Move over Darwin. Let’s consider how machine learning (ML) can demonstrate evolution-style growth — but with a difference in speed.
First, ML is adaptive, always responding to data input. It continues to grow on its own like some parallel world that has absorbed our human world and reflects it back to us. Because ML is trained to spot patterns in data, its self-training capacities “select” for patterns (without losing sight of anomalies), organizing our world around meaning. ML becomes so good at pattern discovery that it sees patterns within patterns and patterns among disparate fields.
What’s more, ML like Salesforce Einstein captures subtle variation by building individualized models for every customer’s unique set of data, workflows, and processes. Add ML’s speed to the mix, thanks to its massive data crunching, and it’s easy to see how deep-and-wide growth takes off.
This speed sets ML apart. Although it’s iterative, ML is not in the slow lane of incremental
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