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I observed that how robots are becoming a greater part of our workplace. And that’s not a bad thing, but it does mean that we need to get more comfortable with today’s technology concepts.

That’s why I’m very excited to share today’s interview with you.

Artificial intelligence refers to a collection of subfields thatsolve complex problems associated with human intelligence and/or interacting with the world. These subfields include computer vision, natural language processing, robotics, and machine learning.

Machine learning generally covers methods that build models of complex data.

There are many types of ML approaches, and it is important to choose the right type of solution for the specific business problem at hand. For instance, in the workforce management space, Kronos uses unsupervised ML algorithms that uncover patterns in data to find potential compliance violations. But in the Workforce Dimensions product, Kronos uses a different type of ML, supervised ML regression, for business volume forecasting.

Each of these types of ML has its own idiosyncrasies, but the one thing they all have in common is that they are driven by the data they are given. In the same way a new recruit might need time on the job to learn all the idiosyncrasies of your office, ML algorithms tend to work better as they receive more data.

Why should human resources professionals pay attention to machine learning?

HR professionals have seen the amount of data they oversee proliferate. Data elements around skills, staffing, performance, payroll and many other areas have grown both in complexity and size. With all of this data it is getting harder and harder to simply view your data in a spreadsheet and quickly find the problems, let alone the solutions.

Machine learning holds the promise to unlock the hidden value in all this data that can no longer simply be perused by hand. For instance, machine learning can uncover patterns, such as those that might indicate compliance risk, and surface outliers that otherwise might have flown under the radar.

Similarly, the patterns detected by machine learning can uncover the root causes of key outcomes like turnover or daily business volume and even predict how these key performance indicators (KPIs) will trend in the future. All of these are use cases that HR professional have confronted for decades, but the growth of Big Data means machine learning tools are now capable and even needed to confront these familiar problems.

For organizations that are thinking about bringing machine learning into their workplace, what are 1-2 things they need to consider?

Machine learning is not one single algorithm or even one particular class of algorithms. Instead, there are many kinds of ML techniques, including:

  • Unsupervised learning to uncover patterns
  • Supervised approaches to predict outcomes for new data points
  • Other variants like reinforcement learning

Ensuring success with these technologies starts by building the right business process around their use. Specifically, before bringing in any ML solution, you need to clearly define the business problem you are trying to solve and the metrics for measuring success.

For many workers and managers, the terms ‘artificial intelligence’ and ‘machine learning’ can evoke mixed feelings. While there is certainly promise that routine tasks could be automated by AI and ML, there can also be confusion about what a new system using these technologies will actually do.

![5ae06c01c72f4.png](serve/attachment&path=5ae06c01c72f4.png) I observed that how robots are becoming a greater part of our workplace. And that’s not a bad thing, but it does mean that we need to get more comfortable with today’s technology concepts. That’s why I’m very excited to share today’s interview with you. Artificial intelligence refers to a collection of subfields thatsolve complex problems associated with human intelligence and/or interacting with the world. These subfields include computer vision, natural language processing, robotics, and machine learning. Machine learning generally covers methods that build models of complex data. There are many types of ML approaches, and it is important to choose the right type of solution for the specific business problem at hand. For instance, in the workforce management space, Kronos uses unsupervised ML algorithms that uncover patterns in data to find potential compliance violations. But in the Workforce Dimensions product, Kronos uses a different type of ML, supervised ML regression, for business volume forecasting. Each of these types of ML has its own idiosyncrasies, but the one thing they all have in common is that they are driven by the data they are given. In the same way a new recruit might need time on the job to learn all the idiosyncrasies of your office, ML algorithms tend to work better as they receive more data. **Why should human resources professionals pay attention to machine learning?** HR professionals have seen the amount of data they oversee proliferate. Data elements around skills, staffing, performance, payroll and many other areas have grown both in complexity and size. With all of this data it is getting harder and harder to simply view your data in a spreadsheet and quickly find the problems, let alone the solutions. Machine learning holds the promise to unlock the hidden value in all this data that can no longer simply be perused by hand. For instance, machine learning can uncover patterns, such as those that might indicate compliance risk, and surface outliers that otherwise might have flown under the radar. Similarly, the patterns detected by machine learning can uncover the root causes of key outcomes like turnover or daily business volume and even predict how these key performance indicators (KPIs) will trend in the future. All of these are use cases that HR professional have confronted for decades, but the growth of Big Data means machine learning tools are now capable and even needed to confront these familiar problems. **For organizations that are thinking about bringing machine learning into their workplace, what are 1-2 things they need to consider? ** Machine learning is not one single algorithm or even one particular class of algorithms. Instead, there are many kinds of ML techniques, including: - Unsupervised learning to uncover patterns - Supervised approaches to predict outcomes for new data points - Other variants like reinforcement learning Ensuring success with these technologies starts by building the right business process around their use. Specifically, before bringing in any ML solution, you need to clearly define the business problem you are trying to solve and the metrics for measuring success. For many workers and managers, the terms ‘artificial intelligence’ and ‘machine learning’ can evoke mixed feelings. While there is certainly promise that routine tasks could be automated by AI and ML, there can also be confusion about what a new system using these technologies will actually do.
edited Apr 25 at 5:28 pm
 
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