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When I read any book, I scribble my comments / notes in the margins
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Page no. 3
·
I read his “Human use of human Beings,” around 1957/58. Wiener was
called “father of cybernetics”- in fact, he coined the word “Cybernetics.”
(30-08-2002).
Page no. 6
·
Is this like our saying “If such & such keywords appear in a
resume, THEN it may belong to such – Industry or Function? (30-08-02).
Page no. 7
·
It is another matter that DEC went bankrupt & was taken
over!
·
I believe the “knowledge contained in our 65000 resumes, is good
enough to develop an expert system (ARDISARGIS)
·
We started work on ARDIS/ARGS in 1996!
·
But taken-up seriously, only 3 months ago. (30-08-02).
Page no. 8
·
“Keywords”
are nothing but “descriptions of resumes.
Page no. 9
·
This was written 13 years ago. May be, by now, there “problems”
have been solved.
Page no. 10
·
Back-end
·
Front –end
Page no. 11
·
Inder/Anjaria/our
consultants
·
I believe ISYS
manual speaks of “context tree” so does oracle context cartridge (Themes)
Page no. 14
·
As in our case
Page no. 15
·
In our case
·
“Hypo the sized outcome” could be a resume
·
Getting shortlisted by 3p
·
“ “ “ Client
·
Or
·
A candidate getting “appointed (after interview)
·
In our case
·
The “presence of the evidence” could be presence of certain
“keywords” in a given resume (the house) or certain “Edu. Qualification” or
certain “age” or certain “Exp (yrs.)” or certain “current Employer”etc. etc.
Page no. 16
·
In our case, these several “pieces of evidence “could be
·
Keywords
·
Age
·
Exp.
·
Edu Quali.
·
Current Industry back grad
·
Current Function background
·
Current Desig. Level
·
Current salary
·
Current Employer etc. 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 (atatistracally) resumes of all candidates
shortlisted during last 13 yrs. to calculate “ODDS.” (30-08-02).
Page
no. 18
·
“Automating the process of Knowledge Acquisition? We could do
this (automating) by getting the jobseeker to select/fill in keywords online,
in the web from. (31-08-02).
Page
no.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 databank, which “few” should
be sent to 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 auto mate.
Page
no. 20
·
These
(few) resumes are “GOOD” for this “vacancy.”
Page
no. 22
·
According to me, this “notation” is:
·
All human thoughts/speech & Action, are directed towards
either increasing the happiness (of that person) or towards decreasing the
pain, by choosing from amongst available thought/spoken words/Actions” this
notation describes all aspects of
human-race.
·
This ability to choose the most appropriate option (at that
point of time), make a human being “intelligent.” (31-08-02).
Page
no. 23
·
There are millions of “words” in English Language-used by Authors in
Books & poets in songs & Lawyers in documents but the words of interest
to us are these used by jobseekers in Resumes & Recruiters in
job-advts. This is our area expertise
·
Probabilities of 10000 Keywords occurrence amongst “past successful”
candidates.
·
See remarks at the bottom of P=19 for “OUR” problem- description.
Page
no. 25
·
“RESUMIX” (resume management software) claims to contain 100,000
“rules.”
Page
no. 26
·
Our expertise in “match making” of jobseekers & “vacancies”
of Recruiters.
·
Our business does fall in such a “specialist” category.
·
Person who have spent is years reading resumes/deciding their
“suitability & interviewing candidates.
Page
no. 27
·
See remarks at bottom of p=19
·
In der & May be some of our own consultants.
·
Agree. We do not expect “Expert system” to conduct interviews!
·
Our consultants do spend 2/3 hours daily in reading/shortlisting
resumes.
·
Abhi, Rajeev
·
We want a “Decision support system” to assist our consultants so
they can spend more time in interviews type of assessment.
·
If, during last 13 years, we have placed 500 executives, then
we/client must have short-listed 5000 resumes. These are enough “Test cases.
·
Must be several hundred by 2002.
Page
no. 28
·
In
last 13 years, this has grown may be 50 times! 50 that cannot be a limitation
·
Now
we have 4th generation. Now we have PENTIUM 4.
·
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
no. 29
·
We can become VICTORS-or the VICTIMS: choice is ours.
·
I am sure by 2002, we must have many “MATURE” expert system
“keywords” “shells,” commercially available in the market.
·
New available for $40. (3000).
·
Abhi, Rajeev, Deepa+ Inder IIT (Pawai),
·
We don’t need but we could talk to TIFR or NCST professors of
AI/Expert system for “guidance”(31-08-02).
Page
no. 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 Export system & ask him which commercially available “shell”
does he recommend.
·
Email: Richard. Forsyth@ uwe.ac.uk. (31-08-02).
Page
no. 31
·
Which
it actually did
Page
no. 32
·
How many does this Directory list in 2002?
·
Google still shows CRI-1986 as the latest! But “Expert system”
in Google returned 299000 links!
·
I took a course in x- ray crystallography at KU in 1958.
(31-08-02).
Page
no. 33
·
When developed, our system would fall under this category.
·
Most certainly we should integrate the two.
Page
no. 35
·
The
resumes shortlisted by our proposed “Expert system” (resume having highest
probability of getting shortlisted), must be manually “examined” –and assigned
“weightage,” by our consultants & these “weightages” fed back into the
system.
Page
no. 37
·
This may have happened by now. By now, quite cheap
·
Abhi, Rajeev
·
I believe our system will be simple “rule-based”-
although, there may be a lot of “processing” involved, in “sequential”
computation of profanities 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
no. 38
·
“RESUMIX”
claims to have 100,000 rules.
Page
no. 39
·
Abhi, Rajeev
·
In my notes on ARDIS/ ARGIS, see separate notes on “logic for….
Here, I have listed the under lying rules. (31-08-02).
Page
no. 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 no. 41
·
We are trying to establish the “relationship” between:
·
Probability of occurrence of a given “keyword” in a given resume
·
Probability of such a resume getting “shortlisted.”
·
Eg: if we have lost the
resumes of 5000 candidates who got shortlisted during last 13 years!
·
Only “Age” & “Exp
(yrs)” are dependent in our case.
Page no.
42
·
Exp (yrs) can never
be > Age (yrs)
·
So, we will need to
prepare a comprehensive list of inconsistences with respect to a resume, eg: as shown above. This will be an ongoing
take for Inder. (01-09-02).
Page no.
43
·
Abhi, Rajeev
·
We should ask both
(the Expert system& the experts) to independently short list resumes &
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 found in a resume, it has a higher
probability of getting/selected.
Page no.
44
·
eg: system
shortlisting a “sales” executive against
a “production” vacancy!
·
What/which “cause”
could have produced what/which “Effect/Result.”
Page no. 45
·
“Human use of Heman
beings” by Norbert wiener.
·
In our case, the
expert system should relieve our consultants to do more “In Telligent” work of
assessing candidates thru personal
interviewing.
Page no. 47
·
Eg: Entering email
resumes in “structured” database of module I
·
Reconstituting a
resume (converted bio data) thru ARGIS, automatically
·
For these tasks, we
should not need human beings at all.
·
Read “what will be”
by Michael Dertouzo (MIT lab) 1997
Page no.
48
·
Even when our own Expert system “shortlists” the resumes (based on
perceived high probability of appointment), our consultants would still need to
go thru their resumes before sending to clients. They would need to
“interpret.”
·
No wonder Google retums 299000 links when you search for “Expert
systems”! Forsyth had foresight!
·
This is Inder`s main task.
·
This is ME!
·
Read all of my notes, written over last 13 years. (01-09-02).
Page no.
49
·
My notes
·
Inder, Abhi, Rajeev
Page no. 50
·
Our future/new
consultants need to be taken thru OES, step-by step thru the entire process
–thru SIMULATION fake search assignments
·
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 data base for suitable candidates (of course using Expert
system), sitting across client`s table.
Page no.
51
·
We can repeat this
even in 2002!
·
Our consultants’
productivity
·
These are our
objectives.
·
To automate as many
“business processes” as possible.
Page no.
57
·
Data bases & knowledge bases.
Page no.
58
·
Abhi, Rajeev
·
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 no.
59
·
Are “Resumes,”
Knowledge about “people”? & their “Achievements?
Page
no.60
·
But is a human, a part
& parcel of nature? Human did not create the 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!
·
Experience *Age
·
Are 204 industries –
names&110
·
“Function-name” granular
enough? Can we differentiate well? (08-09-02).
Page no.
61
·
Read “aims of
Education” by A. N. WHITEHEAD
·
Inference process of
Drawing/Reading “conclusion “based on Knowledge
Page no.
62
·
Calculating probabilities
of occurrence of keywords & then comparing with keywords contained in
“Resume” of successful candidates
Page no.
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
·
Contusion:-
·
Resume R will also
be such an
·
“Successful”.
·
Our expert system
will be such an “Automatic Theorem proving system”
·
Where “Inference-rules” will have to be fist
figured-out/established, from large volumes of past co-relations between “keywords” & “successes.”
Page no.
65
·
Abhi, Rajeev
·
“Successes” can be
defined in a variety of ways, including :
·
Shortlisting
·
Interviewing
·
Appointing etc. et
Page no. 66
·
Abhi, Rajeev
Page no. 67
·
In our case too, we
are trying to “Interpret/diagnose” the “systems’ (in our case-the keywords)
·
Contained in any
given resume (patient?)& then “predict” what are its chances (i.e.(=getting cured)
·
i.e. getting shortlisted
or
·
Getting interviewed
or
·
Getting appointed
·
(08-09-02).
Page no. 68
·
These resumes can be
further sub-divided according to
·
Industry
·
Function
·
Design-level etc. to
·
Reduce population
size of keywords.
·
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 of (in, say, 7500 successful resume), deciding its “weightage ”while
applying the rule.
Page no. 69
·
Our initial
assumption – Resumes of “successful (past) candidates” always contain a, b, c,
& d,
·
Process – Find all
other resumes which contain a, b, c,& d.
·
Concision- These
should “succeed” too.
·
Our Good –find all
resumes which have high resume probability of “success.
·
System should
automatically, keep adding to the database all the actually successful candidates as each search
assignment gest over.
Page no. 70
·
In 1957, this was
part of “operations Research” course at uni: of Kansas.
·
With huge number,
crunching computers, computational costs are not an important consideration
anymore.
Page no. 71
·
Cut-finger & Pain
follows (consequence) somewhat similar to our situation of resumes &
keywords.
·
(10-09-02).
Page no.
72
·
We plotting
frequency of occurrence of keywords in specific past resume to generalist our
observations
·
We can consultants a
take like this from our 65000 resumes & then try to develop “algonthms.
·
Abhi, Rajeev
·
In above table the
last column (Jolo or Actual Designation can also be substituted by & a new
set of “algorithms “will emerge.
·
Industry
·
Function
·
Edu.Quali
Page no. 74
·
Abhi, Rajeev
·
Our resumes also
“leave out” a hell of a lot of “variable”! 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) probability
values.
Page no. 75
·
This statement must be even more true today -13
years since it was fist written
Page no.76
·
Just imagine, if we too can locate & deliver to our client,
candidate that he need! In the first place just those resumes, which he likely
to value appreciate.
·
I am sure by now superior hardware certainly has, as has
“conventional Tools” of database management.
Page no. 77
·
Perhaps what was
“specialized” hardware in 1989, must have become quite common today in 2002 and
fairly cheap too.
Page no. 81
·
We must figure-out (-and
write down), “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 (i. e. our proposed expert system) on this “Type” (see “patterns
“in “Digital Biology”)
Page no. 82
·
This is what we are after.
Page no. 87
·
Industry/Function
etc.
·
Keywords?
·
Probability that
this keyword (symptom) will be observed/found, given that the resume
(patient) belongs to XYZ industry (lines)
Page no, 88
·
Random person = any
given “incoming” email resume Influenza =”Automobile” Industry
·
Based on our past
population? =”Auto” Resume All resumes probability
·
If symptoms =keywords,
which symptoms (keywords) have appeared in “Auto” resumes or which keywords
have never appeared in “Auto” resumes OR appeared with low frequency?
Page no. 89
·
As demon started in
“Digital biology”
·
With addition of all
keywords (including new keywords not previously recorded as “keywords”)
·
From each new,
incoming resume, the “prior probabilities” keep changing. (13-09-02).
Page no. 90
·
In this “Resume,” it
belongs to a particular Industry
·
What keywords may
can be expected
·
What keywords may
not be expected We can also, resume the “Reasoning” viz:
·
What “Industry” (or
Function) might a given incoming resume belong to, if,
·
Certain keywords are
present and
·
Certain keywords are
absent? The “result/answer” provided by Expert system can then be tested/
verified with what jobseeker has himself “clicked.”
Page no. 91
·
Re-iteration so each new incoming resume would change the “prior
probability” (again & again) for each
·
Industry
·
Function
·
Designation
·
Edu. Quli
·
Exp. etc. etc.
Page no. 92
·
With 65000 resume
(i.e. patients) & 13000 keywords (systems) we could get a fairly accurate
“estimate” of “prior probabilities” this will keep improving/converging as
resume as resume database & keywords database keeps growing especially if
we can succeed in down lodging thousands (or job-davits) from Naukari/monster
jobs ahead etc. etc.
·
Eg: Birthdate as
well as Age.
·
This is good enough
for us. (13-09-02).
Page no. 93
·
Eg: In Indian Resumes, keywords keyword
“Birthdate” would have the probability 0.99999. of course most such keywords are of no interest to us!
Page no. 95
·
For our purpose, “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. ”Posterior probability”
·
So, if our knowledge
base (not only of keywords, but phrases/names of current &
past/sentences/employers/ posting cities etc.) is VERY WIDE & VERY DEEP, we
would be able to formulate more accurate hypothesis & obtain higher
posterior probability. (14-09-02).
Page no. 96
·
So, the key issue is
to write down a “set of Hypothesis”
Page no. 97
·
Let us say, keyword
“Derivative” may have a very low frequency of occurrence in 65000 resumes (of
all Industries put together) but, it
could” be a very important keyword for the “financial service” Industry.
Page no. 98
·
Eg: certain key words are often associated
(found in) with certain “Industries “or certain “Function” (Domain Keywords).
·
Page no. 99
·
With each incoming
resume, the probability of each keyword (in the keyword data base) 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 corporates will appoint a
·
Manager
·
General Manager
·
Vice – President
·
(23-09-02).
Page no. 100
·
Line of Best Fit?
Page no. 103
·
Hypo theses
·
Evidence
·
Say, every 20th
incoming resume
·
Based on frequency
·
Distribution in
existing 65000 resumes.
·
Belongs to
·
XYZ Industry
·
ABC Function
Page no. 104
·
Evidence: If an incoming
resume belongs to “Auto” industry it would contain keywords
“car”/”Automobile/or Edu. Quali =Diploma in Auto –Engineering
·
Eg: probability of
selection of an executive is O (zero)if he is above 65 yrs. of age!
·
One could assign
“probability of getting appointed” for each “Age” or for each “years of
Experience” or each “Edu. Level etc. etc.
·
This is very similar to
asking: “will this executive (incoming resume) NOT get shortlisted? OR NOT get
appointed?”
Page no. 105
·
Obviously
·
A person (incoming
resume) having no experience (fresh graduate) will NOT get shortlisted for the
position MANAGER (zero probability)
·
A person (incoming
resume) having NO graduate degree, will
NOT get shortlisted for the position of MANAGER (zero probability)
·
A person (incoming
resume) with less than 5 yrs. of experience will NOT get shortlisted for the
position of GENERAL MANAGER (zero probability) but , will get shortlisted for
the position of SUPERVISOR(o.9 probability) etc. etc. (23-09-02).
Page no. 106
·
So, we need to
build-up a database of who (executive) got shortlisted/appointed by whom
(client) & when & why (to lest of our knowledge) over the last 13 years
& how much his lock ground matched “client requirements”& keywords in
resumes.
Page no. 107
·
How “good” or “bad”
is a given resume for a given client need?
·
Co-relating 1.
Search- parameters used, with 2. Search-results (resume/keywords in resumes)
for each & each very, online/offline Resume-search.
Page no. 108
·
Now 37 yrs. Even in
washing machines
·
Is this like
saying,” resume A belongs to Engineering Industry, to some degree and also to
Automobile Industry to “some degree?
·
Some as our rating
one resume as “Good” & another as “Bad” (of course in relation to clients
stated needs).
Page no. 110
·
Eg; set A – Resumes
belonging to Eng. Industry & set B – Auto
·
A resume can belong
to both the sets with different degree of membership”
Page no. 114
·
In our case we have
to make a “discrete decision
·
Viz: shall I
shortlist & forward this resume to client, or net?
Page no. 115
·
A given keyword is present in resume, and then treat the resume as
belonging to “Eng” industry
·
OR (better) a given “ combination of keywords being present or
about in a resume, shall decide whether that resume should be classified/under
(sey) “Eng” Industry.
·
For us “real world experience “ =several sets of “keywords
“discovered in 65000 resumes which are already categorized (by human experts)
as belonging to industry (or function) A or B or C.
Page no. 118
·
Eg: A given resume
is a
-
Very close match
-
Close match
-
Fairly close match
-
Not a very close
match
·
(With client’s
regalements)
Page no. 119
·
I am sure, by now, many more must be
commercially available.
Page no. 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 & call it “corporate Knowledge base.
Page no. 129
·
One could replace
·
“colour – teeth=
·
“Designation level”
·
=MD
·
OEO
·
President
·
Gen-mgr. etc.
·
Worth checking-now
those 14 yrs have elapsed.
Page no. 130
·
Have they?
Page no. 132
·
Is this somewhat
like saying “this (given) resume belong to Industry
Page no. 133
·
Which is what the
resumes are made up of (-sentences). See my notes on ARDIS & ARGIS
Page no. 136
·
This was written 13 yrs ago. In last few months, scientists have
implanted simple microprocessors in human body& succeeded in “integrating”
these (chips) into human nervous system!
Page no. 139
·
There is no doubt
pronation must have made tremendous progress in last 13 years!
·
We now have
“Gigabyte”(1000 times bigger)
Page no. 140
·
Every major nation
has a “weather forecast satellite these days.
Page no. 143
·
In ARDIS I have
talked about character recognition/word recognition/phrase sentence recognition
Page no. 178
·
We are thinking in
terms of past resum,es which have been “shortlisted” I have already written
some rules.
·
This must have
happened in last 13 years!
Page no. 179
·
We must question our
consultants as to what logic/ rules do they use/ apply shortlist or rather
select from shortlisted candidates
Page no. 180
·
This list must have
vastly grown by now.
Page no. 186
·
I have written some
rules but many more need to be written.
Page no. 191
·
Like weightages in neural networks?
·
To find “real evidence” take resumes (keywords) “interview
Assessment sheets” of “successful” candidates & find correlation.
·
Eg: Rules “Designation –level could conflict with rules
·
“Experience (years) or “Age (Min)”
Page no. 192
·
Basic degree in commas
(B.com)
·
“Rule sets on
·
Age (Max)
·
Exp (Min)
·
Edu.Quali
·
Function
·
Designation etc.
·
If our corporate client
can explicitly state “weightages for each of the alove it would simplify the
design of expert system mr. Nagle had actually built this (weightages) factor
in our HH3P search engine way back in 1992.
Page no. 197
·
Quacks Vs Doctors?
·
Quacks sometime do
“cure” a disease, but we would never “Know” why or how!
Page no. 198
·
We have databases of
·
Successful (or
“failed”) candidates, and
·
Their “resumes”
& “Assessment sheets” and
·
Keywords contained
in these resumes
Page no. 199
·
We must have data on
past 500 successful & 5000 “failure” candidates in our database
Page no. 219
·
This has happened by now
Page no. 222
·
Abhi
·
This is exactly what
our knowledge Tools do:
·
Highlighter
·
Eliminator
·
Refiner
·
Match maker
·
Educator
·
Compiler
·
Member- segregators
etc.
Page no. 223
·
We are one.
Page no. 224
·
Resume & job advt.
databases on jobsites
Page no. 227
·
This was written in
1989!
·
e- Commerce defined
13 yrs. ago.
·
“ SiSA” on
jobstreat.com has elements
·
Of such a self-
serving system.
Page no. 231
·
Our knowledge Tools,
do this.
Page no. 232
·
Statistical analysis
of thousand of job advts of last 5 years, could help us extrapolate next 10
year`s trend.
Page no. 236
·
We are planning a
direct link-from HR mgr. to our OES-(which is our factory floor)
·
We will still need
“consultants” but only to interact (talk) with clients & candidates not to
fill in IN PUT screens of OES!
Page no. 237
·
Obviously Author had
a vision of Interest
·
Which 1 could
envision 2 years earlier “QUO VADIS”
Page no. 239
Norbert wiener had predicted this in 1958.
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