif 10th>80 and 12th>80 and POST_GRADUATE:
    return hired
elif 10th<70 and 12th<70:
    return not_hired

10th

12th

graduate

Output

YES / NO

Inputs

Limitations

Do we need to look beyond expert systems?

(c) One Fourth Labs

Task of  hiring someone for a job - involving many factors

10th marks

if 10th>90 and 12>90 and GRADUATE:
    return hired
elif 10th>80 and 12th>80 and POST_GRADUATE:
    return hired
elif 10th<70 and 12th<70:
    return not_hired

10th

12th

graduate

12th marks

  graduate

post graduate

  projects

   awards

.....

10th marks 12th marks graduate post graduate projects ........... awards
92 82 yes yes 3 ........... 2
83 75.2 yes no 1 ........... 0
75 70 no no 0 ........... 1
96 95 yes yes 5 ........... 4
90 89 yes yes 2 ........... 1
78 82 yes no 0 ........... 0
86 88 yes no 1 ........... 1

 Lots of data to make sense from

Output

YES / NO

Limitations

Do we need to look beyond expert systems?

10th marks 12th marks graduate post graduate projects ........... awards
92 82 yes yes 3 ........... 2
83 75.2 yes no 1 ........... 0
75 70 no no 0 ........... 1
96 95 yes yes 5 ........... 4
90 89 yes yes 2 ........... 1
78 82 yes no 0 ........... 0
86 88 yes no 1 ........... 1 
if 10th>90 and 12>90 and GRADUATE:
    return hired
elif 10th>80 and 12th>80 and POST_GRADUATE:
    return hired
elif 10th<70 and 12th<70:
    return not_hired
elif awards>2 and GRADUATE:
    return hired
elif not POST_GRADUATE and publications>1:
    return hired
elif projects>10 and publications>2:
    return hired
elif competitions>3 and publications>3:
    return hired
elif awards>5:
    return hired
elif GRADUATE and not POST_GRADUATE and awards>5:
    return hired

The rules can be very complex

(c) One Fourth Labs

Limitations

Do we need to look beyond expert systems?

The rules can sometimes be unknown:

- task of predicting Ebola

Output

high  fever

  headache

sore throat

   weakness

    nausea

?

?

YES / NO

Inputs

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Limitations

Do we need to look beyond expert systems?

(c) One Fourth Labs

The rules can be sometimes inexpressible

10th marks

12th marks

  graduate

post graduate

   projects

Output

YES / NO

Inputs

?

I saw truth in his eyes

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Say Hi to Machine Learning

How to move from writing rules to learning rules?

10th marks 12th marks graduate post graduate projects ........... awards HIRED
92 82 yes yes 3 ........... 2 yes
83 75.2 yes no 1 ........... 0 yes
75 70 no no 0 ........... 1 no
96 95 yes yes 5 ........... 4 yes
90 89 yes yes 2 ........... 1 yes
78 82 yes no 0 ........... 0 no
86 88 yes no 1 ........... 1 no 
def f(x):
    if 10th>90 and 12>90 and GRADUATE:
        return hired
    elif 10th>80 and 12th>80 and POST_GRADUATE:
        return hired
    elif 10th<70 and 12th<70:
        return not_hired

10th

12th

graduate

Output

YES / NO

def f(x):
    i=0
    max_epochs=100
    w = rand()
    while(i<max_epochs):
        dw = grad(w,w*x)
        w = w - lr*dx
        i += 1
    return w*x

Data, Democratization, Devices

Why has Machine Learning been so successful ?

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In most applications we have limited intuition but infinite data

 

ML engines have been standardised and democratised 

 

Show many gears and then say it is about matching a gear to your data ?

Different roles in the ML world

How this relates to you ?

(c) One Fourth Labs

 

Show the gears and data on LHS

 

Show a pyramid: 

- Most problems about curating data

- Some problems about optimizing existing algorithms

- Research on new algorithms

Takeaway

How do we contrast expert systems from machine learning?

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Expert systems are useful when we can specify rules - some use cases still exist

 

But primarily focus is on machine learning, where rules are discovered by learning from data

 

// show a collage of respective applications

// very few icons for expert systems

// many many icons for ML

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