In the past, the company has downplayed the privacy concerns about having cameras and microphones in so many houses across the globe. And it has never really spoken about how it trains the AI to work effectively for its millions of users.
It is worth mentioning that the process is known as data annotation and it has now become the base of machine learning revolution, which is responsible for making AI process languages, recognise images and objects and do so much more. However, AI algorithms only improve when the data that they use is categorized. There are times when Alexa, or other digital assistants, don’t understand the information you are asking for. This can happen because of a number of reasons including the use of a different language or a regional slang. When such cases happen, it is humans who help the AI in understanding what the user needs by listening to the recording and adding the correct label of data. This is called supervised learning. Supervised and semi-supervised learning are two techniques that are used by Apple, Google and Facebook too in similar ways for their own digital assistants. However, we should point out that there have been several instances in the past when recordings have been sent to the wrong people by Alexa. And by wrong people, we basically mean other users. In an article called ‘How Alexa Learns’ that was published in Scientific American earlier this month, Ruhi Sarikaya Alexa’s director of applied science said, “In recent AI research, supervised learning has predominated. But today, commercial AI systems generate far more customer interactions than we could begin 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. Our systems need to learn how to improve themselves.”
