February 6, 2020

Artificial intelligence is good at predicting outcomes, but how do we go one step further? Here, Ericsson discusses how AI can use causal inference and machine learning to measure the effects of multiple variables – and why it’s important for technological progression.

In a major operator’s network control center complaints are flooding in. The network is down across a large US city; calls are getting dropped and critical infrastructure is slow to respond. Pulling up the system’s event history, the manager sees that new 5G towers were installed in the affected area today.

Did installing those towers cause the outage, or was it merely a coincidence? In circumstances such as these, being able to answer this question accurately is crucial for Ericsson.

Most machine learning-based data science focuses on predicting outcomes, not understanding causality. However, some of the biggest names in the field agree it’s important to start incorporating causality into our AI and machine learning systems.

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