Hi Friends,

Even as I launch this today ( my 80th Birthday ), I realize that there is yet so much to say and do.

There is just no time to look back, no time to wonder,"Will anyone read these pages?"

With regards,
Hemen Parekh
27 June 2013

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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