Humanizing AI and ML

Beth Adams
October 10, 2019

Did you ever wonder if we’re moving toward a future where we outsource the human aspect of data science to computers? Could computers scale the use of AI and ML to make predictions and validate outcomes across the entire enterprise, eventually taking over the entire decision-making process? 

Seeing that there’s a huge implication for the workforce, it’s important not to let myths and rumors be our guide.

In our new whitepaper, “Delivering Artificial Intelligence at Scale”, we explore some common themes and try to de-mystify this technology as it pertains to equipment and personnel. As a starting point, let’s define ML as the science of computers learning from what they’ve experienced, and producing predictive analytics from that learning. 

First, let’s examine the type of data that we can expect to get from a ML experience.  When an AI-based process categorizes or labels data, it’s using assumptions built into the model, and the results may not be what you’ve intended. To mitigate that problem, we insert “supervised learning” that provides some direction to that process. Supervised learning by AI processes aren’t quite the same as typical human workplace supervision, but there are human inputs that are required.

For example, humans need to provide information to the AI process in order to have it know exactly what they’re looking for and what outcome they’re trying to achieve. While “smart,” computers only identify abnormalities. They can’t understand and cope with abnormalities in the same way as humans.

Even when AI-based processes can more efficiently recognize problems, you will still need experts to guide and confirm their findings. 

This is good news for the workforce! 

  • Computer scientists can still calculate probabilities and make predictions
  • Data scientists can provide more management to the analytics
  • Technical leaders can now work out in the open and their contribution is recognized on a greater level
  • Computers, along with human intervention, can leverage AI and ML for smarter outcomes.

There are plenty of jobs out there that require interaction with computers, in combination with AI and ML. So we’re not ready to totally outsource artificially intelligent models to machines, not yet. 

And remember, a good business analytics system can help along the way to disseminate information that is helpful to everyone in your organization. For more information on AI and ML, and how we can help every type of user with every type of use case, check out our new whitepaper.