Tuesday, 27 June 2017

Recruitment Expert System : Design Concepts



As far as Recruitment ( Online or Offline ) is concerned , there has been a lot of technological progress in the past 15 years



But , we are nowhere near building of an EXPERT SYSTEM , which takes over from human recruiters, tasks that can be better / faster done by a SOFTWARE , leaving humans free to do what software cannot


What are these tasks ?


In August 2002 , I read a book :

EXPERT SYSTEMS : Principles and Case Studies
( Edited by :  Richard Forsyth )



My following handwritten remarks in the margin of this book , might provide an insight into the components of such a system


On my 84th birthday , I dedicate these remarks to my co-professionals in the Recruitment Industry , from whom I continue to learn



Hemen Parekh


27 June 2017



Mumbai

Page    2
·         I read his “ Human Use of Human Beings “ , around 1957 / 58 . Weiner was called “ Father of Cybernetics “ – in fact , he coined the word “ Cybernetics


Page   6
·         Is this like our saying : “ IF such and such Keywords appear in a resume, THEN , it may belong to such and such INDUSTRY or FUNCTION ?


Page   7
·         I believe the “ KNOWLEDGE “ contained in our 65,000 resumes , is good enough to develop an Expert System ( ARDIS – ARGIS ) . We started work on ARDIS – ARGIS in 1996 ! But taken up seriously , only 3 months back !


Page   8
·         Keywords are nothing but “ descriptions “ of Resumes


Page   11
·         I believe ISYS manual speaks of “ Context Tree “ – so does Oracle Context Cartridge ( Themes )


Page   15
·         “ Hypothesized Outcome “  >  In our case , Hypothesized Outcome could be , a Resume ,

#  getting shortlisted by 3P

#  getting shortlisted by Client ,  OR

#  a candidate getting “ appointed “ ( after interview )



·         “ Presence of the Evidence “  >  In our case , the “ presence of the evidence “ could be , presence of certain “ Keywords “ in a given resume ( the horse ) OR , certain “ Edu Quali “ OR , certain “ Age “ , OR certain “ Exp ( years ) “  OR certain “ Current Employer “ etc


Page   16
·         In our case , these several “ pieces of evidence “ could be ,

#  Keywords

#  Age

#  Exp

#  Edu Quali

#  Current Industry Background

#  Current Function Background

#  Current Designation Level

#  Current Salary

#  Current  Employer  etc


We could “ establish “ ODDS ( for each piece of evidence ) and then apply SEQUENTIALLY to figure out the “ Probability / Odds “ of that particular resume getting “ Shortlisted / getting Selected “


We have to examine ( Statistically ) , resumes of ALL candidates shortlisted during last 13 years – to calculate the ODDS



Page  18
·         “ Automating “ the process of Knowledge Acquisition ? We could do this ( automating ) by getting / inducing the Jobseekers to select / fill in , Keywords themselves online , in the web form


Page    19
·         I suppose this has become a reality during the last 13 years since writing of this book

·         The “ Decision Support “ that our consultants need is :

“ From amongst thousands of resumes in our data bank , which “ few “ should be sent to the Client ? Can software locate those few AUTOMATICALLY , which have “ Excellent Probability “ of getting shortlisted / selected ? “


Our consultants , today , spend a lot of time in doing just this manually – which we need to automate




Page   20
·         These ( few ) resumes are GOOD for this VACANCY


Page    22
·         According to me , this “ notation “ is :

All human Thoughts / Speech and Action , are directed towards either increasing the happiness ( of that person ) , OR

towards decreasing the pain ,

 
by choosing from amongst available Thoughts / Spoken Words / Actions .


This “ notation “ describes ALL aspects of Human Race


This ability to choose the most appropriate option ( at that point of time ), makes a human being “ intelligent “




Page   23
·         There are millions of “ words “ in English language – used by authors of books and poets in songs and lawyers in documents , but the words of interest to us are those used by Jobseekers in Resumes and by Recruiters in Job Advts . 

This is our area of expertise
·         Program = Control + Data ( Probabilities of 10,000 keywords occurrence amongst “ past successful “ candidates )

·         Problem Description > See remarks at the bottom of page 19 , for OUR problem description



Page    25
·         RESUMIX ( Resume Management Software ) claims to contain 100,000 “ rules “


Page  26
·         Our expertise in “ matchmaking “ of Jobseekers and “ Vacancies “ of Recruiters

·         Our business does fall in such “ Specialist “ category

·         Persons who have spent 15 years reading resumes / deciding their “ suitability and interviewing candidates



Page   27
·         Agree ! We do not expect “ Expert System “ to conduct interviews ! Our consultants do spend 2 / 3 hours daily in reading / shortlisting resumes


·         We want a “ Decision Support System “ to assist our consultants , so that they can spend more time in “ interview “ type of “ assessment “


·         If , during last 13 years , we have placed 500 executives , then we / client must have “ shortlisted “ 5,000 resumes . These are enough “ Test Cases “




Page   28
·         In last 13 years, this has grown may be 50 times ! – so that cannot be a limitation

·         I had perceived this as far back as 1996

·         Now ( in 2002 ) , expert systems have become an “ Essential “ to survival of all Organizations . We can ignore it at our peril !



Page   29
·         We can become VICTORS or VICTIMS : choice is ours

·         I am sure , by 2002 , we must have many “ MATURE “ expert system “ Kernels “ / “ Shells “ , commercially available in the market

·         We don’t need but we could talk to IIT ( Powai ) , TIFR or NCST professors of AI / Expert System for guidance



Page   30
·         Ask NCST ( Juhu Scheme ) if they can train us

·         May be we could send an email to Mr FORSYTH himself , to seek his guidance . We will need to explicitly state > our problem > solution which we seek , from the Expert System and ask him which , commercially available “ Shell “ does he recommend { Email : Richard.Forsyth@uwe.ac.uk }



Page  32
·         How many does this Directory list in 2002 ?

·         Google still shows CRI – 1986 , as the latest !

·         But , “ Expert Systems “ in Google returned 299,000 links !

·         I took a course in X-Ray Crystallography at KU in 1958




Page   33
·         When developed, our system would fall under this category

·         Most certainly, we should integrate the two


Page   35
·         The resumes shortlisted by our proposed “ Expert System “ ( resumes having highest probability of getting shortlisted ), must be manually “ Examined “ – and assigned “ Weightage “ by our consultants and these “ Weightages “ fed back into the System



Page   37
·         I believe, our system will be simple “ Rule – based “ – although, there may be a lot of “ processing “ involved in “ Sequential “ computation of Probabilities for “ Keywords “ related to :

#  Industry / Function / Designation Level / Age / Exp / Edu Quali / Attitudes / Attributes / Skills / Knowledge / Salary / Current Employer / Current posting location / family etc



Page   39
·         In my notes on ARDIS – ARGIS , see notes on “  Logic for……. “ . Here I have listed the underlying rules


Page   40
·         Expert Knowledge ( - and consequently the RULES ) contained in RESUMIX have relevance to USA jobseekers – and their “ style “ of resume preparation . 

These ( rules ) may not apply in Indian context



Page   41
·         We are trying to establish the “ Relationship “ between :

#  Probability of occurrence of a given “ keyword “ in a given resume,


WITH


#  Probability of such a resume getting “ shortlisted “


·         REASONING WITH UNCERTAIN INFORMATION

( Author’s Note : 

Many expert systems unavoidably operate in task domains where the available information is inherently imprecise ( rules derived from experts are inexact, data values are unreliable etc )


My Comment :  

If we have lost the resumes of 5,000 candidates who got shortlisted during last 13 years



Only “ Age “ and “ Exp ( years ) “ are dependent in our case




Page   42
·         Exp ( years ) can never be > Age ( years )

·         So , we will need to prepare a comprehensive list of “ inconsistencies “ , with respect to a resume eg : as shown above



Page   43
·         We should ask both ( the Expert System and the Experts ) to independently shortlist resumes and compare

·         We have to experiment with building of an expert system which would “ test / validate “ the assumption
 :
#  If certain ( which ? ) “ keywords “ or “ search parameters “ are are found in a resume , it has a higher probability of getting shortlisted / selected



Page   44
·         Eg: System shortlisting a “ Sales “ executive against a “ Production “ vacancy !

·         What / Which “ cause “ could have produced , What / Which “ Effect / Result “ ?



Page   45
·         In our case, the expert system, should relieve our consultants to do more “ Intelligent “ work of assessing candidates through personal interviewing


Page   47
·         Eg:

#  Entering email resumes in “ structured “ database of Module 1

#  Reconstituting a resume ( converted bio-data ) through ARGIS, automatically


For this “ tasks “ , we should not need human beings at all !


Read “ What Will Be “ ( Author : Michael Dertouzo / MIT Lab / 1997 )



Page  48
·         Even when our own Expert System “ shortlists “ the resumes ( based on perceived high probability of appointment ), our consultants would still need to go through these resumes before sending to Clients . They would need to “ interpret “


·         Read all of my notes written over last 13 years


Page   50
·         Our future / new consultants , need to be taken thru OES , step by step thru the entire process – thru SIMULATION – ie a fake Search Assignment


·         Our “ TASK AREA ” is quite obvious – but may not be simple , viz: we must find the “ right “ candidates for our clients, in shortest possible time


·         In 13 years, since this book was written , “ Mobile Computing “ has made enormous strides . Also internet arrived in a big way in 1995 . 

By March 2004 , I envisage our Consultants carrying their laptops or even smaller mobile computers & search our MAROL database for suitable candidates ( of course , using Expert System ) , sitting across client’s table




Page   51
·         “ To increase Expert productivity “ ( ie our consultants’ productivity ) and “ To augment Expert Capability “ ( ie to automate as many business processes as possible ) , are our objectives


Page   58
·         Resumes are “ data “ but when arranged as a “ Shortlist “ , they become “ information “ , because a “ shortlist “ is always in relation to our “ Search Assignment “ ! 

It is that search assignment that lends “ meaning “ to a set of resumes



Page   59
·         Are “ Resumes “ , knowledge about “ people “ and their “ achievements “ ?



Page   60
·         But , is a human , part and parcel of nature ? Human did not create nature but did nature create human ? Our VEDAS say that the entire UNIVERSE is contained in an ATOM. May be they meant that an entire UNIVERSE can arise from an atom


·         Are 204 “ Industries – Names “ and 110 “ Function Names “ , granular enough ? Can we differentiate well ?




Page  61
·         Read “ Aims of Education “ by A N Whitehead

·         Inference is process of drawing / reaching “ conclusion “ based on knowledge



Page   62
·         Calculating “ probabilities of occurrence “ of keywords & then comparing with Keywords contained in resumes of “ Successful Candidates “


Page   64
·         IF a resume  “ R “ , contains keywords “ a / b / c “

 , AND 

if resumes of all past “ SUCCESSFUL “ candidates ALSO contain keywords “ a / b / c “ ,


THEN ,


The chances are that , resume “ R “ will also be “ Successful “


·         Our expert system will be such an “ Automatic Theorem Proving System “ , where “ inference rules “ will have to be first figured out / established , from large volumes of past “ co-relations “ between “ Keywords “ & “ Successes “


·         “ Successes “ can be defined in a variety of ways , including : Shortlisting : Interviewing : Appointing : etc



Page   67
·         In our case too , we are trying to “ interpret / diagnose “ the “ symptoms “ ( in our case , the Keywords ) , contained in any given “ patient “ ( resume ) & then “ predict “ , what are its chances ( ie probabilities ) of “ success ( = getting cured ), ie: getting shortlisted OR getting interviewed OR getting appointed



Page   68
·         For us , there are as many “ rules “ as there are “ keywords ‘ in the resumes of past “ successful “ candidates – with the “ frequency of occurrence “ of each such keywords ( in , say , 7,500 successful resumes ) , deciding its “ weightage “ , while applying the rule


·         These resumes can be further sub-divided according to > Industry > Function > Designation Level etc , to reduce population size of keywords


Page   69
·         Our initial assumption :

    > Resume of “ Successful ( past ) Candidates “ , always contains keywords a / b / c / d /

  
         > Process >  Find all other resumes which contain a / b / c / d /


         >  Conclusion  >  These should succeed too
     *  Our Goal  >  Find all resumes which have high probability of “ success “


    *   System should automatically keep adding to the database of all the actually “
         successful “ candidates as each search assignment gets over





Page   70
·         In 1957 , this was part of “ Operations Research “ course at University of Kansas

·         With huge number-crunching capacities of modern computers , computational costs are not an important consideration any more




Page   71
·         Cut finger and pain follows

·         Somewhat similar to our situation of resumes & keywords


Page   72
·         We are plotting “ frequency of occurrence “ of keywords in specific past resumes to generalize our observations

·         Knowledge Keywords / Skills keywords / Attitude Keywords / Attribute Keywords / Actual Designations


We can construct a TABLE like this ( with such column headings ) , from our 65,000 resumes & then try to develop “ algorithm “


·         In above table , the last column ( Job or Actual Designation ) can also be substituted by : Industry OR Function OR Edu Quali etc,


And a new set of “ algorithms “ will emerge





Page   74
·         Our resumes also “ leave out “ a hell of a lot of “ variables “ ! 

A resume is like a jigsaw puzzle with a lot of missing pieces ! 

We are basing on statistical forecasting Viz: “ frequency of occurrence “ of certain keywords & attaching ( assigning ) probability values



Page   75
·         This statement must be even more true today – 13 years since it was first written



Page   76
·         Just imagine , if we can locate & deliver to our client , just THAT candidate that he needs ! – in the first place, just THOSE resumes which he is likely to value / appreciate


·         I am sure , by now superior languages must have emerged. Superior hardware certainly has , as has “ conventional tools “ of database management



Page   77
·         Perhaps what was “ specialized “ hardware in 1989 , must have become quite common today in 2002 – and fairly cheap too


Page   81
·         We must figure out ( - and write down ) , what “ Logic / Rules “ our consultants use ( even subconsciously ) , while selecting / rejecting a resume ( as being “ suitable “ ) for a client – need .

 Expert System must “ mimic “ a human expert


·         We are basing ourselves ( ie : our proposed Expert System ) on this “ type “ ( see “ patterns “ in book “ Digital Biology “ )




Page   87
·         Illness = Industry or Function

·         Symptoms = Keywords

·         Probability that this keyword ( symptom ) will be observed / found , given that the resume ( patient ) belongs to XYZ “ Industry “ ( illness )




Page  88
·         Random Person = any given “ incoming “ email resume

·         Influenza          = “ Automobile “ industry

·         Based on our past population ? (“ Auto “ resume) divided by ( all resumes ) probability

·         If symptoms = keywords , which symptoms ( keywords ) , have appeared in “ Auto “ industry resumes , OR which keywords have NEVER appeared in “ Auto “ resumes , OR appeared with low frequency ?




Page   89
·         Pattern matching ( as demonstrated in book “ Digital Biology “ )

·         With addition of all keywords ( including NEW keywords – not previously recorded as “ keyword “ ) from each NEW / INCOMING resume , the “ prior probabilities “ will keep changing




Page  90
·         In this “ resume “ , assuming it belongs to a particular “ INDUSTRY “

  ,
    #   what keywords can be expected

    #   what keywords may NOT be expected


·         We can also reverse the “ Reasoning “ viz:

    #  What “ INDUSTRY “ ( or FUNCTION ) might a given incoming resume belong to, if :


·         Certain keywords are absent ?

    The “ result / answer “ provided by Expert System , can then be tested / verified with WHAT jobseeker himself has “ clicked “




Page   91
·         Reiteration : so each new incoming resume would change the “ Prior Probability “ ( again and again ) , for each > Industry > Function > Designation Level > Edu Quali > Exp , etc 

( Handwritten graph drawn on page where X axis = No of resumes , and Y axis = Probability ) , initial wide oscillations would converge as more and more resumes get added to the database )



Page   92
·         With 65,000 resumes ( ie; patients ) & 13,000 Keywords ( symptoms ) , we could get a fairly accurate “ Estimate “ of “ Prior Probabilities “ . 

This will keep improving / converging as resume database & keywords database keeps growing ( especially , if we succeed in downloading thousands of resumes ( or job advts ) from Naukri / Monster / Jobsahead etc


·         Eg ; Birthdate as well as Age


·         This is good enough for us



Page   93
·         Eg ; In Indian Resumes , keyword “ Birthdate “ would have the probability of 0.99999  !  

Of course , most such keywords are of no interest to us !


Page   95
·         For our putpose , “ keywords “ are all , “ items of evidence “ . 

If each & every “ keyword “ found in an ( incoming ) resume, corresponds to our “ hypothesis “ ( viz: keywords A & B & C , are always present in resumes belonging to “ Auto Industry “ ) , then we obtain max possible “ Posterior Probability “


·         So , if our knowledge base ( not only of keywords , but phrases / sentences / names of current & past employers / posting cities etc ) is VERY WIDE & VERY DEEP, we would be able to formulate more accurate hypothesis & obtain higher “ Posterior Probability “



Page   96
·         So , the key issue is to write down a “ Set of Hypothesis “


Page   97
·         Let us say, keyword “ Derivative “ may have a very LOW “ frequency of occurrence “ in 65,000 resumes ( of all Industries put together ) but , it could be a very important keyword for the “ Financial Services “ Industry



Page   98
·         Eg: certain keywords are often associated ( found in ) with certain” Industries “ or certain “ Functions “ ( Domain keywords )



Page   99
·         With each incoming resume , the probability of each keyword ( in the keyword database ) will keep changing.

·         Eg ; Does “ Edu Quali “ have any role / effect / weightage in the selection of a candidate ?

·         Eg; What is the “ Max Age “ ( or Min Exp ) at which corporate will appoint a > Manager > General Manager > Vice President ?



Page   100
·         Line of Best Fit ?


Page   103
·         Say , every 20th incoming resume belongs to > XYZ industry > ABC function ( based on frequency distribution in existing 65,000 resumes )


Page   104
·         If an incoming resume belongs to “ Auto Industry “ , it would contain keywords “ Car / Automobile “ etc OR Edu Quali = Diploma in Auto Engineering

·         Eg; Probability of selection of an executive is 0 ( zero ), if he is above 65 years of age !

·         One could assign “ probability of getting appointed  “  OR even “ probability of getting shortlisted “,  for each “ AGE “ or for each “ Years of Experience “ , or for each “ Edu Level “ etc




Page   105
·         Obviously :

      #  a person ( incoming resume ) having NO EXPERIENCE ( fresh graduate ) will NOT
          get shortlisted for the position of a MANAGER ( Zero probability )


     #  a person ( incoming resume ) having NO GRADUATE DEGREE , will NOT get shortlisted for the position of a MANAGER ( zero probability )


    #  a person ( incoming resume ) with less than 5 years of experience, will NOT get shortlisted for the position of GENERAL MANAGER ( zero probability ),


BUT,


   # will get shortlisted for the position of SUPERVISOR ( 0.9 probability )




Page   106
·         So , we need to build up a database of WHO ( executive ) got shortlisted/ appointed , by WHOM ( client ) and WHEN and WHY ( to best of our knowledge ) over the last 13 years & HOW MUCH his background matched “ CLIENT REQUIREMENTS “ & KEYWORDS in resumes



Page   107
·         How “ good “ or “ bad “ is a given resume for a given “ client needs “ ?

·         Co-relating > “ search parameters “  used , with  >  “ search results “ ( resumes / keywords in resumes ) , for each and every , online / offline  “ resume search “


Page   109
·         Is this like saying , “ Resume A belongs to ENGINEERING INDUSTRY , to some degree and also to AUTOMOBILE INDUSTRY , to some degree ? .

 Same way can be said about “ Functions “

·         Same as our rating one resume as “ Good “ & another as “ Bad “ ( of course , in relation to client’s stated needs )



Page   110
·         Eg; set A  >  Resume belonging to ENGINEERING industry

                Set B  >  Resume belonging to AUTOMOBILE industry

·         A resume can belong to both the sets with “ different degree of membership “


Page   114
·         In our case , we have to make a “ discrete decision “ , viz; “ Shall I shortlist & forward this resume to client or not ? “


Page   115
·         A given keyword is present in resume , then treat the resume as belonging to “ Engg Industry “  OR ( better ) , a given “ Combination of keywords “ being present or absent in a resume , shall decide whether that resume should be classified / categorized under ( say ) “ Engg Industry “


·         For us , “ real world experience “ = several sets of “keywords “ discovered in 65,000 resumes which are already categorized ( by human experts ) as belonging to Industry ( or Function ) , A or B or C



Page   118
·         Eg; A given resume is a ,

#   very close match

#   close match

#   fairly close 
match


#   not a very close match


With client’s requirements



Page  119
·         I am sure , by now many more must be commercially available


Page   127
·         Who knows , what “ NEW / DIFFERENT “ keywords would appear ( - and what will disappear ) , in a given type of resumes, over next 5 / 10 years ?


·         We may think of our Corporate Database in these terms & call it “ Corporate Knowledgebase “



Page   129
·         One could replace “ colour of teeth “ = Designation Level ( MD / CEO / President / Gen Mgr etc )

·         Worth checking , now that 14 years have elapsed

·         “ Data Structure “ Tabulation with Keywords listed against following column headings :


Industry / Function / Designation Level / Edu / Age / Current Employer / Skills / Attitude / Attribute




Page   132
·         Is this somewhat like saying : “ This ( given ) resume belongs to Industry X or Industry Y ? “

·         Number of times a given resume got shortlisted in years : X  / X + 1 / X + 2 / etc

·         If we treat “ getting shortlisted “ as a measure of “ Success “ ( which is as close to defining “ success “ , as we can ) = prize money won

·         Of course, in our case , “ No of times “ getting shortlisted ( in any given year ) is a function of > Industry background > Function background > Designation Level > Edu > Age ( max ) > Exp ( min ) > Skills > Knowledge etc



Page  133
·         Which is what the resumes are made up of ( - sentences ) . 

See my notes on ARDIS ( Artificial Resume Deciphering Intelligent System ) & ARGIS ( Artificial Resume Generating Intelligent System )




Page   136
·         This was written 13 years ago . In last few months, scientists have implanted micro-processors in human body & succeeded in “ integrating “ these ( chips ) into human nervous system ! eg: restoring “ vision “ thru chip implant ( Macular Degeneration of Retina )



Page   139
·         There is no doubt Princeton must have made tremendous progress in last 13 years

·         We now have “ Gigabyte “ ( 1000 times bigger )

·         How about a resume ?


Page   140
·         ( A weather predictor ) : Every nation has a “ weather Forecast Satellite “ these days


Page   143
·         In ARDIS , I have talked about character recognition / word recognition / phrase recognition


Page   148
·         You see what you “ want “ to see & you hear what you “ want “ to hear

·         I have no doubt these ( object oriented languages ) must have made great strides in last 20 years



Page   149
·         Are our 13,000 keywords , the “ Working Memory “ ?

·         Eg: Frequency distribution of Keywords belonging to ( say ) 1,000 resumes belonging to “ Pharma “ Industry , is a “ pattern “ . When a new resume arrives , the software “ invokes “ this “ pattern “ to check the amount / degree of MATCH



Page   150
·         Eg : This is like starting with an assumption ( hypothesis ) that the NEXT incoming resume belongs to “ Auto “ industry or to “ Sales “ function & then proceed to prove / disprove it



Page   151
·         Keywords pertaining to any given “ Industry “ or “ Function “ will go on changing over the years, as new skills and knowledge gets added . So , recent keywords are more valid


·         “ Balanced Score Card “ was totally unknown 2 years ago !



Page   152
·         In case of “ keywords “ , is this comparable to ordering ( ie; arranging ) by frequency of occurrence ?


Page   153
·         Treating a child as an “ Expert System “ , capable of drawing inferences

·         Eg; Blocks of different colours

·         Rules will remain same

·         Keywords will change over time ( working memory )

·         Gross ( or Crude ) search, to be refined later on


Page   154
·         Obtaining an understanding of what the system is trying to achieve

·         I suppose this has already happened




Page   155
·         See my notes on “ Words and Sentences that follow “


Page   178
·         We are thinking in terms of past “ Resumes “ which have been “ shortlisted “

·         I have already written some rules

·         This must have happened in last 13 years !


Page   179
·         We must question our consultants as to what logic / rules do they use / apply ( even subconsciously ) to “ shortlist” or rather select from shortlisted candidates



Page   181
·         List of “ Knowledge Acquisition Tools “ developed by us :

       Highlighter  /  Eliminator  /  Refiner  /  Compiler  /  Educator  /  Composer  /
       Match-maker  /  Member Segregator  /  Mapper ( to be developed )




Page   186
·         I have written some rules but many more needs to be written


Page   191
·         Like “ weightages “ in Neural Network ?

·         To find “ real evidence “ , take resumes ( keywords ) AND “ Interview Assessment Sheets “ of “ successful “ candidates & find co-relation

·         Eg: Rules re “ Designation Level “ could conflict with rules re : “ Experience Years “ or rules re “ Age ( min ) “




Page   192
·         Eg: Edu Quali = CA / ICWA / MBA etc , for FINANCE function

·         Basic Degree in Commerce ( B Com )


·         “ Rule Sets “ on ,

Age ( max ) / Exp ( min ) / Edu Quali / Industry / Function / Designation Level etc


·         If our Corporate Client can explicitly state “ weightages “ for each of the above, it would simplify the design of the Expert System . 

Mr Nagle had actually built this ( weightages ) factor in our HH3P search engine , way back in 1992 . 

Instead of clients , consultants entered these “ weightages “ in search engine ; but resume database was too small ( may be < 5,000 )



Page   197
·         Quacks Vs Doctors

·         Quacks , sometime do “ cure “ a disease, but we would never know why or how !


Page   198
·         We have databases of ,

       #   Successful ( or “ failed “ ) candidates . and
       #   their “ resumes “ & “ Assessment Sheets “
 , 
and

       #   keywords contained in these resumes



Page   199
·         We will need to define which are “ successful “and which are “ failure “ candidates ( of course , in relation to a given vacancy ) . 

We want to be able to predict which candidates are likely to be “ successful “


·         We must have data on past 500 “ successful “ & 5000  “ failure “ candidates in our database



Page   222
·         Abhi : this is exactly what our “ Knowledge Tools “ do :

Highlighter  /  Eliminator  /  Refiner  /  Sorter  /  Matchmaker  /  Educator  /  Compiler  /  Member Segragator  etc





Page   227
·         Diebold predicted such a factory in his 1958 book , “ Automatic Factory “

·         Someday, we would modify our OES ( Order Execution System ) in such a way that our clients will “ Self Service “ themselves

·         This was written in 1989


·         E Commerce defined 13 years ago


·         “ SIVA “ ( on Jobstreet.com ) has elements of such a Self-Serving system



Page   232
·         Statistical Analysis of thousands of job advts of last 5 years , could help us extrapolate next 10 year’s trend



Page   236
·         We are planning a direct link from HR Managers to our OES ( which is our factory floor ) . 

We still need “ consultants “ – but only to interact ( talk ) with clients and candidates ; not to fill in INPUT screens of OES !



Page   237
·         Obviously , Author had a vision of Internet – which I could envision 2 years earlier in my report to L&T Chairman


( QUO VADIS / 1987 )




Page  239

·         Norbert Weiner had predicted this in 1958
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