Yesterday , I attended a seminar on CHAT BOTS , conducted by Beerud Sheth ( www.GupShup.io )
Leaving aside the technical part ( which was
beyond me ) , I realized that the time has come for some of my ideas , which I
jotted down , way back in Jan 2007 , in the margins of the book :
ARTIFICIAL
INTELLIGENCE APPLICATION PROGRAMMING ( M. Tim Jones )
I believe , someday soon , either some start up in the Online Recruitment space or
one of the major Job
Portal ( Naukri / MonsterIndia / TimesJobs / LinkedIn ), will implement these ideas
I have no doubt , Beerud ( beerud@gupshup.io ) would be able to
create appropriate BOTs
for each of these ideas ( and , even make these BOTs talk to each other ) , for a few thousand
dollars !
I wonder why people are worrying about ROBOTS replacing humans
in distant future
To me , the issue is : How many human jobs, will BOTS take away in near future ?
For Recruiters / Headhunters , that answer lies on Pages 142 / 300 /388 !
Here are those notes scribbled
in the margins of the book :
Page 3
·
We are planning that “ weights “ of each keyword gets automatically
adjusted / updated dynamically , with arrival of each new resume in our
database . We only provide the “ seeds “ – the starting weights . Inputs are “
Keywords “ / Outputs are “ Raw Scores “
Page 4
·
This would be of interest to us . Job Advts posted ( on any job
site ) are nothing but expressions of a Company’s “ staffing requirements “ .
If we have 5,000 job advts for a given Company ( over past 3 years ), we should
be able to find a time-series / trend and should be able to predict its “
future “ requirements . Simplest is extrapolation of past trends !
Page 47
·
We have talked of a concept like Rubik’s Cube , to place ( ie;
bring together ) on each “ Face “ , keywords belonging to a given “ Skill “ or “
Function “
·
Goal = Getting all “ same coloured “ keywords ( squares ) onto the
same face of the cube
·
Path = Time taken ( shortest )
·
Each “ face “ of our Virtual Rubik Cube , would have same colour ,
if someone succeeds in virtually “ rotating “ the layers along the 2 AXIS of
freedom , until all squares are of SAME COLOUR – meaning that all squares contain Keywords
belonging to SAME SKILL
·
We can “ time “ the game , to see which visitor ( WHO ) , manages
to get ALL keywords ( squares ) on the SAME face in the shortest possible time
; then give him recognition / credit by publishing his name on our web site
·
This could be a lot of FUN – and could, possibly draw a lot of
young kids to our web site !
There will be no login / registration for playing
the game . Just walk in and play . Then upload the COMPLETED / SUCCESSFUL image
( with time taken ) and email to all friends to prove your cleverness ! [ 06 JAN 2007 ]
Page 69
·
Particles = Keywords
·
Space = Text
Resume
·
Best found solution = Resume with highest score
·
Better one = Addition
/ deletion of “ which “ & “ how many
“ keywords , would result in a Raw Score better than the “ best found “
solution / resume ?
Page 70
·
What keywords ( elements ) are always found “ together “ ?
Page 72
·
Is one resume , a “ solution
space “ in which particles ( ie; Keywords ) are swarming around ?
Page 91
·
Set of data = Master set of 5,000 keywords
·
Smaller set = Function-wise sets of 100 keywords
·
Classification = “ Skills “ & “ Functions “ are
Classifications
Page 92
·
Read my notes on “ Expert Systems “ / eg:
finding keywords which have NEVER occurred before in ANY resume .
How will software “ Know “ that it is a NEW
keyword ?
– and then know “ to which new Skill / Function “ does it belong to ?
·
Clusters are , Functions /
Skills / Cities / Designation Levels / Edu Qualifications / Experience etc
·
Customer Set : Obviously , WIPRO / Infosys / TCS / Satyam
, belong to a well-defined “ customer set “
. They all have “ Common Attributes “ , ie :
similar / identical job advts posted / similar / identical , resumes searched
Page 93
·
Each “ sub set “ can be ,
# same SKILL / same FUNCTION / same CITY / same
EDU etc
·
Corporates are “ purchasing “
resumes from job portals and we will have exhaustive data on their “ purchases “
, ie : Number / Type of resumes transferred to folders / opened / viewed
Page 98
·
We are planning a “ Recommendation System “ – which would recommend job
advts to jobseekers and resumes to Recruiters
·
If Wipro HR manager
shortlisted / interviewed such and such candidates , same could also be of
interest to Infosys HR manager
Page 113
·
A learning component to identify new rules = I have written some rules
Page 118
·
This is what we are
interested in. Starting with ( may be ) 50 rules on recruitment ( getting
selected / getting appointed ) , we want the algorithm to discover 500 “ NEW RULES “
Page 142
·
Can we develop an “ AGENT “ ( ant ) for each corporate subscriber’s each job advt.,
which travels to “ distant places “ ( different job sites ) , and safely bring
back “ food “ ( resumes ) to its own “ nest “ ( folder ) ?
·
Millions of “ ants “ ( software agents ) , let loose on Web
Network, each programmed to find a “ specific “ type of resume – then , when it
finds , passing on this data to the next ( adjoining ant / agent ), ie;
communicating
Then becoming free again to search for next resume for which it is
programmed
A resume may pass from one agent to another ( may be several
hundred / thousand ) , till it finally gets received by the ANT / AGENT , who is “
programmed “ to find it in the first place !
So , it is not necessary that an AGENT ANT finds only that resume for which it is
programmed , as long as it finds any ONE resume , from ANYWHERE & communicates ( passes onto the next / adjacent
Ant )
Like Packet Switched Network ?
All packets getting assembled by the DESTINATION ANT
Parameters stored getting matched with arriving resume’s parameters
Page 143
·
In GURU-JEM ( improved
version of HARVESTER ) , we are trying to find web records containing Company
Name / Designation,
But from a
single source ( viz: Google )
Our software
agent travels to Google , finds and brings back the results ( food )
But , could
we possibly design / devise , millions of software agents , each programmed to
roam the Web ( or , predetermined URLs ) and find,
# One of the thousands of “ designations “ , OR
# One of the thousands of “ Company Names , OR
# One of the lakhs of “ Executive Names “ , OR
# Any combination of the above ,
Then bring
back the results ?
·
Since most job sites permit
access to “ Job Advt Database “ to any
visitor without need for a Password, it may be much easier for ANT AGENTS to
roam job sites ( like Search Engine Spiders ? ) and bring home suitable Job
Advts ( what we do in a limited manner )
Page 165
·
Game = Online Recruitment / Searching for Employers
/ Searching for Candidates
·
Characters = Recruiters
/ Jobseekers
·
Agents = Job
Advts / Resumes
·
Environment = Virtual Job Fair / Virtual Employment Portal
Page 207
·
I believe Cyril used such an algorithm, to read a plain text resume
. The software did manage to accurately “ find “ the “ Address “ ( from
anywhere in the resume ), after about 72 hours of continuous “ exploring /
processing / learning “
This was nearly 8 years ago . I am sure by now , far more powerful
neural-net “ Shells “ can be freely downloaded , which would “ parse “ a plain
text resume to find accurately, ALL the “ fields / values “ which we need to
create a “ Structured Database “
It is simply a question of experimenting and now , we have no
shortage of hardware [ 07 JAN 2007
]
Page 230
·
Someday ( when we have a
million resumes on our web site ), we will have many sub-sets of candidates who
started their careers ( first job ) ,
# at the same AGE , and with
# the same EDU QUALIFICATION
Question is : How did their careers
“ Evolve “ ?
What Salary / Designation , did each of them reach / achieve , after 5
years / 10
years / 15 years ?
Was there a “ pattern “ ?
Did their “ career paths “ run “ parallel “ or did they diverge ?
What other “ factors “ ( eg: Employer Companies ), influenced such
divergence ?
Did some “ plateau out “ after a time whereas others
continued to climb the
Designation / Salary ladder
? (
Graph drawn to illustrate the
concept )
Page 300
·
With an ever-expanding set of “ Rules “ , it will be possible for a
“ Rule-based “ system to detect fake / fudged resumes , where a candidate is
telling a lie re: some facts
·
I have listed several such rules . See my notes in “ Expert System “
. Also my handwritten notes in the margin of book , “ EXPERT SYSTEMS “
·
In each resume , each & every field-value is a “ FACT “ .
Therefore , facts are , Age / Experience / Designation Level / Edu Quali / City
/ “ Skill Keywords “ found / Salary / Employer Name etc
·
One can start with simple rules such as
,
# Experience ( years )
cannot exceed Age
# Age cannot be less than 16
# Edu Quali cannot be less
than 10th Std / SSC
# MD ( Managing Director )
cannot be less than GM ( General Manager )
# Post-Grad cannot be less
than Graduate
# Salary cannot be less than
Rs 1000 pm
Page 335
·
In developing Function Profile grphs, we are simply depending on “
presence “ or “ absence “ of any given Keyword ( Binary Status ) .
Then
assigning a “ weightage “ ( of a given amount of weightage of ZERO ) ,
depending upon present or absent
But in real world, it may turn out that the Candidate with a lower
Raw Score ( because of many “ absent “ keywords ) , turns out to be a better
choice ( higher interview-score in Interactive Interview Tool ) , than a
candidate with a higher Raw Score ( where most Keywords were present )
In such a scenario , should we try out “ Fuzzy Logic Algorithm “?
Page 372
·
Author’s note : Consider an email program that monitors the behaviour
of the user. When e-mail message arrives, the model observes what the user does
with the e-mail message and uses this information to learn how to automatically
deal with subsequent e mail message
My comments
: Beysian spam filter ? – based on a starting
database of “ unwanted “ keywords in the e mail message
Page 376
·
We must experiment with Kurzweil’s Paraphrasing Software
·
Could Kurzweil’s “ Paraphrasing Software “ be based on this ? See
web site of Kurzweil .
Using this can we “ re-write “ a resume ( lilke creating
a step brother ), from a given “ Sample Resume “ ?
If such “ paraphrasing / re-writing “ of resumes can be done online
( automatically ) on our web site , then
we could add one more element of FUN – even if the re-written resume contains
some absurd text ! In fact , such “ absurdity “ may lend an element of FUN
! [
07 JAN
2007
]
Page 385
·
If we succeed in paraphrasing / re-writing a text resume online, we
could also generate / develop a “ Subliminal / Subconscious “ function profile
graph ! ( - since keywords would have got changed ) . This could be fantastic !
Page 388
·
Eg :
Agents of Jobseekers negotiating Salary / Terms etc with Agents of Recruiters
·
Virtual JobFair will be / can be a “ Negotiating
Platform “ for
,
# Buyers’ Agents ( Recruiters )
# Sellers’
Agents ( Jobseekers )
Page 389
·
Only yesterday we discussed that in “ Post Jobs “ form ,recruiter
will add,
# Put in my folder, all future/incoming resumes
having percentile score of > xyz
Now he has created an AGENT which checks
incoming resumes daily & puts into folder “ Resumes of Interest “ – also an
email will go out to the recruiter concerned ( alert )
Page 440
·
A few weeks back , there was a report of a mechanical spider-legged
robot , which learned on its own to change its “ gait “ when one of the legs
was broken !
24 June
2017
www.hemenparekh.in
/ blogs
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