Shyamsunder Panchavati

Shyamsunder Panchavati
Linkedin now a follower of Shyam on twitter

Friday, June 16, 2017

Job posting frauds, jobseeker woes, The database sale????

google-site-verification: googled8cb12c78271c415.html H_abxalAdC9Cg0xe3NtXHQ6qbrc  



IF YOU ARE AN HONEST RECRUITER WITH SINCERITY OF PURPOSE AND HONEST TO YOUR PROFESSION, THIS ARTICLE IS NOT ABOUT YOU. BUT I KNOW EVEN YOUWILL AGREE, THAT DISHOENST RECRUITERS ALSO EXIST.







On LinkedIn, updates....



Recruiters, job Ads, the applicant’s complaint that there is no response/acknowledgement, or lies from them, or not lifting the phone.



Why this sudden spurt in job advertisements???




What happens with the Data collected?

Motives, Seen/Unseen, Ulterior/Bonafide.

There are many ways your profile is used.

SOMETIMES IT IS EVEN USED TO GET YOU A JOB.....


The main reason for this spurt is the value for the database. A recruiter having large volume gets good value from the organizations. And the one having high value data base is adored by the organizations.


Second angle :  The recruiters take money from a jobseeker and while presenting his profile to the HR also present 25 to 30 (or more) other less attributed ones. These are from the database they collect from sources like LinkedIn updates. The HR obviously gets a large data base to select from. In all fairness the HR selects the best among the available. No guessing needed to know, who gets the job.



Third angle:  

When a recruiter makes a business call on an organization, the first thing the HR asks is the available data base for the available positions in the organization. He proudly presents the huge data base with him and the recruiter is hired as official recruiter for the organization.  He gets a nominal retainer and gets money, whenever he provides candidates for the vacant position. The payment is about one percent or half percent or one month salary, depending on the position and the salary package and has to provide alternative candidates in case people leave earlier than the stipulated time.



Fourth and unfortunate angle:

This data base is sold on demography basis to the Insurance companies, Real Estate companies, mobile companies, Finance companies marketing shares, mutual funds and fixed deposits. Loans, mortgage, and credit cards.  

There are many start-up service providers, super markets, online stores who buy this data, send you a mail offering a promo code that gives a huge discount for a product or service. They make you register you email id, include you legally in their database, and send promo mails regularly.

Fifth angle:

You now have a new breed of professionals who do the profile whetting or profile check for people. These people buy large databases or sometimes these people write to these recruiters for the details including the addresses and phone numbers of the specified people. They search such people on social media platforms like Facebook, LinkedIn, Whats App, Google+ and others. They become your contacts and mail you a fake job requirement and when they get the data, they hand it over to the profile whetting professionals. Sometimes private recovering agencies working on behalf of banks also get the details of the defaulters. It is heard that some people working for the gas companies are selling city wise Adhar card details bank details to these agencies.

How to discern between good and bad.

It is bad........  
When the compensation package looks too good to be true. (It is actually not True)
When you get a job offer through email, where the job requirements are an exact match of your qualifications and experience.
When someone says that you are shortlisted directly for the final interview and asks you to send a CV with detailed address mobile and landline numbers, alternate contact numbers.
When you have not contacted them, still you receive a communication that they have seen you profile somewhere and shortlisted you for an interview.


Then what is safe?

The safest is when you apply

Through organization’s website

Through reputed recruiter’s website

To the jobs directly posted by the organizations

Sometimes, an employee from the concerned department asks you to directly apply to his personal email id. He forwards your profile with his reference to the HR, and the one selected from the list is considered his reference candidate. He gets a commission, once the candidate completes a certain period of time with the organization. Sometimes the purpose to get relevant candidates for the job. Most of times these are genuine honest and safe.  

In conclusion, I would like to state that not all recruiters are fraud. There are good recruiters doing extraordinary work. You need to discern between the good and the bad. Please visit their websites. The good one will definitely have one. See their work, view their client list. See the people behind it their qualifications, work experience, and the type of organizations they have been associated with. And send the profile to the trustworthy ones only. It does not take more than half hour to verify a recruiters. 

If you find this article useful, please share this with your friends’ followers, and fellow jobseekers. You can also help me in improving the article by sending your inputs on this subjects via comments. 



Best Wishes,

Shyam




Saturday, June 3, 2017

AI and me 06-04

google-site-verification: googled8cb12c78271c415.html H_abxalAdC9Cg0xe3NtXHQ6qbrc




Damodar Padhi,

A good and valid viewpoint. I liked it.

But before we dwell too much into what AI can do or not do, We should think of the inverse of that. 

IS ARTIFICIAL INTELLIGENCE MAKING HUMAN BEINGS LESS EMOTIONAL TOWARDS THEIR OWN FAMILY MEMBERS. BEING WITH COMPUTERS, ARE WE BECOMING EMOTIONLESS, FEELINGS LESS, EMPATHY LESS. 

The money present generation is earning can take care of all the physical needs, healthcare needs, They also try to buy solace for family members wherever possible spending money. The present generation does have some quantity of dispensable money.

Will there be a situation in distant future, where machines running on Artificial Intelligence will develop emotional feelings and empathy towards human beings and machines, and Human Being will become machine like, where they fix a scheduled time for pouring love and affection on their ones. And at other times, they may say,

"SORRY YOU ARE OUT OF SCHEDULE, LOVE IS NOT SCHEDULED AT THIS TIME". 

The first development (where machines develop emotions) is good. The second, 

well you people know it better than me.

Friday, June 2, 2017

Understanding Machine Learning 06-02

google-site-verification: googled8cb12c78271c415.html H_abxalAdC9Cg0xe3NtXHQ6qbrc  

Whenever there is evolution in technology and science, There is a natural evolution in the human thought process. This results in transition from one plane to the next one. Every improvement in automation makes the human being more and more dependent on the machines. Since ages human being has learned from machines. 

And now......

The machines have become so intelligent, that they have  started learning and themselves.  Based on their have become more discerning and precision freaks. This process of machines learnng and improving their performance is known as Machine Learning.

The term machine learning refers to the automated detection of meaningful patterns in data. In the past couple of decades it has become a common tool in almost any task that requires information extraction from large data sets. We are surrounded by a machine learning based technology: search engines learn how to bring us the best results (while placing profitable ads), anti-spam software learns to filter our email messages, and credit card transactions are secured by a software that learns how to detect frauds. Digital cameras learn to detect faces and intelligent personal assistance applications on smart-phones learn to recognize voice commands. Cars are equipped with accident prevention systems that are built using machine learning algorithms.

Machine learning is also widely used in scientific applications such as bioinformatics, medicine, and astronomy. One common feature of all of these applications is that, in contrast to more traditional uses of computers, in these cases, due to the complexity of the patterns that need to be detected, a human programmer cannot provide an explicit, finedetailed specification of how such tasks should be executed. Taking example from intelligent beings, many of our skills are acquired or refined through learning from our experience (rather than following explicit instructions given to us). Machine learning tools are concerned with endowing programs with the ability to “learn” and adapt.

Machine learning is one of the fastest growing areas of computerscience, with far-reaching applications.

Roughly speaking, learning is the process of converting experience into expertise or knowledge. The input to a learning algorithm is training data, representing experience, and the output is some expertise, which usually takes the form of another computer program that can perform some task. Seeking a formal-mathematical understanding of this concept, we’ll have to be more explicit about

what we mean by each of the involved terms:

What is the training data our programs will access?


 How can the process of learning be automated?


How can we evaluate the success of such a process (namely, the quality of the output of a learning program)?


When Do We Need Machine Learning?

When do we need machine learning rather than directly program our computers to carry out the task at hand? Two aspects of a given problem may call for the use of programs that learn and improve on the basis of their “experience”: the problem’s complexity and the need for adaptability.





Tasks That Are Too Complex to Program. • Tasks Performed by Animals/Humans: There are numerous tasks that we human beings perform routinely, yet our introspection concerning how we do them is not sufficiently elaborate to extract a well defined program. Examples of such tasks include driving, speech recognition, and image understanding. In all of these tasks, state of the art machine learning programs, programs that “learn from their experience,” achieve quite satisfactory results, once exposed to sufficiently many training examples.


• Tasks beyond Human Capabilities: Another wide family of tasks that benefit from machine learning techniques are related to the analysis of very large and complex data sets: astronomical data, turning medical archives into medical knowledge, weather prediction, analysis of genomic data, Web search engines, and electronic commerce. With more and more available digitally recorded data, it becomes obvious that there are treasures of meaningful information buried in data archives that are way too large and too complex for humans to make sense of. Learning to detect meaningful patterns in large and complex data sets is a promising domain in which the combination of programs that learn with the almost unlimited memory capacity and ever increasing processing speed of computers opens up new horizons.


Adaptivity. One limiting feature of programmed tools is their rigidity – once the program has been written down and installed, it stays unchanged. However, many tasks change over time or from one user to another. Machine learning tools – programs whose behavior adapts to their input data – offer a solution to such issues; they are, by nature, adaptive to changes in the environment they interact with. Typical successful applications of machine learning to such problems include programs that decode handwritten text, where a fixed program can adapt to variations between the handwriting of different users; spam detection programs, adapting automatically to changes in the nature of spam e-mails; and speech recognition programs.  


But this advancement in technology  is not being used by the people. People are still undermining the capabilities of the machines to deliver better results than any human effort. I only hope that people use the machine learning not only for business analysis but also to optimise the costs and  increase productivity by eliminating the redundant processes.

Hope that happens, 

Best Wishes, 

Shyam.