Rohit ,
As discussed over phone , we look
forward to your visit to our office on 2nd May , at 9 am for demo of
your product
In the meantime , following will
interest you
Warm Regards,
Hemen Parekh
+91 98675 50808
{ My Comments in RED }
When artificial intelligence evaluates chess champions
April
25, 2017 by Alexiane Agullo
The ELO system, which
most chess federations use today, ranks players by the results of their games.
{ Headhunting is a kind of “
game “ in which several “ players ( candidates ) “ compete against each other ,
in order to “ Win “ ( get selected= result ) for a given position }
Although simple and
efficient, it overlooks relevant criteria such as the quality of the moves players actually make.
{ Moves = Job Changes during
the course of the career / some GOOD and some POOR }
To overcome these limitations,
Jean-Marc Alliot of the Institut de recherche en informatique de Toulouse (IRIT
- CNRS/INP Toulouse/Université Toulouse Paul Sabatier/Université Toulouse Jean
Jaurès/Université Toulouse Capitole) demonstrates a new system, published on 24
April 2017 in the International Computer Games Association Journal.
Since the 1970s, the
system designed by the Hungarian, Arpad Elo, has been ranking chess players according
to the result of their games.
{ Results = In how many GAMES ( interviews for different positions ) , did
a candidate manage to cross each step of “ Elimination “ , to reach the final
interview ? or “ get selected by different companies for appointment }
The best players have the highest
ranking, and the difference in ELO points between two players predicts the probability of either player winning a given game.
{ Difference in the NUMBER of “ Elimination Stages “ that each candidate
managed to cross ? }
If players perform better or worse
than predicted, they either gain or lose points accordingly.
This method does not take into
account the quality of the moves played during a game and
is therefore unable to
reliably rank players who
have played at different periods.
Jean-Marc Alliot suggests a direct ranking of players based on the quality of their actual moves.
His system computes the difference between the
move actually played and the move that would have been selected by the best
chess program to date, Stockfish.
{ “ What / which” are those
BEST MOVES that a executive could have , ideally made , to reach / qualify for
a GIVEN POSITION ? }
Running on the OSIRIM
supercomputer, this program executes almost perfect moves.
Starting with those of
Wilhelm Steinitz (1836-1900), all 26,000 games played since then by
chess world champions have been processed in order to create a probabilistic model for each player.
{ Among the 1275 positions
that we have filled over the past 6 years , each IDENTICAL position , may be
only 20 / 30 in number . But , if we ignore that , we have put through the
process ( all stages ) , some 25,000 executives for these 1275 posts . And if there
are ( say ) 5 stages of elimination for each post , on an average , these 25 ,
000 executives may have gone through ( say ) 3 stages . That would make for
25000*3 = 75,000 INSTANCES of eliminations }
For each position, the
model estimates the probability
of making a mistake,
and the magnitude of the mistake.
These models can then be
used to compute the win/draw/lose
probability for any given match
between two players.
The predictions have
proven not only to be extremely close to the actual results when players have played
concrete games against one another, they also fare better than those based on
ELO scores.
The results demonstrate that the
level of chess players
has been steadily increasing. The current world champion, Magnus Carlsen, tops
the list, while Bobby Fischer is third.
Under current conditions, this new
ranking method cannot immediately replace the ELO system, which is easier to
set up and implement. However, increases in computing power will make it
possible to extend the new method to an ever-growing pool of players in the
near future.
From: Rohit Verma
[mailto:rohit.v@ipredictt.com]
Sent: 26 April 2017 18:43
To: Hemen Parekh
Cc: Nirmit Parekh; Mitchelle; Prashant Sharma; Manoj Pandey
Subject: Re: NICE TO HAVE MET YOU
Sure,
Proposing either 2nd or the 3rd
May, 9 -11 AM
Let me know which date works
Thanks,
Rohit
Rohit,
I had a meeting with my colleagues, this
morning
Let me know your convenience next week
when you could visit our office for a demo of your current products and for
discussion on your proposed “ elimination algorithm “
Warm Regards,
Hemen Parekh
+91 98675 50808
Thanks for the update.
Here are some platform websites and documents as
prereads/precursor to the meeting.
While we will be discussing much in detail on our data
science based recruitment intelligence platform for Enterprise (aka B2B
platform),, we are seeing great adoption of our B2C platform which you may
visit to experience.
Access details
Password – ipredictt
(or you may create new account by submitting details)
Access details
Password – ipredictt
(or you may request for a new account by submitting
details)
Also attached please find presentation for reference
Look forward to meeting.
I am reaching Mumbai on 27th, so anytime
after that.
Regards
Rohit
Rohit,
I plan to discuss this with concerned
colleagues , on Tuesday
By end of the week or early next week ,
we can look at your demo ( current product ) and finalize the requirements for
the “ Elimination Algorithm “
Warm Regards,
Hemen Parekh
+91 98675 50808
Nirmit / Mitchelle / Prashant :
From: Rohit Verma
[mailto:rohit.v@ipredictt.com]
Sent: 23 April 2017 09:38
To: Hemen Parekh
Subject: Re: NICE TO HAVE MET YOU
Sir, Just wanted to know if you were able to have an
internal discussion around data feed for creating “elimination algorithm”. It
would be great if we can get started anytime soon, if approvals are in place.
Many thanks
Rohit
Thanks Mr Parekh for your time.
I am deeply enlightened to meet you. I am also very
enthused to be aligned to your prophecies in some ways.
Thank you for the codes. We will review the same.
It would be great if you provide us opportunity to
work on the “elimination algorithm”, and we will certainly build a Free of cost
use case for P3 Consultants.
Look forward,
Regards,
Rohit
Rohit,
It was nice to meet you this morning
Sachin is forwarding to you , source codes for :
Resume Rater ( tool )
When he finds , he will send you source codes for,
“ My Jobs / Resume Blaster “ mobile apps
Find below the link to my blog :
With Regards,
hemen
parekh
(
M ) +91 - 98,67,55,08,08
