In this moment, an excerpt from the Generative AI Digital Summit event held on May 25th, Laurence Moroney, Google’s Principal AI Advocate, demonstrated a machine learning (ML) use case for tracking activity types, demonstrating how machines It explains how learning can be judged. The pattern when entering data.
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00:08 — Moloney gives an example of using machine learning (ML) as it relates to activity tracking. Given the nature of this activity type, there is a large amount of data that needs to be tracked. He explains that humans don’t understand what to do with the data collected.
00:28 — But Moloney explains that humans can collect this data, label it, and feed it to computers to determine patterns in the data. In this way, computers may be able to detect patterns that humans would otherwise be unable to detect. When a computer learns patterns in the data it receives, it demonstrates that ML is in action. “You’re looking at the data, looking at the labels of this data, and learning why this data is this label. That’s where the term machine learning comes from.”
01:24 — Engineers write the code for this data themselves, but rather than writing the rules for the code, they write the code for a “neural network” that takes the data and identifies patterns. “It’s a very simple paradigm, but it leads to very complex and cool solutions.”
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