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When I read any book, I scribble my comments / notes in the margins
Page No 16
·
This is how Hindu Vedas deceived,
both the universal & the “Atma “-Anadi [without a beginning] [shashwat=permanent]-Anant
[withontan end]
Page No 18
·
This two has been proved
wrong a few months ago!
Page No 19
·
Matter &
Anti-matter/Dwait& Adwait [Two-ism] & [unit-ism] of Vedanta.
·
Maya wad=Illusion-ism
·
It is Anadi [without a
beginning!} without an origin.
Page No 22
·
Hindu scriptures also
describe “Atma “as being “Nirguna” [I, without any “properties]
Page No 23
·
What caused this chaotic
universe to come into existence?
Page No 24
·
Already disproved
Page No 44
·
Just “different”
Page No 46
·
So does business.
Page No 55
·
From 50,000 resumes we need
to compile 500,000 Keywords and “evolve” co-relation between every unique set
of keywords with “salary levels”& “Designation levels “which are the
results of “SELECTION “by the Businesses evolution process 21/04/02.
·
This can be made into a
predictive technique-whether an executive will fail or succeed in his next job.
Page No 56
·
Or “keywords”-belonging to a
“language universe’?
·
Employee does not trust an
unpredictable boss!
·
Software’s with a capacity to
“learn”
Page No 75
·
Neural network software works on
this basis.
Page No 76
·
Post experience
Page No 81
·
Also an illusion?
Page No 83
·
Feedback loop.
Page No 87
·
Hardly a day passes without
some Japanese/Korean company coming out with new model of voice-activated
robots & toys, who seem to “learn”
Page No 91
·
By this token, I suppose, one
day neural network software would be able to study / examine” keyword in a resume
than compare with keyword specified by client and okay or reject a resume.
·
Collection of good & bad
resumes?
·
Brand new resumes arriving
daily in our office.
·
To-day our consultants are
spending 2/3 hours daily “reading a set of resumes, to decide which are good
& which are bad. Their [biological]
·
Brain is “processing “resumes
& giving “weightages”
Page No 92
·
In our case when we tell the
network “this resume is good it will look-up & record & remember what
KEYWORDS it contained [i.e. what makes it “good].
·
We could also give the neural
network on puts such as how often this expansive gets short listed or gets an
intewiew calls.
·
Cyril wrote such software in
1995/96 & ran it for several on his computer. The software gradually
learned to identify “address “in only resume.04/05/2002
Page No 93
·
Con text cartridge “software
[now a part of ORACLE 8i database is supported to be working on neural network.
·
Does “resumix software use
this we can treat in coming resumes? As product”
·
In our case, we would have to
“train “our neural network software, on
·
Thousands of resumes, which
our, consultants [human experts] have, earlier, categorized as “GOOD “or “BAD
“depending upon, the [highlighted] KEYWORDS, contained in resume.
Page No 94
·
Resumes, too, contain lot of
“i.e.superfluos/unrated words.
Page No 100
·
Maya Vad OF ancient India
[illusionism] 05/05/2002 Anokhce’s 16th birthday.
·
Somewhat like magician kill’s
[the wet of illusion]
Page No 101
·
Perhaps, we do “sense “but
jail to “interpret”
Page No 112
·
Like dot –com companies which
suddenly cropped up during 1994/99
·
And then died equally
suddenly? But not trying in a “state of chaos”.
Page No 124
·
Some 10/15 years back an
India scientist at AT&AT, found an algorithm for talcum scheduling.KARMARKAR
‘S’ ALGORITHM
Page No 125
·
In one aquarium. A very small
electrical current in this reversed, making a school of fish to reverse
direction.
·
Like people sitting in a
small group-in an otherwise empty bus, but if is otherwise “full, a person
tries to find farthest empty seat!
Page No 129
·
We will be able to build net
when we list 300,000 keywords in 10,000 resumes belonging to 100,indrstries/ 50
functions / a designation-level/10 educational levels & co relate [give
weightage]with up got appointed who got short-listed &how many times
05/05/2002
Page No 140
·
If we treat each resume [-and
job a vat] as an “organism “and if we study thousands of resumes we will
discover elefionite “patterns” of. Keywords, in each”type”of resume. Keywords
are DNA to resumes.
Page No 141
·
May be, we too will discover
that no two resumes have precisely the /identical set of keywords although
these may be very very similar.
·
To test this by hypotheses,
we need millions of resumes!
Page No 144
·
Is it likely? That we will
find same / similar “keywords” [DNA] in, say job destination or Experience
parts of resumes of executives form similar”
·
Industry & function”?
·
Each unique combination of “ind.”&
funs is like a plant “species we will uncover the similarly of keyword in each
such species.
·
We will find these obvious
patterns pattern in resumes too!
·
Page No will this be somewhat
like saying: “give me a person’s INDUSTEY & FUNCTION, and I can construct
his resume.
Page No 146
·
We will need to establish our
own “rules by figuring out the co-relation
·
Between Keywords [found] on
one hand and the age /sex/eduqnali/Ind./funs/Designation etc. On other hand.
·
Obviously these rules are
built the resumes. We need to figure these out.
Page No 150
·
Resumes are like melodies
structures [of paragraphs & their arrangement/order]
·
Repeating elements
[keywords]]
·
Symmetries [phrases &
sentence employed]
Page No 151
·
Pro extending this logic we
may soon be able to” see”muscic-smell “image eats.
Page No 159
·
E.g.: neural net software’s that
mimic the “learning “of a human brain. Page No 164
·
We keep coming back to neural net
software’s that “learn.
Page No 171
·
Let us analyses “keywords
“contained in the resumes of all the candidates that we succeed in placing
[appointing]end see if any particular “pattern/behavior”emerges.in reverse can
we predict “success-rate “for a given patten?11/06/2002
Page No 173
·
Can we boost the
“success-rate of a candidate by planting in his resume, the “missing “keywords?
Page No 186
·
Winning Keyword
patterns=patterns of keywords contained in resumes of “successful
candidates.[success “can be defined as those who got shortlisted or got an
interview –call form client.
·
So during our search it we
find such a pattern in a given resumes we may have found a winner”!
Page No 190
·
How do keywords in thousands
of resumes mutate with higher Edu/age/exp/canwe d’état a pattern? Like GM
foods, can we grow’ genetically Modified Resumes, which have much
better”chance”of getting “shortlist”-and ‘selected’-and “and appointed”?
Page No 192
·
I suppose programs have been
writer which would remove from a resume all proper nouns/verbs/prepositions
etc.-so the only words left are keywords”
·
Once this is done it would be
very easy for a human expect to defeat& highlight “keywords using our
“highlighter “software.
Page No 200
·
What
“Nrefeeional bodies” we are member of what “bosses “we worked under. What
“training courses “we underwent.
·
It we were
to examine, points mentioned on top in the resumes of “successful” executives,
would we see some “pattern” [genes] emerging.14/06/2002
Page No 201
·
What schools /colleges /universities
we studied in what cities we lived in what companies we worked for.
·
But who is a “successful’-[a
well-developed] executive? Shall we arbitrarily define an executive as being
“successful “if he became-a-vice-president “in 15 year from date of graduation
a gen.mgr’in 10 years.
Page No 202
·
Same can be said about our
“keyword project”thre is a long journey ahead.
Page No 207
·
What patterns of “development-stages
“can we expect to find if were to examine the resume of young executive after
every 5 years? Then divide them into successful “&non-successful”
executives.
Page no 217
·
Using “keywords” ”tiles dishes,
we can produce an infinite patterns resumes using a grammar rules! 24/06/2002
·
Page No yesterday on TV
[robots ump 2002 I saw two human looking rollout’s pay football!
Page NO 233
1.
Once we are able to disc to discover the “pattern of keywords key
phrases/key sentences/key pares] followed in resumes, we
2.
May discover their “golden
mathematic. Millions unique resumes bill/butut together from.
3.
Permutation / combination of
a few hundred keywords following
4.
Following single grammatical
rules.
Page No 235
·
Neural net software’s oiled
up on feedback in order to leam/to improve more &more complex.
Page No 243
·
Someday we will a periodic
cube whose 3 sides are industry /function/Designation level 210x120x10] and
populate each cell with profanity of
this cruel with,probality of occurrence of keywords “what will it
predicd.03/07/02
Page No 249
·
Already out in market. We are
into this.
·
Tower of babel language
translator brat-in.
Page No 520
·
Vedas on puranas have described
the capabilities of Anima [to become smaller than an ANU-or atom grime a [to
became bigger than a mountain a GIRI].
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