Article Title: How Alexa Learns

 

Article Summary

In this article, the author explains how Alexa learns. A closed loop of consumer usage & engagement-more data-better algorithmic performance is used to train machine learning systems preferably in 3 different ways – Supervised learning, Semi-supervised learning, Unsupervised learning. In recent AI research, supervised learning had predominated. But today, commercial AI systems generate far more customer interactions than it is possible to label by hand. The only way to continue the torrid rate of improvement that commercial AI has delivered so far is to reorient ourselves toward semi-supervised, weakly supervised and unsupervised learning.  Also, Companies that depend on machine learning for real-time data classification have an additional semi-supervised–training option and Amazon researchers are using this approach across a range of business units. Customers of the Amazon Alexa voice service don’t typically rate Alexa’s responses to individual requests, but their interactions with Alexa do provide useful implicit signals. The author says that the promise of commercial AI is the promise of machine learning at scale. But instead of just throwing more and more data at existing problems, it also means finding more ingenious ways to use that data efficiently, without human involvement.

Article Link: Click here to read the full article

 

Words to learn from this Article:

Implicit: Implied though not directly expressed; inherent in the nature of something

Torrid: Full of difficulty

Reorient: Set or arrange in a new or different determinate position

Smattering: A small number or amount

Elicited: Called forth from a latent or potential state by stimulation

 

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