Dialogue with Authors
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When I read any book, I scribble my comments / notes in the
margins
These reflect my views / opinions about what the author is
saying – including my disagreement
Often, my comments are in the nature of telling myself :
Hey ! We should try out this idea in our own business (
Head-hunting / Online Recruitment )
Following are my comments re :
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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,
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
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 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
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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