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While a lot of inaccurate information on AI circulates, it’s important to correct frequent misconceptions. Many might be concerned about being eventually replaced by a robot, or imagine a scene from the movie Terminator when thinking about AI. It’s no longer about “AI is coming”. AI is here and these are the 5 things you should actually worry about.

1 — You’re at Greater Risk of Losing your Job Without AI

We hear too often people talk about losing their jobs to AI. This is not what people should be concerned about. The reality is technology (not just AI) has and will continue to redefine what tasks should be completed by humans…


Understand the key differences between supervised and unsupervised machine learning and discover game changing applications for HR

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Introduction

The goal of unsupervised learning is to build a model that can create its own representation of the world to then generate imaginative content. It allows us to uncover hidden correlation, trends and anomalies in data, not perceptible to humans. Unsupervised learning has been used in many instances to uncover game changing findings across multiple fields including medicine, science, engineering, etc. In business, unsupervised learning has been used in many instances to identify untapped market opportunities and lead to new company segments or venture creation. …


See how you can easily develop a chatbot to support client exchanges and gain competitive insights

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Eliza was the first chatbot created in 1994 by Joseph Weizenbaum from MIT. Originally, chatbots were built to catch on to key words in sentences and had pre-programed responses, creating the illusion of having human-like conversation. For example, at the time, if you told Eliza, “My father is a great tennis player,” Eliza would probably pick up on “father” or maybe “tennis” and respond: “Tell me more about your family.” or “Who are your favorite tennis players?”. Today, chatbots have significantly evolved thanks to neural networks, deep learning and many freely available natural language processing frameworks, allowing almost anybody to…


This machine learning model predicted a dangerous outcome nobody saw coming in 9 steps

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Following my last article that discussed the applications of Supervised Learning for Strategic Workforce Planning, I was asked to share a concrete example and illustrate how a machine learning model works.

This article offers a step-by-step guide (with code) to implement a simple supervised learning model to predict attrition. You will see the results at the end are surprising and will create a sense of urgency for the company to quickly respond to predicted cases for attrition.

The model was programed in Python. Python is one of the most accessible programming languages available. It can be used for multiple purposes…


How supervised learning can help predict talent needs and prescribe the appropriate course of action

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Recruiting and retaining top talent continues to be increasingly competitive, despite the shift of equilibrium in talent supply and demand caused by the pandemic. Organizations are deploying important efforts in talent acquisition and management, but are they investing in the right areas? As we approach the end of confinement measures and see the light with the vaccine roll out, some expect talent volatility will only continue to rise as many employees have been sticking to their jobs through the pandemic, waiting for markets to stabilize before making their move.

Machine learning offers many use case applications that enable employers to…


See how you can enhance candidate experience, detect diversity & inclusion issues, automate steps and improve the overall effectiveness of your recruitment process through natural language processing

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Following the publication of my article The Potential for Machine Learning in HR: 3 Business Application Examples, a few reached out with questions on the different machine learning techniques cited. In response, I’d like to take a deeper dive in one of the mentioned techniques: natural language processing (NLP) and explain how it can be leveraged in a company’s HR recruitment process. I will start by explaining what NLP is, why it is relevant in the recruitment process and walk you through how companies can use it. …


How machine learning can unlock the potential of your workforce

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Business applications for machine learning are becoming increasingly integrated in organizations, and not just in the commonly known giants such as Google, Amazon, and Tesla. AI and machine learning are increasingly reaching midsize and even small companies, especially in operations/logistics, business development and finance/accounting domains. The speed at which companies are onboarding data science solutions is only accelerating, except for one field commonly left behind: HR.

HR is commonly perceived as a less “quantitative” business function, yet it’s probably the one holding the greatest amount of data (both structured and unstructured). Why?

There are definitely multiple reasons; the top three…

Isabelle Bittar, MBA, PMP, CRHA

Isabelle is a Montreal-based business consultant specialized in HR and data science.

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