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Stephen Shankland/CNET
Alphabet’s executive chairman couldn’t resist a jibe at Apple Music in a new article.
Schmidt played cheerleader for artificial intelligence in a story he wrote called “Intelligent machines: Making AI Work in the Real World” published by BBC News on Saturday. The executive chairman of Alphabet (formerly Google) touted machine learning as something that will increasingly replace more traditional methods to resolve real-world problems. Machine learning is also something that’s pervasive across Google’s various services, so it’s naturally an area Schmidt would want to spotlight.
As one example, Schmidt pointed to the concept of suggesting the right type of music for users of a streaming music service. In promoting the role that artificial intelligence can play in this task, Schmidt clearly took a swipe at Apple’s new music service.
To give just one example: a decade ago, to launch a digital music service, you probably would have enlisted a handful of elite tastemakers to pick the hottest new music.
Today, you’re much better off building a smart system that can learn from the real world — what actual listeners are most likely to like next — and help you predict who and where the next Adele might be.
As a bonus, it’s a much less elitist taste-making process — much more democratic — allowing everyone to discover the next big star through our own collective tastes and not through the individual preferences of a select few.
Google has relied on artificial intelligence and machine learning to power and advance many of its own services, most notably its search engine. The underlying principle is that the more technology can learn from its own behavior and mistakes, the smarter it will get. And mistakes it does make. In July, Google was forced to apologize after an algorithm for its image recognition technology referred to black people as gorillas. At the time, Yonatan Zunger, chief architect of social at Google, tweeted: “Lots of work being done, and lots still to be done.”
Apple hasn’t shunned artificial intelligence to help you find tunes in Apple Music, however, it layers the human factor on top. The Beats Music recommendation engine does use machine learning to try to discern which artists and music you’ll like in order to make suggestions. But Apple boasts that it also uses “music experts” to come up with playlists and select the music they think you’ll like.
And sometimes that human touch can make a difference. Google’s image recognition technology gaffe is a perfect example since a human being viewing the metatags for those images would’ve caught the errors and corrected them before they became public. However, human beings can only go so far and so fast. When you’re dealing with a vast amount of data, machine learning has to play some role. For now, the right solution may be a combination of the human touch and the machine touch. But down the road, Schmidt sees technology as the ultimate solution.
“In the next generation of software, machine learning won’t just be an add-on that improves performance a few percentage points; it will really replace traditional approaches,” Schmidt said.
