One of the most interesting demos on Google's I / O keynote this week included a new release of Google's Voice Assistant later this year will appear. A Google employee asked the Google assistant to call up her photos and show them with animals. She tapped one and said, "Send it to Justin." The photo has been moved to the messaging app.
From then on things became more impressive.
"Hello Google, send an e-mail to Jessica," she said. "Hello Jessica, I just came back from Yellowstone and totally fell in love with her." The phone rewrote her words and put "Hi Jessica" on a separate line.
"Take on the Yellowstone adventures," she said. The assistant understood that "Yellowstone Adventure" should be included in the subject line and not in the body of the message.
Then, without explicit instructions, the woman again dictated the bulk of the message. Finally she said "send it" and Google's assistant did it.
Google also endeavors to expand the understanding of the personal references assistant, the company said. If a user says, "Hey Google, what's the weather like in Mama's house?", Google can determine that "Mama's house" is referring to the user's mother's house, looking up her address, and providing a weather forecast for her city.
Google says the next generation assistant is coming to "new pixel phones" ̵
Obviously, there is a big difference between a can demo and a shipping product. We'll have to wait and see if the typical interactions with the new wizard work so well. However, Google seems to be progressing steadily towards the dream of a virtual assistant, who can handle even complex tasks with speech.
Many announcements at I / O looked like this: Not the announcement of important new products. But the use of machine learning techniques to make a series of Google products progressively more complex and helpful. Google also highlighted a number of hidden improvements to its machine learning software that could leverage both Google-built and third-party machine learning techniques.
In particular, Google is making great efforts to relocate machine learning from the cloud to people's mobile devices. This should allow ML applications to be faster, more private and offline.
Google has cited the cost of machine learning.