One of the more exciting and promising technologies in the tech world is voice recognition. Early in the last century, computers talking to people was pure science fiction. However, in 1968, with the release of 2001: A Space Odyssey voice recognition and AI were popularized when the movie’s computer, named Hal, spoke to the movie’s protagonist Dave as if it was a real person.
Of course, in 1968, voice recognition at this level was still considered science fiction.
A mere 24 years later, in 1992, Apple brought computer voice recognition into the real world when then Apple CEO, John Sculley, with Apple engineer Kai-Fu Lee at his side, demonstrated Project Casper. This was the first commercial use of voice recognition on the Mac, demonstrated on ABC’s Good Morning America.
In late 1991 I was asked to go to Apple to meet with Kai-Fu Lee and was shown his groundbreaking work in voice recognition. I remember this meeting vividly as Kai-Fu showed me, Casper, answering questions he asked of it on the Mac, and the Mac gave him its answer instantly. Up to that moment, I was like most people thinking voice recognition was more science fiction than science fact.
Apple and Mr. Lee’s Casper Project got a lot of attention and proved that voice recognition was possible. However, the Mac and any other PC then needed to be more powerful to make voice recognition work accurately, and the AI software behind Casper required it to be more advanced at best.
Fast forward to today, and voice recognition in Apple’s Siri, Google’s Voice Assistant, and Amazon’s Alexa has brought voice recognition powered by AI and machine learning into the mainstream. It is no longer science fiction and has been helpful as a new computer interface that controls lights and thermostats, delivers search, and a plethora of actions by just speaking to our computers, smartphones, home speakers and devices.
Although AI-based voice as a computer interface plays a central role in many applications today, there are other significant reasons companies like Google, Apple, Amazon, and others continue to invest in voice recognition.
While we as consumers understand its face value as a UI, the companies’ real thrust behind these investments is vital to their businesses and future.
In a piece I did on Apple’s Siri for Time Magazine in 2016, when Siri was given more features, I stated the following-
“When Apple introduced its version of Siri, I wrote that it would become the underlying data gathering engine for Apple and could even become a threat to Google’s dominance in search. To some degree, that has been true. As people have used Siri, it has gathered all types of data points. It started building its own knowledge database that was tied to an already impressive database Apple had.”
The public face of voice recognition is clear, but the underlying reason is that it provides the companies behind them with a vast amount of data. This data is used by their AI and machine learning technology to build enormous databases about public interests, needs, focus, politics, or anything that these voice engines can cull from voice requests and questions.
In the Time Magazine article, I said that Siri would become a powerful search engine in its own right, and it has. Apple, like Amazon, Google, and many others, leverages voice requests to help them build richer databases for many aspects of their businesses.
For one, it helps them garner detailed information on their customers’ interests and desires and then uses that to provide targeted information, services, and ads. It also is used to help train the AI and machine learning engines to make them more accurate.
Recently, Amazon announced layoffs in their Alexa business, but this was only related to the devices division. Indeed, like others in this field, Amazon is actually pouring more money into its voice recognition technology as it has become one of the central pillars of its business models.
Voice recognition as a user interface and data collection vehicle is no longer science fiction. It has now become a significant way for companies to learn about their customers so that it can be more helpful in meeting their customers’ needs. And the more data that is collected via voice, processed in AI, and through machine learning will make them even more accurate and valuable in the future.