Time to Reinvent

Have you ever imagined how the self-driven driverless car is a reality? Welcome to the world of machine learning.

Machine learning algorithms make it possible for cars to act upon data collected from cameras and other sensors fitted in the car. Machine learning allows for meaningful interpretations of that data and enables decisive actions. This even allows cars to learn how to perform these tasks as good as or even better than humans.

Machine learning is bound to impact all areas where the computers can learn the tasks that a human can.

Machine learning has started impacting different areas of our lives. Needless to say, Machine Learning will soon influence the way Talent is acquired. 

There are several areas of Artificial Intelligence that will impact Talent Acquisition. We explore the area of Machine Learning, as an example of the possibilities of AI, to understand the kind of imminent transformation that is about to happen in Talent acquisition. 

Machine Learning

Machine learning algorithms build a model based on sample data, known as “training data”, in order to make predictions or decisions without being explicitly programmed to do so. Machine learning provides computers with the ability to learn and improve from experience.

The discipline of machine learning employs various approaches to teach computers to accomplish tasks where no fully satisfactory algorithm is available.

The primary aim is to allow the computers to learn automatically without human intervention or assistance and adjust actions accordingly.

1. Supervised learning

Supervised learning algorithms build a mathematical model of a set of data that contains both the inputs and the desired outputs. The data is known as “training data” and they are well “labelled” too. Each training example has one or more inputs and the desired output, also known as a supervisory signal. 

Supervised learning algorithms learn a function that can be used to predict the output associated with new inputs. An optimal function will allow the algorithm to correctly determine the output for inputs that were not a part of the training data.

Screening a resume is a classic case of supervised learning. It is possible to teach the computer how to read a resume.

Machine learning can not only help you screen resumes for keywords but for meaning.

Through supervised learning, the computer can be taught to check the suitability of a resume for a position. It is possible to teach the computer to make sense of a resume like a human recruiter. How would a human recruiter make sense of the educational qualification, the work experience, gaps in resume, the number of job changes and whatever is required to analyze a resume?

To teach the computer effectively, the mapping of each of the attributes of the resume to the relevant interpretation is the key. Importantly, a large number of resumes have to be mapped. The more the data you supply to a machine learning system the better it performs.

An algorithm that improves the accuracy of its outputs or predictions over time is said to have learned to perform that task.

2. Unsupervised learning

Unsupervised learning algorithms take a set of data that contains only inputs, and finds structure in the data. The algorithms, therefore, learn from test data that has not been labeled, classified or categorized. Instead of responding to feedback, unsupervised learning algorithms identify commonalities in the data and react based on the presence or absence of such commonalities in each new piece of data.

Resume parsing is an application of Unsupervised learning that can leverage the abundance of large unlabeled datasets. Unsupervised learning enables the exploration of unknown and raw data. It can further be leveraged for recognizing patterns in large data sets.

Accurate and efficient resume parsing significantly contributes to the effective implementation of Applicant tracking Systems. 

An effective parser contributes to the availability of clean and complete data

A culture of data alone can drive technological change

3. Reinforcement Learning

Reinforcement learning (RL) is an area of machine learning concerned with how intelligent computer programs ought to take actions in an environment in order to maximize the notion of cumulative reward.

This is best illustrated through an example. Imagine the self-driven driverless cars driving on the streets, come up with scenarios that are completely unpredictable. The car is finding the right balance between exploration (of uncharted territory) and exploitation (of current knowledge). 

In machine learning, the environment is typically represented as a Markov decision process (MDP). Many reinforcement learning algorithms use dynamic programming techniques. Reinforcement learning algorithms do not assume knowledge of an exact mathematical model of the MDP, and are used when exact models are infeasible.

The computer employs trial and error to come up with a solution to the problem. To get the machine to do what the programmer wants, the artificial intelligence gets either rewards or penalties for the actions it performs. Its goal is to maximize the total reward.

Although the designer sets the reward policy–that is, the rules of the game–he gives the model no hints or suggestions for how to solve the game. It’s up to the model to figure out how to perform the task to maximize the reward, starting from totally random trials and finishing with sophisticated tactics and superhuman skills. 

By leveraging the power of search and many trials, reinforcement learning is currently the most effective way to hint machine’s creativity. In contrast to human beings, artificial intelligence can gather experience from thousands of parallel gameplays if a reinforcement learning algorithm is run on a sufficiently powerful computer infrastructure.

Chatbots is a classic case of reinforcement learning. Chatbots in addition is driven by another branch of AI, Natural language processing.

A chatbot is a software application used to conduct an on-line chat conversation via text or text-to-speech, in lieu of providing direct contact with a live human agent.

Designed to convincingly simulate the way a human would behave as a conversational partner, chatbot systems typically require continuous tuning and testing. Chatbots is AI driven to enable a conversation with a human in a natural language.

Chatbots has simplified the interaction between human and computers. 

Chatbots can help recruiters save time by performing some of the time-consuming tasks, like responding to simple questions, scheduling interviews and gathering basic information from applicants.

How is a Chabot driven?

Like the self-driven driverless car, the Chatbot is finding the right balance between exploration (of uncharted territory) and exploitation (of current knowledge). 

Remember the old adage, once bitten twice shy.

Human actions are programmed based on past experiences.

After each interaction, they decide whether to repeat an action or not based on the consequences of that action in the past.

To train a dialogue system with reinforcement learning, the chatbot interacts with the end-users and observes the results of its actions. It receives each time a reward which can be positive or negative. The chatbot becomes increasingly efficient  with experience and time.

Re-Imagining Talent Acquisition

It is time for the recruiters and hiring organization to take stock of how they are going to be impacted. 

Re-imagine Talent Acquisition, when powers of Machine learning combine with other areas of AI, Analytics, social media and the power of computing available.

It is about time the recruitment community reinvents itself.

References:

The definition “without being explicitly programmed” is often attributed to Arthur Samuel, who coined the term “machine learning” in 1959, but the phrase is not found verbatim in this publication, and may be a paraphrase that appeared later. Confer “Paraphrasing Arthur Samuel (1959), the question is: How can computers learn to solve problems without being explicitly programmed?” in Koza, John R.; Bennett, Forrest H.; Andre, David; Keane, Martin A. (1996). Automated Design of Both the Topology and Sizing of Analog Electrical Circuits Using Genetic Programming. Artificial Intelligence in Design ’96. Springer, Dordrecht. pp. 151–170. doi:10.1007/978-94-009-0279-4_9.

Russell, Stuart J.; Norvig, Peter (2010). Artificial Intelligence: A Modern Approach (Third ed.). Prentice Hall. ISBN 9780136042594.

 Mohri, Mehryar; Rostamizadeh, Afshin; Talwalkar, Ameet (2012). Foundations of Machine Learning. The MIT Press. ISBN 9780262018258.

 Mitchell, T. (1997). Machine Learning. McGraw Hill. p. 2. ISBN 978-0-07-042807-2.

 Hu, J.; Niu, H.; Carrasco, J.; Lennox, B.; Arvin, F. (2020). “Voronoi-Based Multi-Robot Autonomous Exploration in Unknown Environments via Deep Reinforcement Learning”. IEEE Transactions on Vehicular Technology. 69 (12): 14413–14423.

 Kaelbling, Leslie P.; Littman, Michael L.; Moore, Andrew W. (1996). “Reinforcement Learning: A Survey”. Journal of Artificial Intelligence Research. 4: 237–285. arXiv:cs/9605103. doi:10.1613/jair.301. S2CID 1708582. Archived from the original on 2001-11-20.

 van Otterlo, M.; Wiering, M. (2012). Reinforcement learning and markov decision processes. Reinforcement Learning. Adaptation, Learning, and Optimization. 12. pp. 3–42. doi:10.1007/978-3-642-27645-3_1. ISBN 978-3-642-27644-6.

https://deepsense.ai/what-is-reinforcement-learning-the-complete-guide/

https://medium.com/opla/chatbots-approaches-battle-part-1-sequence-to-sequence-vs-reinforcement-learning-731d3db75e0e