Amazon, like many other tech companies investing heavily in artificial intelligence, has always been forthright about its Alexa assistant being a work in progress. “The more data we use to train these systems, the better Alexa works, and training Alexa with voice recordings from a diverse range of customers helps ensure Alexa works well for everyone,” reads the company’s Alexa FAQ.
What the company doesn’t tell you explicitly, as highlighted by an in-depth investigation from Bloomberg published this evening, is that one of the only, and often the best, ways Alexa improves over time is by having human beings listen to recordings of your voice requests. Of course, this is all buried in product and service terms few consumers will ever read, and Amazon has often downplayed the privacy implications of having cameras and microphones in millions of homes around the globe. But concerns about how AI is trained as it becomes an ever more pervasive force in our daily lives will only continue to raise alarms, especially as most of how this technology works remains beyond closed doors and improves using methods Amazon is loathe to ever disclose.
In this case, the process is known as data annotation, and it’s quietly become a bedrock of the machine learning revolution that’s churned out advances in natural language processing, machine translation, and image and object recognition. The thought is, AI algorithms only improve over time if the data they have access to can be easily parsed and categorized — they can’t necessarily train themselves to do that. Perhaps Alexa heard you incorrectly, or the system thinks you’re asking not about the British city of Brighton, but instead the suburb in Western New York. When dealing in different languages, there are countless more nuances, like regional slang and dialects, that may not have been accounted for during the development process for the Alexa support for that language.
In many cases, human beings make those calls, …
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