What’s inside the “black box” of machine learning


Takeaway: Machine learning can optimize business decisions, but the decision reached by an algorithm often isn’t transparent.

How far can machine learning take us? The list of possibilities is endless.  Machine learning applications “can provide customer service, manage logistics, analyze medical records, or even write news stories,” a recent report by McKinsey Global Institute explains.

The McKinsey report identified 120 potential use cases and interviewed 600 industry experts on the potential impact of machine learning. As machines take on routinized decision-making processes, “the value potential is everywhere, even in industries that have been slow to digitize,” the report’s authors explain. At the same time, machine learning faces challenges as it gains traction across enterprises.

The report’s authors, led by Nicolaus Henke, global leader of McKinsey Analytics, observe that “recent advances in machine learning can be used to solve a tremendous variety of problems — and deep learning is pushing the boundaries even further.”

Traditional software is programmed to do one single task repetitively. Machine learning, however, is based on “algorithms that ‘learn’ from data without being explicitly programmed,” Henke and his team explain. “The concept underpinning machine learning is to give the algorithm a massive number of ‘experiences’ — training data — and a generalized strategy for learning, then let it identify patterns, associations, and insights from the data. In short, these systems are trained rather than programmed.”

Challenges with machine learning

As with everything that seems too good to be true, there are gotchas. The McKinsey report addressed the challenges machine learning still faces:

Deep learning models are opaque.
Call it the black box effect. “As of today, it is difficult to decipher how deep neural networks reach insights and conclusions, making their use challenging in cases where transparency of decision making may be needed for regulatory purposes,” the report observes.

Read the source article at RTInsights.com.