Of Interest to Recruiters / Job Portals

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

     { On Line  Jobs Fair  / 

 http://hcpreports.blogspot.com/2016/02/online-jobs-fair.html }

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 10^th 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 )

{ http://hcpreports.blogspot.com/2016/02/online-jobs-fair.html }

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            

Is it NOW or NEVER ?

That is the question that Shri Nitin Gadkriji must be asking himself , after reading the following news report which appeared in New York Times , yesterday

Or , would like to ask Elon Musk , in connection with his tweets re setting up an electric car plant in India

But Shri Gadkariji holds a TRUMP CARD ( pun intended ! )

He can tell Elon :

“ Look , to set up a plant in China , you must have a Chinese Company as your JV partner and you cannot hold more than 50 % equity in the JV

No such conditions in India  !

On top of that , we will match all the other concessions / amenities that the Chinese offer you

And , we will allow to participate in the tender for 3 million EV for the Central / State governments ( to replace the existing petrol / diesel fleet , by 2019 ) , as long as 50 % of value addition takes place in India , under Phased Manufacturing Program “

--------------------------------------------------------

Shri Gadkariji :

In your dream to electrify India’s 200 MILLION polluting vehicles, by 2030 , this could well be the “ TIPPING  POINT 

“

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 Tesla Motors is in discussions to establish a factory in Shanghai, its first in China, a move that could bolster its efforts in one of its major markets even as it further lifts China’s position as a builder of electric cars.

In a statement on Thursday, Tesla said it needed to set up more overseas factories to make cars that customers could afford. Such a strategy is a must in China, which charges steep tariffs for imported cars.

“Tesla is working with the Shanghai Municipal Government to explore the possibility of establishing a manufacturing facility in the region to serve the Chinese market,” a company spokesman said. “Tesla is deeply committed to the Chinese market, and we continue to evaluate potential manufacturing sites around the globe to serve the local markets.”

“While we expect most of our production to remain in the U.S., we do need to establish local factories to ensure affordability for the markets they serve,” the spokesman said.

China accounted for about 15 percent of Tesla’s revenue last year, nearly double the percentage it contributed in 2015.

Shanghai city officials did not respond to requests for comment. Bloomberg News reported earlier that Tesla and Shanghai had signed a preliminary agreement.

Tesla’s negotiations do not guarantee that a plant will be built. Under Chinese law, such a project would require Tesla to find a Chinese joint-venture partner. While China is full of Chevrolets, Fords and Volkswagens, most are made in factories jointly owned by a foreign automaker and a local company.

The City of Shanghai controls the SAIC Motor Corporation, one of China’s largest automakers and a partner for General Motors and Volkswagen. It was not clear whether Tesla’s negotiations with the city government would steer the company to negotiate with SAIC. Calls to the Chinese automaker were not returned.

Tesla could get around the joint-venture requirement by building a wholly owned factory in a foreign trade zone in China. But it would still have to pay the 25 percent import duty for cars sold in China, as the factory would be treated as outside China for trade purposes.

Further complicating matters, China recently announced that it would issue no more business licenses to make automobiles, including electric cars. Tesla does not have a license, although it could form an alliance with a company that has one.

These are formidable obstacles. But some in the Chinese auto industry say that the economics of producing in China — a low-cost supply chain, especially for electric cars, as well as the ability to bypass the import tariffs — make the proposition attractive.

For China, a domestic Tesla factory could represent a big symbolic victory. Spurred by incessant pollution and increasing dependence on foreign oil, China for the last several years has pushed to be a leader in electric car development.

That has raised concern in Western countries. In March, the European Union Chamber of Commerce in China complained that Chinese law requires manufacturers who set up shop in China to transfer crucial technology to their Chinese partners.

The complaint coincides with a broader debate over China’s plan — called Made in China 2025 — to become self-sufficient in some technology industries. That plan has led to concerns that China will nurture and subsidize domestic competitors to Western companies.

Still, it is not clear what arrangements Tesla would make in China. The battery is central to electric car technology. Tesla has already invested heavily in its huge, $5 billion Nevada factory, called the Gigafactory, to produce batteries.

23  June  2017

www.hemenparekh.in / blogs